FUTUROLOGY CHRONICLE No 24 - A.I 2.0 - REALITY CHECK -

AI WON’T REPLACE YOU A PERSON USING AI WILL

Angel Berniz- Game changers newsletter-Madrid-2021

ANY SUFFICIENTY ADVANCED TECHNOLOGY IS INDISTINGUISHABLE FROM MAGIC

Arthur c ClarkE- Profiles of the Future- 1962

MY WORST FEAR IS WE CAUSE SIGNIFICANT HARM TO THE WORLD. IF THIS TECHNOLOGY GOES WRONG, IT CAN GO QUITE WRONG

Sam Altman, CEO of OpenAI Washington DC Congress

AI hearing - 18th May 2023

Front cover - Photo design- credit

Louise Moon -2023 INSTAGRAM @LOUISEMMOONINC

PREAMBLE

Artificialintelligence(AI)hasalreadyimpactedmanyaspectsofdailylife andisbecomingmoreandmoreinterwovenintooursociety.

Toprovidemyreaderswiththemostcomprehensiveeditionpossible,I spentfivemonthsstudyingallavailableinformationandresearch.

Iaimtopresentacomprehensive360degreesoverviewofwhatis occurringandwhatcouldtakeplaceinthenearfuture.

Shouldweautomateawayeveryjob,eventheonesthatarefulfilling?

ShouldwecreateamorepowerfulAIthantoday.

ThealreadycalledAGI(ArtificialGeneralIntelligence)thatcouldoneday outnumber,outsmart,andevenreplacehumans?

Mygoalwastoallaytheseconcerns,comprehendworries,and demonstratethetruevalueofAI.

Ibelieveyouwillliketherealitycheckwithscientificflair,mixedwith mycustomaryentertainingtwist.

Wishingyouagood “Summer”reading!

-Next page: Design author- Ricardo Rey- for the April 20th front cover of The Economist-

PART 1 – THE CONTEXT

PART 2- THE INFORMATION SCENE

PART 3- THE SCIENCE SPECTRUM

PART 4- THE FUTURE OF THE FUTURE

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

PART 1—THE CONTEXT

WHO INVENTED THE ALGORITHM?

The creation of what is widely regarded as the first algorithm is attributed to Augusta Ada Byron, countess of Lovelace (1815–1852).

In a translation of an article about Charles Babbage's Analytical Engine (photo) that Ada Lovelace had written, her first algorithm was first made public in 1843.

The algorithm includes a thorough explanation of each step the Analytical Engine would take to carry out the procedure, which was created to generate Bernoulli numbers. Many people regard this effort to be the earliest instance of computer programming, and it showed how machines could be used to carry out difficult mathematical operations.

It's important to remember that the technology at the time was very different from what we have today, and Lovelace's work on the Analytical Engine was primarily theoretical in nature.

She also contributed to the development of modern computer science with her thoughts and insights about computing and the capacity of computers to carry out difficult jobs. She can be considered the very first ‘programmer’ in history.

Some 93 years later, Alan Turing marks the next major development in computer science. The idea of a universal machine, which could be programmed to perform any computation, was developed by Alan Turing (1912–1954). Fundamentally, the invention of contemporary computers was based on his personal concept.

He proposed the concept of the universal machine and laid the theoretical bedrock for computing in a paper titled "On Computable Numbers, with an Application to the “’Entscheidungsproblem’", which was published in 1936. This paper is largely recognized as the most significant contributions to the subject of computer science, and it contributed to the recognition of Turing as a very important figure in the history of the computer.

https://en.wikipedia.org/wiki/Alan_Turing

Book extract sold at auction on 21st July 2018 for $125.009 by Moore Allen Auction house

Whothencreatedtheveryfirstalgorithm?

The conclusion is: Lovelace… but only in theory.

It's important to note that Lovelace's work concentrated mostly on the creation of specialized algorithms for the Analytical Engine, making her the pioneer of algorithms. With the creation of the first algorithm, Lovelace made a significant contribution to the subject of computing. However, Turing's work laid the groundwork for modern computing as we know it today.

Lovelace'salgorithmicbrightmindandTuring'stheoreticalunderpinnings,when combined,formedtogetherthefoundationforourcontemporarycomputersand digitalworld.

He was also a keen runner and won the British Marathon national record in 1947 in 2h 46 minutes and 3 seconds

AI PIONEERS TIMELINE

The phrase ‘Artificial Intelligence’ was first coined by John McCarthy in 1955.

While working as a research scientist at Dartmouth College, McCarthy was composing a proposal for a workshop on "the study of automata," which he believed would bring researchers from many disciplines together to work on the challenge of building intelligent machines. He tried to find something more striking than “Automata” and nailed down the intriguing and catchy term of AI.

In 1956, he was one of the main organizers of “The Dartmouth Summer Research Project on Artificial Intelligence,” a six-week program gathering a group of scholars from diverse fields who were interested in building robots that could accomplish activities that would ordinarily need human intellect.

Besides McCarthy, the contributors were Marvin Minsky, Nathaniel Rochester, Claude Shannon,. Oliver Selfridge,Ray Solomonoff and Trenchard More.

The major objective of the workshop was to create a "thinking machine," computer software that could simulate human cognitive processes and find solutions to be issued logically. Natural language processing (NLP), problem-solving, pattern recognition, and learning were some of the subjects covered throughout the workshop by the attendees.

The 1956 Dartmouth Conference established AI as a separate field of study and is therefore regarded as a pivotal moment in the history of the discipline.

The first AI research labs and the first AI programs were developed as a result of the conference. The foundation was created for many of the advancements that would come in the following decades.

The next pioneers were Tom Mitchell, who developed the first machine learning (ML) algorithm, and Arthur Samuel, who in 1959 created the first self-learning program, dubbed the Samuel Checkers-playing Program (photo).

By assessing its own gameplay and changing its strategy as necessary, the program employed ML techniques to enhance its performance over time. Samuel's work paved the way for the creation of algorithms, decision trees and neural networks, which have since grown to be crucial resources in the field of AI.This was followed by the creation of the first neural network in the 1980s.

The backpropagation technique, created in the early 1980s by David Rumelhart enabled neural networks to be taught to spot patterns in data. This innovation made it possible to train multi-layer deep neural networks, which are currently widely utilized in functions like speech and picture recognition.

Still In the 1980s, Geoffrey Hinton is recognized as one of the forerunners of deep learning (DL). The very first neural network—called a “Boltzmann” machine—which could recognize and understand complicated correlations in data, was created in the 1980s by Hinton and his colleagues.

By creating a method known as deep belief networks, which are deep neural networks that can learn to represent complex data distributions, Hinton made important strides in DL in the following years.

Research into DL by Hinton has significantly influenced the field of AI, and his work has aided in the creation of systems for speech and picture recognition, NLP, and autonomous cars.

The development of the next discipline of reinforcement learning (RL) has been significantly aided by a number of pioneers over the years. Richard Sutton, who is recognized as one of the pioneers of the subject, is among the most significant individuals in the history of RL. He created the ‘temporal-difference learning’ framework for RL in the 1980s, which allowed agents to learn from experience more effectively than earlier techniques. Sutton is also credited for co-authoring the renowned book Reinforcement Learning: An Introduction, which has gained ubiquity as the main source of information in the area.

Richard Bellman, who is credited with first inventing the term ‘dynamic programming’ and set the framework for RL, is another well-known pioneer in the subject. Andrew Ng, who co-founded Google Brain and later launched the AI education company, Deeplearning.ai, is another.

All this seems a smooth ride but historically AI research has swung wildly in and out of favor. In the 1970s and the late 1980s to early 1990s, respectively, there were two AI winters hype cycles of inflated expectations that caused AI research to fail to live up to the over-anticipated reality.

Bell Labs was known as the epicenter of innovation for many years, and several of its scientists, including Yann Le Cun, Yoshua Bengio, and Geoffrey Hinton, were honored with Nobel Prizes and Turing Awards on AI related matters.

But the lab's resources were reduced as management began pushing for more immediate returns based on incremental technological changes, and it ultimately failed spectacularly to capitalize on the internet revolution of the early 2000s.

The downturns occurred when the hottest AI techniques of the time failed to demonstrate advancement and were unstable and challenging to operate.

Government organizations in the US and the UK that had supported AI research quickly concluded that this strategy was fruitless and stopped funding not least due to the drop off in defense-related spending and research after the end of the Cold War and the increasing demand for ‘immediate’ and ‘commercially-focused’ results.

However, the ‘main character’ moment in AI research is just currently taking place – more precisely, since very recently in the fall of 2022.

AI research is now finally fascinating, and we are witnessing weekly spectacular rollouts of systems that are pushing back the frontiers of what AI can do.

But it was not always ‘fashionable.’ In the 1990s, the reputation of the field was regarded as a strange, fringe activity by the larger computer industry, much how homeopathy is regarded by conventional medicine!

Neural networks, which made a significant advancement in the 1980s and mimic the patterns of the human brain, were the driving force behind the current surge in AI research.

Since computers at the time could not run the software properly, technology hit a brick wall. This is the opposite of today where we have a lot of data available and incredibly powerful computers.

The AI moment is now! Google has declared a "code red" threat status for its key search product in light of the popularity of Open Ai’s Chat GPT; it is attempting to aggressively redesign its search engine with its own AI R&D.

The AI industry has a once-in-a-generation chance to experiment with the potential of new technology at this time. Despite the negativity, anxieties and fear, it's a fascinating moment to witness.

This is even more so with the coming out of large language models (LLMs), the talk of the town since November 2022. LLMs have been developed as a result of the contributions of numerous researchers but three stand out: Tomas Mikolow (Czech), Joshua Bengio (Canada), and Ilya Sutskever (Canada).

In the AI community today, it is Ilya whose brilliant mind is seen as being slightly ahead of the others.

Lately, his name has been in the press as a result of his work at Open Ai, while Sutskever, as one of Open Ai's co-founders, was instrumental in the creation of the company's most illustrious accomplishments, including the GPT-2 and GPT-3 language models.

Sutskever has made important contributions to the field of DL outside of his work at Open Ai, including the creation of the Sequence to Sequence (Seq2Seq) model for machine translation, which has grown to be a popular method for translating text between different languages.

He has also contributed to significant AI research in other areas like generative modeling and RL.

Wehavevisitedallthesignificantpioneers,butthousandsoflesser-known researchersbravethetwo “Winters”ofresearchandmadesignificantcontributions tothenextandrecentrapiddevelopmentofAI.

Anewgenerationofcreativemindsandexcitingtechnologicalandscientific advancementswillundoubtedlyresultfromthenewAItoolsthatarecurrentlyopen toallacademicsatlowornocost .

AIisnotanymore,ascientistfantasyland. Withtheenergyofthenewgenerationshiningbrains

Weshallbereadyforaradiantfuture

Ihearyoucoughingandgrumbling!

Waittoreadmore, Youarejustatthestart

Beforemakingyourownconclusion

ASKING AI TO EXPLAIN HIMSELF

This chapter will offer you a brief overview of AI and how it functions, whether you are a total beginner or just seeking to brush up on the fundamentals.

What exactly is AI?

AI describes computer programs that are capable of learning, solving problems, and making decisions tasks that would typically need human intellect of any required level. The two basic classifications of AI can be considered with broad brushes as follows. Strong AI is capable of performing any intellectual work that a human is capable of, while narrow AI is created to execute a specific task like in robotics or such as playing chess

How does AI function?

Algorithms, which are collections of guidelines that direct computers to what to do, are used by AI systems. These algorithms are made to learn from data and form hypotheses or judgments based on it.

Now after this extremely basic introduction let’s move to the next step. Being a solid partisan of creativity, as you know it’s now three years since publishing my first Futurology Chronicle – where I am writing all of my text, A to Z.

As the opportunity presented itself this time, I asked Generative AI, to work at my place, and express itself on this vast domain. Accordingly, I used with Chat GPT 3.5 (No 4 is still in final testing phase) to present in intelligible terms the AI basics for beginners.

You will read below my prompt or question and the ultra-fast immediate replies. I have underlined certain important wording and added some minor comments in hard brackets, but the replies are untouched and totally ‘in texto’. **BEGINS**

Q:Algorithmaretrainedunderthreemodels.Canyoudescribethemin300words? [Foursecondslater….]

A: Certainly! Here is a concise explanation of the differences between machine learning, deep learning, and reinforcement learning:

Machine learning is a branch of artificial intelligence that involves training algorithms to recognize patterns in data. These algorithms can be used to make predictions, recognize images or speech, or perform other tasks that would be difficult or impossible for humans to do manually. Machine learning algorithms learn from data without being explicitly programmed, and can improve their performance over time as they are exposed to more data.

Deep learning is a type of machine learning that involves training artificial neural networks with multiple layers to recognize patterns in data. These networks are modeled after the structure of the human brain, and can learn to recognize

complex patterns in data such as images, speech, and text. Deep learning has been used to achieve breakthroughs in areas such as computer vision, NLP, and speech recognition.

Reinforcement learning is another type of machine learning that involves training agents to make decisions based on the rewards or punishments they receive from their environment. This type of learning is often used in robotics, gaming, and other applications where an agent needs to learn how to navigate a complex environment and make decisions in real-time. Reinforcement learning algorithms learn by trial and error, and can improve their performance over time as they receive feedback from their environment.

In summary, machine learning is a general term for algorithms that learn from data, while deep learning is a specific type of machine learning that uses neural networks with multiple layers to recognize complex patterns. Reinforcement learning is a type of machine learning that involves training agents to make decisions based on feedback from their environment.

Q:WhenAlgorithmarenotusingneuralnetworkswhatelsedotheyuse?? [Fasterandfasterreply…1.5seconds.GPTrecognizedthatweareonthesame patternofrelatedquestions.]

A: In addition to neural networks, various methods can be used by machine learning algorithms to learn from data. Non-neural network machine learning methods come in a variety of forms, such as:

Decision Trees: A decision tree is a flowchart-like structure where each internal node represents a “test” on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label. Decision trees are often used for classification tasks, such as predicting whether a customer will churn or not.

Support Vector Machines: A support vector machine (SVM) is a type of algorithm that tries to find a hyperplane that separates different classes of data. SVMs can be used for both classification and regression tasks.

K-Nearest Neighbors: The k-nearest neighbors (KNN) algorithm is a type of instance-based learning where the algorithm finds the k data points that are closest to a new data point, and predicts the class label of the new data point based on the labels of the k-nearest neighbors.

Random Forest: A random forest is an ensemble learning algorithm that constructs a multitude of decision trees and combines their predictions to improve the accuracy and reduce overfitting.

Naive Bayes: A naive Bayes algorithm is a probabilistic classifier that uses Bayes' theorem to predict the probability of each class label given a set of features. Naive Bayes assumes that each feature is independent of the others, which is often not the case in real-world applications.

These are just a few examples of machine learning algorithms that do not use neural networks. Each algorithm has its strengths and weaknesses and is suited for different types of problems.

Q:Whatisthemostusedneuralnetworksbetweenthesefivetechniques. [2.8secondsonmymobiletimer.Slowingdown… sortof… duetotheintricaciesof thequestionwiththeprecedentone.]

A: Within the category of deep neural networks, some of the most commonly used architectures include:

Convolutional Neural Networks (CNNs): CNNs are often used for image and video recognition tasks. They apply a set of learnable filters to the input image to extract relevant features and classify the image.

Recurrent Neural Networks (RNNs): RNNs are used for sequential data processing tasks, such as speech recognition and natural language processing. They have a "memory" component that allows them to keep track of previous inputs and use this information to predict the next output.

Long Short-Term Memory Networks (LSTMs): LSTMs are a type of RNN that are designed to avoid the "vanishing gradient" problem, which can occur when training traditional RNNs on long sequences of data.

Generative Adversarial Networks (GANs): GANs are used for generative modeling tasks, such as generating realistic images or videos. They consist of two networks, a generator network that produces the output and a discriminator network that tries to distinguish the generated output from real data.

Transformer Networks: Transformer networks are a type of neural network architecture that have been used to achieve state-of-the-art performance on NLP tasks, such as language translation and question answering.

While deep neural networks are currently one of the most popular techniques in machine learning, the choice of algorithm ultimately depends on the specific problem being addressed and the characteristics of the data.

Q:IamreadingalotaboutANNpotential.Pleasedescribeinsimpleterms?

[Twosecondslater…]

A: ANN stands for Artificial Neural Network, which is a type of machine learning algorithm inspired by the structure and function of the human brain.

ANNs consist of multiple layers of interconnected nodes, called "neurons," that can learn to recognize patterns in data through a process called training.

In an ANN, each neuron receives input from other neurons and processes that input using a set of learnable parameters, known as weights.

The output of the neuron is then passed to other neurons in the network, and the process is repeated until the final output is produced.

ANNs are used for a variety of tasks, including image and speech recognition, NLP, and control systems.

They can learn from large amounts of data without being explicitly programmed, and can be used to make predictions, classify data, or perform other tasks. There are different types of ANNs:

Feedforward neural networks are the simplest type of ANN, with inputs that are processed through one or more hidden layers before producing an output.

Recurrent neural networks are designed to process sequential data, such as speech or time-series data.

CNNs are designed to process spatially structured data, such as images or videos. Overall, ANNs are a powerful tool in machine learning, with applications in a wide range of fields including computer vision, NLP, and robotics.

Q:Allisratherclear,butallthesearestandardAItechnologyoftheday.Whatare themodelsoftomorrow?

[Ifeltsomehesitationhere,andittookfivesecondsthistime…foraprudentreply!]

A: As an AI language model, I cannot predict the future or anticipate new technologies that may emerge.

However, I can provide some insights into current research and developments in machine learning that may point towards future directions.

One area of research that is receiving increasing attention is called ”meta-learning," or learning to learn. Meta-learning involves training models that can learn how to learn from a small amount of data, which could lead to more efficient and adaptive learning models in the future.

Another area of interest is "Explainable AI," which involves developing machine learning algorithms that can provide explanations for their decisions or recommendations. This could help to increase the trust and transparency of AI systems and could be especially important in applications such as healthcare or finance.

In addition, there is growing interest in "unsupervised learning," which involves training models without the use of labeled data. This could be particularly useful in situations where labeled data is scarce or expensive to obtain.

Finally, "reinforcement learning" is an area of machine learning that is still evolving and could see new developments in the future.

Reinforcement learning involves training agents to make decisions based on the rewards or punishments they receive from their environment and has been used in applications such as robotics and gaming.

These are just a few examples of areas of research that could lead to new learning models in the future. As technology continues to evolve, we can expect to see new developments in machine learning that will shape the future of AI. **ENDS**

So,inconclusion

BeingabletowatchtheAI'sspeedandhesitancyinresponsetothequeriesin secondsgivesmeasenseofintellectualsatisfaction.

EventhoughI'vebeenfamiliarwithalltheAIjargonanditsvariationsforawhile— Chronicleno3ofMarch2021wasanintroductiontotheAIsector–itwasobviously efficientforAItorespondaboutitsownfield.

IthinkeasilythatIwillneverhavethespeedorabilitytoexpressalltheseconcepts insimpleterms,farfromit.,

1527wordsofthiscaliberwouldrequiremetowriteit,revise,andcleanitupover twohoursasopposedto15.3secondsoverallforChatGPT.

GenerativeAIisourgo-toassistantforeveryresearchproject,workingfastto increaseouroutputacrossallindustries.

Weareallbetteroffquicklybecomingaccustomedtothisnew,soon-to-beessential working ‘assistant’thanthecryingwolfinthedarkforitsbiasesandotherirrational anxieties.

THE LAMP GENIE: TO RUB OR NOT?

Science fiction authors, ethicists, and scientists have long been fascinated about AI that develops abilities against the will of its programmers.

An arresting illustration from mid-April (on 16th): the Google data scientist team found that despite not having been educated to know Bengali, its system was able to teach itself the language. They found that it can now translate all of Bengali with only a small amount of prompting in that language.

The team responded with excitement, enthusiasm, and anxiety. How can we now control an AI that learns on its own, with very few components to begin with, that goes against the million pieces of information in DL we believed it needed to be properly trained?

When a journalist (CBS) pressed Sundar Pichai, the CEO, for an explanation, he responded that some aspects of how AI systems both learn and act continue to astound both us and our top experts. Humm really!

He continues saying that internally when we do not fully comprehend and are unable to determine why AI said something in a certain way, we refer to this internally as the "black box mystery."

He makes then the easy official claim that additional research is necessary before we can fully understand how it operates and came up with a non-acceptable off-ramp dumb citation:

A low level of diplomatic sheer escapism for someone in this position!

The problem is now evident to everyone that the Big Tech Big Boys have been caught off guard by their own fast, billion-dollar AI R&D success.

In a well-known open letter signed by big wigs like Musk, Thiel, and thousands of other tech titans, data science professors and luminaries, they demanded a "pause" in the creation of more potent models.

A pause. It is an utterly cynical easy term to describe a budget halt in the race to create the next generation.

The letter to read is here: Pause Giant AI Experiments: An Open Letter - Future of Life Institute

Open Ai CEO, Sam Altman, received criticism for declining to sign the letter with his colleagues (including his former associate Elon Musk) but then went on his own, on 18th April, that he would not advance further than GPT-5. Exactly following his colleagues “pause” signal! If Sam is also very concerned, we should all be alongside him.

So, now they all have their feet on the brake – but for how long until corporate competition comes back? The letter asks for a six-months pause. Frankly, do you see all these data scientists’ top brains going for a long holiday in Cancun?

It is pure fantasy, but on the other hand that letter did not miss the point that they have been moving too fast without any strings attached, and that it is time to calm down and go back to the AI ethics founding term, called the “Asilomar Accord.”

Asilomar is a small conference town near Monterey, California, which had its illustrious debut with a group of scientists who met in February 1975 to talk about the advantages and disadvantages of genetic engineering. There, they signed an international agreement on ethics.

On the same ethics philosophy debate, top AI researchers (800+) convened in 2017 at the same place on the potential drawbacks and advantages of AI technology. Theycan’ thide.TheywereallpresentonAugust11th,2017,

The Asilomar AI principles (5720 signatories) set forward several safety precautions and moral principles for the creation and application of AI systems in the same pattern as the Asilomar Accords for genetic engineering. The guidelines take transparency, justice, privacy, and the potential social effects of AI into account.

The official documents with the participants’ list: AI Principles - Future of Life Institute

On purpose, as it was a bit of a long search to find them properly, I asked Chat GPT for assistance in summarizing the Asilomar AI principles in bullet points. It came back in less than three seconds (!) and is below with no alterations:

 Research Goal: The goal of AI research should be to create safe and beneficial AI.

 Safety: AI systems should be designed and operated to avoid unintended negative consequences.

 Transparency: The operation of AI systems should be transparent to allow for scrutiny and accountability.

 Fairness: AI systems should be designed and operated fairly, without bias or discrimination.

 Privacy: People should have the right to privacy regarding the data collected by AI systems.

 Controllability: People should be able to control and override the actions of AI systems that directly affect them.

 Lawfulness: AI systems should be developed and operated in accordance with the rule of law.

 Ethical Considerations: AI systems should take ethical considerations into account, such as avoiding harm, respecting human dignity, and promoting human well-being.

 Societal and Environmental Well-Being: AI systems should be developed and deployed to promote societal and environmental well-being.

Back to ‘human’ writing …

These beautiful minds are returning to the Asilomar Accords that they have neglected in their haste to advance, having moved too fast without a clear understanding of what they were doing especially now that that they are being watched by everyone on Earth who are catching up all the daily AI news on “anxiety” meds (!).

The entire community of data scientists should take the principles they all defined back in 2017 very seriously and make genuine commitments. To align with the values and interests of society, it has to be hoped that the next phase of AI research will be conducted responsibly for all humanity and not only for Big Tech’s classic greed.

Wishfulthinkingorotherwise?

HastheGenielampalreadybeenrubbed,ornotyetcompletely….?

COUNTRIES ON THEIR HIGH REGULATORY HEELS Vs AI

The necessity for regulations regarding the use of artificial intelligence (AI) is growing as this technology does.

The area of law known as AI law is focused on figuring out the subtleties of AI and how it will affect society and the future.

From the ethical ramifications of AI to the economic and legal ramifications of the technology, the legal field of AI law addresses a broad variety of concerns.

In 2022, 37 related AI laws addressing global artificial intelligence were enacted by legislative bodies in 127 nations.

The United States took the top spot on the list, passing nine laws, followed by Spain with five laws and the Philippines with four laws. These laws address education changes to address the difficulties brought on by new technologies, including AI.

Worth noting, the Spanish legislation emphasized nondiscrimination and accountability for AI algorithms.

123 AI-related laws have been enacted across 123 nations since 2016, with a rapid increase starting from 2021.

This looks new for the public but very early in 2018 United Kingdom created a Centre for data ethics and Innovation to provide guidance and advice on the ethical use of date and AI.

In 2019 Canada release a directive on automated decision making that requires federal government departments to provide explanations to individuals when automated decisions are made that affect them.

On the same wavelength Singapore created an AI Model for governance framework to provide guidance on the responsible use of AI in various industries.

EU have the solid base of the general data protection regulation (GDPR ) that regulates the collection , processing and storage of personal data of EU citizens ,but this was clearly not enough and don’t include properly all the new elements coming from AI

A white paper followed by a draft legislation by the EU Commission was published in April 2021. It suggests a legal framework for AI in the EU with provisions for openness, responsibility, and the moral application of AI included in the suggested framework. A European approach to artificial intelligence | Shaping Europe’s digital future (europa.eu)

The AI Act, the mother of all artificial intelligence regulations, is what the European Union believes to be the answer. It is the first law that tries to reduce these effects by regulating the entire industry. It was released on 28 March 2023 and shall be voted in October after the summer recess.

Veryambitiousatscale

The AI Act has a very lofty goal. Additional oversight would be necessary for "high risk" applications of AI that pose the greatest risk to human safety. This could apply to tools used in hiring staff, grading tests, or assisting judges with legal and judicial decisions.

The bill's initial draft also prohibits the use of AI for "unacceptable" purposes, such as assigning people a trustworthiness score.

The bill would also limit the use of facial recognition technology by law enforcement agencies in public areas.

A Classic EU reaction to every supranational difficulties!

There is a vocal group of influential people who demand a complete ban or moratorium on its usage in public by both law enforcement and private corporations, claiming that the technology allows for mass surveillance. This group includes members of the EU Parliament and nations like Germany.

Howwillitimpactpeople?

By making sure that applications are subject to at least some amount of inspection and accountability, it should, in principle, shield humans from the worst effects of AI. According to Brando Benifei, an Italian member of the European Parliament and a significant contributor to the team revising the legislation, people can have faith that they will be shielded from the most dangerous types of AI.

The legislation mandates that people be informed when they come across deepfakes, biometric identification tools, or AI programs that assert to be able to read their emotions. Legislators are also debating whether the law needs to establish a process for those who have been harmed by an AI system to file complaints and seek remedies. Predictive policing technologies are also being pushed for a ban by the European Parliament, one of the EU institutions working on changing the law.

These systems employ AI to evaluate massive data sets in an effort to either forecast a person's prospective criminality or to dispatch police in advance to crime-prone locations. These systems are quite contentious, and their detractors claim that they frequently display racism and lack transparency.

Whatdifficultiesarethegreatest?

Some of the provisions of the bill are currently technically impractical to implement. The bill's initial version stipulates that data sets must be error-free and that people must be able to "fully understand" how AI systems operate.

The size of the data sets required to train AI systems makes it impossible for a person to verify that they are truly error-free without investing countless hours of labor.

Furthermore, modern neural networks are so complex that even their designers are unsure of how they come to their conclusions.

Tech firms are also quite uneasy about needs to grant access to their source code and a lgorithms to external auditors or regulators in order to uphold the law.

"The current drafting is creating a lot of discomfort because people feel that they actually can't comply with the regulations as currently drafted," says Miriam Vogel, president and chief executive officer of Equal AI, a nonprofit organization whose goal is to lessen implicit prejudice in AI systems. She also serves as the chair of the recently established National AI Advisory Committee, which provides the White House with AI policy advice.

There is also a heated debate over whether the AI Act should outright forbid the use of facial recognition. It's controversial because EU nations despise when Brussels tries to tell them how to handle concerns of law enforcement or national security.

Many nations, including France, want to grant exceptions for using facial recognition technology to safeguard national security. In contrast, the newly elected government of Germany, a sizable European nation with a significant say in EU policymaking, has stated that it is in favor of a complete ban on the use of face recognition technology in public spaces.

What types of AI are labeled as "high risk" will be the subject of yet another heated debate. The list of items covered by the AI Act includes everything from lie detectors to welfare payment distribution systems.

There are two competing political camps: one worries that the extensive regulation would stifle innovation, while the other claims that the bill's current form won't adequately safeguard citizens from grave harm.

Willthispreventinnovation?

Silicon Valley lobbyists frequently complain that the regulation will burden AI businesses with additional red tape. Europe is in disagreement.

The EU responds by claiming that the AI Act will only be applicable to the riskiest types of AI applications, which the European Commission, the EU's executive body, predicts will only account for 5 to 15% of all AI applications.

Tech firms "should be reassured that we want to give them a stable, clear, legally sound set of rules so that they can develop most of AI with very limited regulation," says Benifei.

Failure to comply can result in fines of up to €30 million ($31 million) or, in the case of corporations, up to 6% of annual global revenue. And history demonstrates that Europe is not averse to fining tech corporations.

For violating the GDPR, Amazon was fined €746 million ($775 million) in 2021, while Google was fined €4.3 billion ($4.5 billion) for breaking the EU's antitrust laws in 2018.

Whenwillitbecomeactive?

Before a final text is enshrined in law, at least another year will pass, and it will take a few more years before businesses must abide by it. It's possible that working out the specifics of a complete measure with so many difficult issues would take much longer.

The GDPR was negotiated for more than four years, and it took another six years for it to go into effect. Anything is possible in the world of European Union lawmaking.

One of the greatest technical restrictions to date would be achieved if the EU is successful in doing this. Facial recognition is subject to limitations in some US states and cities, such as San Francisco and Virginia, but the EU's ban would cover 27 nations with a combined population of over 447 million people.

If the EU is successful, it might establish a new international standard for AI oversight. The EU's Artificial Intelligence Act, explained | World Economic Forum (weforum.org)

They need to act fast to avoid a wind of excessive fear like the one expressed by one of his members: Italy

The Italian government's, via the Italian data protection authority, block and ban the use of Chat GPT 3 on 31st march 2023 with immediate effect,

The Italian data-protection authority said Open AI had one month to say how it would address the watchdog's concerns, under penalty of a fine of €20 million or up to 4% of annual revenues.

Sudden backtrack on 29 th April the Italian regulator conceded that Open Ai complied -with no specific details- but seems satisfied by the new safety measures!

It did not resolve the anxiety ,but on the opposite create a feeling that GPT is certainly evil somewhere ?Australia, South Korea and Japan are working on with more calm on AI related legislation.

Weanticipatethat2023willbeapivotalyearforstabilizingglobal “angst”overthis newadditiontoourdigitalenvironmentthatisoperatingtoowell,ortoobadly dependingonwhichsideofthefenceyouplaceyourself!

A SECURITY DISASTER IN THE MAKING

Getting instructions from users and then searching the internet for solutions is how LLMs fundamentally function, which implies zillions of hazards. They could be utilized by criminals to assist them in phishing, spamming, and other forms of identity theft thanks to AI. Security and privacy experts warn that a "disaster" is on the horizon.

Here are three ways that language AI models could be abused.

Jailbreaking

Text created by chatbots like Chat GPT, Bard, and Bing using AI language models reads like it was written by a human.

They respond to the user's commands or "prompts" and then create a phrase by predicting, using their training data, the word that will most likely come after each word that has already been spoken.

But the very ability to follow instructions that makes these models so good also leaves them open to abuse. This is possible by using "prompt injections," which are prompts that tell the language model to disregard its earlier instructions and safety barriers.

On websites like Reddit, a small industry of individuals attempting to "jailbreak" Chat GPT has developed over the past year.

People have persuaded the AI model to support prejudice or conspiracies, or to advise users how to commit crimes like shoplifting and making explosives. This can be achieved, for instance, by instructing the chatbot to "role-play" as another AI model that is able to carry out the user's wishes, even if it means defying the original AI model's limitations.

According to Open Ai, it is noting every method used to jailbreak Chat GPT and adding instances of each to the training data for the AI system in the hopes that it would eventually learn to resist such methods.

Scamming

Additionally, Open Ai employs an approach known as adversarial training, in which Chat GPT is tested against assisting in phishing and scams.

The ability to include Chat GPT into products that browse and interact with the internet was announced by Open Ai at the end of March. Through this, it faces a much more significant issue than jailbreaking.

Startups are already making use of this functionality to create virtual assistants that can schedule meetings and make flight reservations in the real world. Making Chat GPT's "eyes and ears" open on the internet leaves the chatbot incredibly open to intrusion.

According to Florian Tramèr, an assistant professor of computer science at ETH Zürich who specializes in computer security, privacy, and ML, "I think this is going to be pretty much a disaster from a security and privacy perspective."

The fact that AI-enhanced virtual assistants extract text and images from websites makes them vulnerable to an indirect prompt injection attack, in which an outsider modifies a website by inserting concealed material that is intended to affect the AI's behavior.

For instance, attackers could lure consumers to websites with these hidden cues using social media or email. After that, the AI system might be tricked into allowing the attacker to try to steal people's credit card information. Someone could also receive an email from a malicious attacker that contained a secret prompt injection. The attacker might be able to trick the recipient's AI virtual assistant into sending the attacker personal information from the victim's emails or even to send emails on the attacker's behalf to people in the victim's contacts list if the recipient used such a device.

According to Princeton University computer science professor Arvind Narayanan, "Basically any text on the web, if it's crafted the right way, can get these bots to misbehave when they encounter that text."

Prompt injection attack

A cyberattack known as a prompt injection attack targets Chat GPT and other NLP systems. In order to trick the system into producing undesirable or destructive responses, these attacks entail inserting malicious code or text prompts into the system's input fields.

It's crucial to comprehend the operation of NLP systems like Chat GPT before understanding how a quick injection attack operates. To find patterns and connections between words, sentences, and concepts, these systems analyze enormous amounts of text data.

Based on this analysis, the system creates replies to user input with a response that is intended to be understandable and contextually appropriate.

An NLP system's input fields can be corrupted by an attacker using a technique known as prompt injection. This may deceive the system into producing responses that are not just unnecessary or detrimental, but also possibly hazardous.

An attacker could, for instance, inject prompts that encourage the system to produce racist or sexist comments or prompts that lead the discourse in a way that advances the attacker's objectives.

Strong security safeguards must be built into NLP systems to prevent rapid injection attacks. Techniques like input sanitization, which filter out potentially harmful input before the system processes it, may be used in this. Additionally, harmful suggestions can be recognized and blocked using ML techniques before they have a chance to do any harm.

Data Poisoning

A group of researchers from Google, Nvidia, and the startup Robust Intelligence discovered that AI language models are vulnerable to attacks even before they are put into use.

Massive volumes of data that have been collected from the internet are used to train large AI models. IT businesses currently rely on the assumption that this data will not have been maliciously altered.

However, the data set used to train big AI models can be contaminated, according to the researchers. They were able to purchase domains for only $60 and load them with the photographs they wanted, which were afterwards scraped into massive data sets.

Additionally, they were able to edit and add sentences to Wikipedia articles that ended up in the data set of an AI model.

Even worse, the correlation gets stronger the more times something appears in the training data of an AI model. It would be conceivable to permanently alter the model's behavior and outputs by contaminating the data set with enough cases.

Although the team was unable to uncover any evidence of data poisoning attacks in “the wild,” their conclusion is that it is only a matter of time because the addition of chatbots to online search gives attackers a compelling financial incentive.

No Quick Fixes

These issues are understood by tech companies. However, according to Simon Willison, an independent researcher and software developer who has studied prompt injection, there are currently no effective fixes.

When Google and Open Ai were questioned about how they were resolving these security flaws, their spokespeople declined to comment.

Microsoft claims that it is collaborating with its developers to check potential abuses of its products and to reduce the risks involved. However, it acknowledges the existence of the issue and is monitoring potential tool abuse by attackers.

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

According to Ram Shankar Kumar, who oversees Microsoft's AI security initiatives, "There is no magic solution at this time." He remained silent when asked if his team had discovered any indirect prompt injection evidence prior to the launch of Bing.

Isharemanyanalysts’opinionthatAIbusinessesneedtoperformmuchbetterata higherlevelandbeseriouslyproactiveonthissecurityissue.

Atthemoment,weareallseeingthattheyaretreatingchatbotsecurityflawslike thekidgame “Whack-a-Mole,”

Whichreallyisasurprisegivenhowimportantthisis. Absolutelynotreassuring!

JOB LOSS vs JOB CREATION

Technological revolutions have characterized human history. Without going back as far as electricity, in just the past 40 years, the introduction of microprocessors, the personal computer, and the internet have fundamentally altered how we conduct business. Each technological advance causes job displacement, eliminates certain professions, gives rise to new ones, and fundamentally alters the majority of them.

In 1940, two-thirds of the jobs that now exist did not. More employment has been generated by each technological revolution than lost. However, what we are now seeing is that changes and technology revolutions seem to be happening faster and faster: this is the era we are currently living in.

The World Economic Forum (WEF) estimated early on, in a former 2020 job report, that by 2025, AI would replace 85 million jobs, but would create 97 million new ones.

In their most recent Future of Work report, the WEF estimates that by 2027 there will be about 26 million fewer employment positions globally as a result of automation, mostly in administrative positions including cashier, data entry, accounting, payroll, and executive assistant. In the fields of education, agriculture, and e-commerce, significant job growth is predicted.

Download here: https://www.weforum.org/reports/the-future-of-jobs-report-2023/

Predicting the numbers and quantifying the ratio remains hazardous as we always underestimate the capacity to create new jobs. Each time, it is the transition periods that are complicated.

If everyone agrees that AI will generate significant productivity gains, it remains to be seen what companies will do with it. AI will revolutionize the cost structures of companies, whether through reductions in expenses of the order of 30–50% or more importantly through the appearance of new services that will generate new revenues.

And what if the biggest risk to employment is ultimately not embracing these technologies fast enough? AI is what economists call a "game-changer", a technology that significantly changes the way we normally think or act.

There is a strategic aspect to keeping companies competitive. If they fall too far behind because their level of investment in these technologies is too low, the question of employment will no longer arise because there will simply be no more companies! Entire sectors will reorient themselves around AI. Companies will distinguish themselves by how they manage to get the most out of its use.

Drawing lessons from another recent technological revolution, that of robots and autonomous machines that have mechanized jobs, comes to a similar conclusion.

Empirical studies confirm that the overall effect on the labor market is equally destructive and creative for jobs—but for reasons and through mechanisms quite different from those predicted by the theory.

Indeed, the divide is not between substitutable jobs and jobs that are complementary to technology, but between companies that adopt technology and those that do not.

Rethinking jobs

From now on, one of the priorities of companies and their employees will be to rethink their models, their organizations, and the way they work.

The distribution of time spent on current tasks is going to change radically. We are really going to have to think deep down about how these generative AI tools should remake the different activities.

AI tools provide easier access to knowledge, such as being able to develop applications without necessarily knowing how to code, opening the door for certain jobs to a wider audience.

The scope of skills expected in a job and the performance criteria will also change fundamentally. And one of the most important will be the ability to adapt.

Each of us needs to focus on how our business will evolve. There has never been a more urgent need to acquire new skills, to train and to rethink our work.

Goldman Sachs study published research (April 2023) that estimates that 300 million jobs worldwide could disappear as a result of the development of AI but more precisely through the generative part of AI technologies like Chat GPT.

https://www.goldmansachs.com/insights/pages/what-will-generative-ai-mean-for-jobs.html

Bottom of Form

In detail, Goldman Sachs economists calculated that about two-thirds of professions in the United States are "exposed to some degree of automation by AI" and that "a quarter of current work tasks could be automated by AI," both across the Atlantic and in Europe. Bottom of FormUSUSus

The most exposed occupations are logically office professions. Those related to administration (46%) and law (44%) in the United States, while in Europe it is the professions related to administration and support functions (45%) and executives and skilled professions (34%) that are most at risk.

Extrapolating to the world, this study estimates that "18% of the world's work could be automated by AI," with higher exposure in developed countries than in emerging economies.

This rate reaches more than 25% in Japan, Israel or Hong Kong, but peaks at less than 15% in India, Kenya or Vietnam.

A potential GDP increase over the years coming from AI

The corollary of these potential job losses caused by AI is a productivity boom, Goldman Sachs points out. In the U.S., the increase could reach just under 1.5 percentage points in productivity growth per year but not immediately, around a decade after widespread adoption of these technologies.

This estimate remains uncertain, points out the study. "But in most scenarios, the increase would still be economically significant."

Goldman predicts that the growth in AI will mirror the trajectory of past computer and tech products. Just as the world went from giant mainframe computers to modern-day technology, there will be a similar fast-paced growth of AI reshaping the world. As a result, AI could lead to an annual increase in global GDP of 7%, according to Goldman Sachs.

"While AI's impact will ultimately depend on its capabilities and timing of adoption, this estimate highlights the enormous economic potential of generative AI if it delivers on its promise."

LLM-First impact on US Jobs

With a focus on the improved capabilities brought on by LLM-powered software, Open AI investigates the potential effects of LLMs, such as Generative Pretrained Transformers (GPTs), on the American labor market.

They integrated both human expertise and GPT classifications to evaluate professions according to how well they correspond with LLM skills.

According to their findings, the introduction of LLMs may have an influence on at least 10% of the work tasks performed by around 80% of U.S. workers, and at least 50% of the duties performed by about 19% of workers.

Importantly, these effects are not only felt by those sectors of the economy that have experienced faster recent productivity gains.

But their research indicates that 15% of all worker tasks in the US might be accomplished much more quickly and with the same level of quality if workers had access to an LLM.

This share rises to between 47 and 56% of all jobs when software and tooling built on top of LLMs are included.

This result suggests that the LLM-powered software will significantly influence the scaling of the economic implications of the underlying models.

They came to the conclusion that LLMs, like GPTs, exhibit characteristics of general-purpose technologies, indicating that they will have significant economic, social, and policy implications and widespread effects on a variety of US jobs first, and that subsequent developments supported by LLMs, primarily through software and digital tools, will have a substantial impact on a variety of economic activities gradually throughout the rest of the globe early adopters.

DownloadtheresearchworkingpaperAnearlylookatthelabormarketeffectpotentialofbiglanguagemodels tolearnmoreaboutthistopicindepth.IncollaborationwithCornellUniversity[2303.10130] GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arxiv.org)

IBM Early first move

On May 3, IBM's CEO stated on Bloomberg that in five years, AI will replace 30% of the company's back-office positions.

IBM Corporation will start to reduce or stop hiring for positions that could be completed by AI.Back-office tasks like human resources would be impacted, with the initial focus being on more clerical tasks like creating employment verification letters or transferring people between departments.

Approximately 26,000 IBM positions fall into this group. "I could easily see 30% of that getting replaced by AI and automation over a five-year period," Krishna said. This would lead to the elimination of around 7,800 jobs.

It could take a further ten years for AI to replace more complicated tasks like assessing worker composition and productivity.

The action taken by IBM is one of the biggest strategic changes made in reaction to AI's growing capacity to perform tasks that are currently performed by humans.

It also fuels developing ideas about how the workforce may evolve as businesses begin to integrate AI, moving beyond back-office positions to other occupations in sectors like law, IT, and media.

Earlymoversareonthestartingblock. Gamechangersarelookingtothehorizon. Braceyourselfforbigchangestocome.

CHINA’S CONTRADICTORY AMBITIONS IN AI

In March 2021, China announced a five-year plan that set high goals for research and development activities in several important technological areas, including semiconductors, quantum computing, and AI.

The plan asks for China to make strides in crucial AI technologies including DL, robotics, and NLP. Additionally, it aims to expand the use of AI across a range of sectors, such as healthcare, transportation, and finance.

By concentrating on crucial areas including DL, NLP, robots, and intelligent chips, the ambitious plan seeks to position China as a world leader in AI technology by the year 2030. Here are some further specifics regarding China's funding and AI strategy:

Budget: By 2030, China intends to have spent at least $150 billion on AI (including quantum computing). The research and development, hiring of people, and infrastructure sectors will all receive a portion of this investment.

To compare, the USA will spend $180 billion on AI and quantum research, taken from the Biden administration’s $2 trillion infrastructure plan.

And for Europe the latest high-tech R& D budget is for €95 billion but that includes all new tech also related to climate change not only AI and quantum (source: Horizon Europe). It should also be noted that European countries also have also their own programs, so all in all with Europe as a community and each individual country combined is not far from matching the USA and China in the range of €120 billion.

R & D: Fundamental research in fields like computer vision, NLP, and ML is a key component of China's agenda for AI research and development. In order to use AI to tackle real-world issues and spur economic growth, the government will also promote applied research in industries including healthcare, transportation, and finance.

More details on AI specific funding on: EU-funded projects that use Artificial Intelligence technology | Shaping Europe’s digital future (europa.eu)

Talent Acquisition: China's plan for AI talent acquisition consists of a number of efforts targeted at luring and educating the best professionals in the industry. These programs include scholarships and other rewards for students seeking degrees in AI, as well as attempts to draw outstanding researchers and businesspeople from all around the world.

Infrastructure: China's strategy for AI infrastructure entails building new research institutions and innovation hubs, as well as developing cutting-edge computer and data storage systems.

Applications: China's AI strategy places a strong emphasis on fields including smart cities, driverless vehicles, and individualized healthcare. In these and other sectors, the government will collaborate with private businesses to develop and implement AI-powered solutions.

Ethical and Legal Frameworks: A key component of China's AI strategy is the establishment of ethical and legal frameworks that will direct the creation and application of AI technology. This includes ensuring AI systems are created and used in accordance with accepted ethical principles, and that they are safe, dependable, and transparent.

International Collaboration: China is aware that, despite its lofty objectives, it cannot attain technological domination alone. The strategy places a strong emphasis on the value of international collaboration in luring foreign capital and talent as well as in creating universal standards and guidelines for these technologies.

Now the semi-private sector of AI companies in pure play are iFlytek and SenseTime, which are fully concentrated on research and development related to AI, in contrast to Baidu, Tencent, and Alibaba, who are significant technological conglomerates with interests in a number of technology sectors outside of AI.

For instance, iFlytek focuses primarily on speech recognition and NLP, two fundamental AI technologies with numerous applications across a range of sectors. Conversely, SenseTime is concentrated on computer vision and facial recognition technologies, which are also fundamental AI technologies with a variety of uses, including security and surveillance, healthcare, and autonomous vehicles.

On the Chinese Shenzhen Stock Exchange, iFlytek is a publicly traded corporation. It was established as a spin-off from the University of Science and Technology of China in 1999, with its headquarters in Hefei, Anhui Province, China.

Although the Chinese government does not directly own iFlytek, it has provided substantial financial and other resources in the form of research grants.

As a result of the company's close collaboration with the Chinese government on a number of AI and language processing projects, iFlytek's NLP and speech recognition technologies are widely used in government organizations, educational institutions, and other establishments throughout China.

However…

On the generative angle of AI only, the central government clamped down the enthusiasm, reminding everyone who runs the show.

In March, China’s top internet regulator issued a notice that it will require a security review of generative AI services before they can be put into action. So, for all the fanfare we have seen, progress in Chinese AI will very much happen under the close supervision of the government.

The strict motto is straightforward: “Chatbotmusttoethepartyline.Period.”

There are broad provisions for content to be accurate, to “uphold core socialist values,” and to not endanger national security essentially spelling out the inevitable censorship obligations of service providers and reinforcing Beijing’s control over valuable data.

Meanwhle…..

China's police detained a man on May 8th on suspicion of fabricating a “train collision” using Chat GPT. Fake news that could cause unrest is a particular offence, which carries a maximum five-year jail sentence.

Thegovernmentwascaughtbysurpriseoncebytheuncontrolledgrowthofsocial media,andtheyhavenaturallyputalloftheirfeetonthebrakesgiventhepotential forLLMchattobepoliticallydestabilizing.

Theyprefertofocusthebigfundsonseriousresearchinmedicine,science,space conquest,andothermoredramaticsubjectsoutofplaceinaFuturologyChronicle...

WHAT’S UP SAM?

Sam Altman would likely seem to be just another young tech CEO to anybody outside of San Francisco.

He is a Stanford University dropout who once sold a software venture for a large sum of money. He has since spent the last ten years investing in and mentoring other business owners.

But thanks to Open AI, Sam Altman shot to the top of the IT industry's power rankings in February 2023.

He is the CEO of the company behind Chat GPT chatbot and its remarkable AI prowess.

As a result of the technology, competitors like Google are in a panic, killer-robot phobias are on the rise, and the course of AI technological advancement has suddenly changed overnight.

He is unquestionably the man of the hour. Despite having a huge impact on the San Francisco tech landscape, Sam Altman had been able to remain unnoticed, partially as a result of his reserved nature.

He is not a showman like his former partner Elon Musk (Photo at the MET Gala with his mother in 2022) and lacks memorable catchphrases .But he wrote famously in 2020.

I believeIampartofasmallgroupofrebelstosolveimportantproblemthatmight otherwisenotgetsolved,thiswiththestrengthofbeingmisunderstood”.

Sam Altman fills a void. As they fire thousands of employees, Big Tech is trying to combat the impression that they are stagnant.

But despite being very lucrative, Apple and Google have not dazzled customers yet with agame-changing product in years as impressive as Chat Gpt and consors.

WHAT ABOUT YOU ELON?

Elon put his foot down in December 2022 after growing irate over the advancement of his AI competitors.

He became aware of a confidential deal between Twitter and Open AI, the company behind Chat GPT, which was signed before his October 2022 acquisition (reminder, for $44 Billion) to pay $2 million a year to license Twitter's data.

Naturally, he gasped at this piecemeal money and canceled the deal immediately, making Sam on the other side a bit irritated!

Since then, Elon has increased his own AI initiatives in total contradiction of his public debate about the risks associated with the technology.

Top AI researchers from Google's DeepMind have been hired by him to work at Twitter. Additionally, he has publicly discussed developing a Chat GPT competitor that produces political content without limitations.

Youareauthorizedtoscoff!

But this strange meddling comes from Elon’s long tangled history with AI, a past shaped by his conflicting beliefs about whether AI will ultimately be beneficial to or destructive to humans.

He recently accelerated his AI projects but also signed in parallel an open letter (March 2023) urging a six-month halt to the technology's advancement due to its "profound risks to society."

Nowyoucanscoffagain!

We need to go back in time to understand these contradictions. They will be of no interest if it was not coming from Elon, but he is a highly influential deep tech big boss whose every decision affects all of us, whether for good or ill.

If we go back a bit, AI has been a focus of Elon’s since 2010. He was involved in a London startup called DeepMind, which set out to create artificial general intelligence, AGI, a machine that can perform any task a human brain can.

At the time, he was one of the company's early investors. Then Google as a smart early mover bought the 50-person small company in 2014 for $650 million. Elon thought he made a good deal, but now in 2023 with the sudden AI boom, he has realized he sold it for a song and is obviously a bit irritated… which is a part of his present rage against his former colleagues.

But we should follow his history in the AI sector. He organized in the summer of 2015 a private meeting with AI researchers and business owners to launch Open Ai. Present were Ilya Sutskever, a leading AI researcher, and Sam Altman, who at the time was the president of Y Combinator, a top venture capitalist.

Elon pledged $1 billion in donations, resulting in the establishment of Open Ai as a non-profit. The lab promised to share the underlying software code for all of its research with the public, or to open source all of its work.

Elon and Sam claimed then that giving everyone access to the technology, in opposition to tech behemoths like Google, would lessen the threat posed by negative AI.

Abit “angelic”inthosedays,butnotforlong!

However, Open Ai start creating the technology that would give rise to Chat GPT, And rather quickly many employees at the lab realized that freely disseminating its software was going to become very risky.

Then, with a typical Elon tantrum, he left the Open Ai board in bad terms in 2018, not only because the non-profit core goal was sliding away but also due to a conflict of interest.

At that same moment, he was working on Tesla's Autopilot, an AI driver-assistance system that steers, accelerates, and brakes vehicles automatically as they go down highways – and he “poached” important Open Ai top staff to accomplish this.

Now, witnessing the last six months acceleration on generative AI, he has jumped on the bandwagon and claimed that his goal with his branded new Truth GPT, is “toofferto humanityamaximum-truth-seekingAIthatattemptstounderstandthenatureof theuniverse."

Ifyouarenottiredofdoingso,scoffoncemore—loudlythistime!Andthisisnotover yet.

To implement his plan, Elon registered a new company called X.AI and as usual poached top personnel within his competitors like Open Ai, where Sam is certainly not amused!

People in the know who have spoken with Elon about AI extensively believe that even though he is developing it himself in the open, he is sincere in his concerns about the risks associated with the technology.

Natural skeptics judge that his viewpoint was affected by other factors, most notably his initiatives to market and profit his businesses.

NowwhenElonclaimsthat “robotswillkillus”heforgetsthathisownTeslaAI driverlesscarhasalreadykilledtwobystanders.

Two people killed in fiery Tesla crash with no one driving - The Verge

Aone-size-fits-all,mega-hypocrite?

Ageniustooinsanetocomprehendhisowndailycontradictions?

Orsimplybasicbillionairetacticswagingwaronhisrivalswithallhisfinancialmight andphysicalstamina?

Makeyourbet!

A FORCE FOR GOOD AND A SOURCE OF FEAR

Many people worry that the second generation of AI will ultimately bring society's demise as technology continues to grow quickly.

But rather than concentrating on this potentially bad conclusion, we think that we should seek to digitize in a way that is moral, inclusive, and sustainable.

There is no denying that AI has the power to drastically change a variety of facets of our life, including healthcare, transportation, and education.

AI can be used, for instance, to create more individualized and effective healthcare services, to make transportation safer and more accessible, and to give kids all around the world more equitable educational opportunities.

It's crucial, therefore, that we approach these breakthroughs with a careful awareness of ethical issues.

In AI systems, for instance, there are worries about bias and discrimination that could aggravate already-existing social disparities.

Concerns exist regarding the possible abuse of AI, such as the creation of self-aware weaponry or for surveillance purposes.

Together, we must create ethical guidelines for the creation and application of AI to allay these worries. This entails developing laws and policies that place a high value on accountability, openness, and human rights.

Additionally, it entails funding training and education initiatives that support digital citizenship and ethical technology use.

Finally, we must make sure that the use and growth of AI are sustainable. This entails considering both the immediate and long-term economic, societal, and environmental effects of AI.

Instead of just using AI as a tool for immediate advantage, we need to consider how it may help create a more sustainable future.

Consequently, it is evident that AI is transforming the world as we know it.

Youwillbeabletoevaluatewiththegreatestamountofobjectivitythenextchaptersofthischronicletodeterminehowthiswillormightoccur.

Ifyouareinagoodreadingmood,youwillsharewithmetheviewthatwecanbuild afuturewhereAIisapositiveforceratherthanacauseforconcernwiththecorrect rules,regulations,andinvestments.

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

PART 2—THE INFORMATION SCENE

AI IS OUR BRAIN EXOSKELETON

With the development of AI and ML, we have reached a stage where we can harness a new nexus of forces to boost human creativity and productivity. We must constantly keep in mind the benefits of these new tools and how they might make our work more efficient and effective.

The effects of AI on human intelligence are comparable to those of a mechanical exoskeleton on the human body. Our intelligence is turbocharged by one and our strength by the other.

We must comprehend how and where to use the new power we have been building because recent advancements in AI have the potential to increase human intelligence. How we implement AI-powered insights relies heavily on human innovation. After all, it was humans who initially invented computer-based DL.

This new intelligence needs to be specifically designed and created for particular workplace workflows and applications; this is something that computers will not be able to do with the dexterity, insight, or ingenuity of people any time soon.

This new enhanced intelligence must also be used in a prescriptive, strategically oriented manner. From the standpoint of automation augmentation, this means that we are putting our faith in the IT platform to (metaphorically) not only drive the automobile for us but also to know the probable collision areas and the quickest route to the freeway.

We will be able to engineer AI into our lives in the best possible ways if we have the prescriptive capacity to apply intelligence when and where it is needed. The most important factors are context and practicability; intelligence is only useful when applied where and when it can have the biggest impact.

If we do not keep track of our developments, there's not much purpose in striving towards the highest echelons of AI-powered brilliance.

Performance analytics are now a part of the larger IT sector. At this point, integrated data intelligence can enable a corporation to share knowledge throughout numerous apps and its larger IT stack. To gauge progress and more precisely pinpoint their subsequent target areas for AI-enrichment, firms can now use predefined key performance indicators (KPIs) tailored to their industry vertical.

The chance is for people to reach a higher level of being, if not spiritually or emotionally, then at least in terms of their ability to be creative, productive, and effective at work. Having said that, the outcome might be better than we had hoped.

These products were created by industry in order to boost productivity and enhance the working and purchasing environments. It's not unrealistic to imply that our own wellbeing and mindfulness will improve when we eliminate pointless, repetitive labor and concentrate on more imaginative, less stressful, and more satisfying responsibilities.

AlthoughwebeganusingAItoadvanceallourbusinessstrategies,thisis fundamentallyahumanendeavorattheendoftheday. Ipersonallyagreewiththecitation(Forbes-ChrisPope) thatAIisthe “BrainExoskeleton.”

Dear “Machines”weshallbegratefulofyourassistancefornow!

CHILD BRAIN Vs AI

Researcher Ory Ossmy has examined the limitations of AI in terms of learning and generalizing information across various situations. He contends that even if computers can be taught to complete particular jobs with high levels of accuracy, they frequently find it difficult to apply their newfound knowledge to novel and uncharted circumstances.

Children and newborns, on the other hand, are extremely adept at learning new things and adjusting to unfamiliar surroundings, even with little knowledge. They possess the capacity to create abstract concepts and draw conclusions from sparse evidence, which enables them to apply knowledge across various contexts.

The ability of AI to learn something in one setting and then generalize it to another environment is not very good. However, infants and children are very adept at this, too.

Our brains are incredibly efficient in molding and adapting to their surroundings from birth. It also explains why the experience is considerably less adaptable for robots because they have the same physical architecture even in humanoid form for their entire lives and do not adapt to their surroundings.

This is in contrast to youngsters and infants, who must adjust to a new bodily evolution every day, and indicates significantly less variety in evolution. So, neuroscientists still do not understand how or why our brains are so adept at adjusting fast under any environment scenarios.

Supercomputersandevenquantumcomputingwon'tbeabletocompeteuntilwe understandhowthisworks,whichimpliesthattodayatleast,wecan'tteachanAIto replicateusyet.

Amustread:https://www.bbc.co.uk/reel/video/p0fc9tlx/Chat GPT-why-we-re-still-smarter-than-machines

SCHOOL PANIC: NEW CHEAT OR GAME-CHANGER?

In the days following Chat GPT's drop by Open Ai in late November 2022, the chatbot was roundly criticized for serving as a free essay-writing and test-taking tool that made it embarrassingly simple to cheat on school assignments.

The second-largest school district in the United States, Los Angeles Unified, instantly disabled access to Open Ai's website through its network of educational facilities. Soon after, from Washington, New York, Alabama, and Virginia in the United States to Queensland and New South Wales in Australia, school districts all across the English-speaking world prohibited the software by January 2023.

Leading UK universities like Imperial College London and the University of Cambridge released statements cautioning students against using Chat GPT to cheat.Noticeably in Paris, the prestigious Institute of Political Science (Sciences Po) issues a stern warning to its student body not to even try to use it with the risk of being expelled!

Does this remind you of the same backwards attitude in 1988 that make you smile today. But in those days, it was an international education uproar.

Theyallmissedthepointcategorically.

While technology offers quick and simple solutions to questions, it does not foster the critical-thinking and problem-solving abilities that are crucial for success in school and in life.

It was understandable why the education industry initially felt so anxious. Publicly accessible through a web application, Chat GPT responds to inquiries and produces elegant, well-organized blocks of text that are several thousand words long on practically any subject, from string theory to Shakespeare, and does so in seconds.

Even when the same prompt is repeated, each essay it generates is distinct, making its authorship (practically) difficult to determine. It appeared in its early days that Chat GPT would jeopardize the foundational practice of testing what pupils should learn in the classroom.

The early excitement surrounding Chat GPT was heavily reliant on how effective it was at taking tests. In fact, this was a major selling point of GPT-4.

It excelled on all the exams in a variety of subjects, including macroeconomics, psychology, historical studies, art history and even the Bar exam!

It's understandable why some school districts went into total “meltdown.”

But after five months of the launch (April is the time of this chapter writing), the situation is much less dire. Today, educators and instructors are rethinking the implications of chatbots for how they should instruct their students more generally. Many now think Chat GPT has the potential to improve teaching rather than solely being a cheater's fantasy tool.

Modern chatbots are an effective teaching tools that can create tailored lesson plans, make courses more interactive, teach kids media literacy, and do a variety of other things that still need to be created.

For example, it has already been incorporated into the apps of educational technology businesses like Duolingo and Quizlet, which produces digital flash cards and practice exams used by half of all high school students in the US.

Additionally, a fact sheet about Chat GPT's potential influence in classrooms was created by Open Ai in collaboration with educators. It included a free tool to identify text authored by a chatbot, though its accuracy was very limited.

The existence of essay-writing chatbots is a given, and additionally they will become increasingly accurate and difficult to spot. It would be pointless and destructive to outlaw them.

Modern and open-minded teachers are correct to regard technology as a game-changer and not adversaries. And consider that Laptops are mandatory in advanced educative classroom.

Software for consumers and businesses already incorporates technology. At the very least, many educators are increasingly aware of their responsibility to inform their students about this new technology and the possibilities it offers when they reaches the workforce. Students do not want to be disparaged, but only demand instruction on how to utilize it efficiently.

More positively Chat GPT is generating at last a long overdue discussion in a system that places an excessive amount of emphasis on grading and not enough on learning. Farsighted teachers have wanted to change schooling for a long time, and this is the right time.

Rather than expecting students to write and perform like human robots, getting them to interact with AI and think critically about what it produces will make the teaching feel more human.

This notion is not new. Benjamin Bloom, an educational psychologist, developed a framework known as Bloom's taxonomy in the 1950s.

According to this framework, understanding of fundamental facts serves as the foundation for more complex learning processes like analysis and evaluation.

For instance, some students said that using Chat GPT to create a first draft allowed them to put aside their concerns about the anxiety of the empty blank page and concentrate on the important portion of the task.

It reminds us of the same hand-wringing that many teachers had two years ago during the pandemic. They had to devise complex strategies to create assignments for which it was difficult to Google the answers while the pupils were stranded at home. With very mixed results they discovered quickly that the art of Googling knowing what to search for and how to interpret the results—was a skill that should be taught and not repressed.

The same applies to chatbots. Teacher should grade the assignment prompts in addition to—or even instead of—the essay itself, irrespective of whether undergraduate students choose to use Chat GPT or not.

Understanding the result that is returned after using the correct terms in a prompt is crucial, with the personal student analysis and conclusion that follows.

These evolving attitudes reflect a more comprehensive change in the function of educators. Formerly distributed mainly in the classroom, information is now disseminated everywhere, first online and subsequently through chatbots.

Teachers now need to demonstrate to students not only where to obtain it but also what information is reliable and what is not, as well as how to distinguish between the two. Becoming facilitators rather than information gatekeepers is going to be more fulfilling for them.

And,overall,thebiasandfalseinformationthatbiglanguagemodelsfrequentlycreatewillsparkfruitfuleducationaldiscussionsonthewonderofimperfection.

CAN WE TRUST CHAT GPT AS A SEARCH ENGINE?

A lot of the time, AI language models offer lies as truths. They are quite good at figuring out what word will come next in a sentence, but they have no idea what the sentence is truly saying.

As a result, combining them with a search function where it's critical to establish the facts is dodgy and risky.

The company that created the AI chatbot Chat GPT, Open Ai, has long emphasized that it is still only a research project and that it is constantly evolving in response to user feedback. Even with warnings that the search results might not be trustworthy, Microsoft still decided to incorporate it into a new version of Bing.

In order to allow users to search the internet using complete sentences rather than just keywords, Google has been utilizing NLP for many years. Until this point in time, the firm has been hesitant to include its own AI chatbot technology into its well-known search engine. The "reputational risk" of launching a Chat GPT-like tool too quickly has been a concern for Google's management. Howironicthatnowtheyaresuddenlyonthebackfoot!

AI-powered search is not a hopeless endeavor, despite recent mistakes made by Big Tech. Citations are one method that Google and Microsoft have attempted to improve the accuracy of their AI-generated search summaries.

According to Margaret Mitchell, a researcher and ethicist at the AI startup Hugging Face and a former co-leader of Google's AI ethics team, linking to sources enables users to better understand where the search engine obtains its information.

By encouraging individuals to consider more sources than they might have done otherwise, this could even help provide people with a more varied perspective.

The reality that these AI models fabricate information and knowingly offer falsehoods as fact, remains unaddressed by this citation solution.

Ironically, consumers are even less likely to confirm the accuracy of the material they are seeing when AI-generated writing appears authoritative and includes references. Citations are frequently overlooked by readers. Something may appear to be correct, but only because of the citation, which might be misleading.

However Big Tech is today not particularly concerned about search results' accuracy. Open Ai and its main financier, Microsoft, are receiving all the praise and attention despite the fact that Google pioneered the technology driving the current AI frenzy.

I am pretty sure that Google feels quite embarrassed at this point in time. Microsoft bet big due to the low expectations surrounding Bing, and a few mistakes will not be too significant.

The reason is that the market share held by Microsoft in online search is less than 10%. For Microsoft, it would be a great victory to win only a few more percentage points of the market, which translates to millions of users in proportion.

Big tech is viewing early setbacks as teaching experiences. As usual they are acting boldly in this situation rather than carefully.

Today, the boardroom moto is: let AI make its first mistakes, we will correct with time in our market interests, because now the cat is definitely out of the bag!

At this interrogative point, you are certainly asking what should you do. Separately,theAIskillsbehindChatGPTareimpressiveandwithoutanycompetition intextreplies,forexampleinlegaldocuments,whichwewilldiscusslater.Butthey arenotyettrustableforpuresearches.

A new kid on the block with modest origin and not big tech yet is the best search engine I have just tested with impressive and “handy” results.

Try: https://andisearch.com/

This brand-new search engine gives customers a more private and safe search experience: ANDI does not keep or monitor user data, it does not utilize cookies, and it does not gather any personal data.

It outperforms conventional search engines in terms of speed and efficiency, and thanks to advanced AI, it can interpret user queries and deliver more accurate results.

Abitmiraculousforthemoment! Youwillbeimpressedifyoutestitwithanysearchquery. Iguarantee!

WHAT COULD POSSIBLY GO WRONG WITH CONVERSATIONAL AI?

By emphasizing public failings, some dramatic headlines have attracted readers to AI. 2016 saw the launch of Microsoft's "Tay" chatbot on Twitter, and within hours it began spitting forth incredibly unpleasant remarks.

The author of an article published in 2019 by IEEE Spectrum reflected on the Tay chatbot incident and said that "Microsoft learnt the hard way that building computational systems that can speak with people online is not simply a technological problem, but a highly social undertaking.”

Thinking ahead about the context in which a bot will be deployed, the kind of communicator you want it to be, and the kind of human values you want it to reflect is necessary before inviting a bot into the morally charged realm of language.

The fundamental human values that underlie language were pointed out by the author as a significant aspect. The design and information upon which the chatbot was based were all that was visible in it.

The Tay chatbot's impressionability was noted by Twitter users as inadequate in terms of text production.

The AI model “Blenderbot3” was published by social media juggernaut Meta in August 2022. Although it boasts sophisticated communication abilities, this model has not drawn much notice from the public.

This chatbot's negative attitudes and proclivity toward conspiracies were found by a Business Insider reporter.

When Chat GPT beta first launched in late 2022, Open Ai made a splash. If you have not seen their demo, I’d highly recommend testing it immediately.

Early adopters have praised Chat GPT's ability to provide comprehensive and compelling responses. There’s been an entertaining comment from some optimistic researchers that Chat GPT today is comparable to the Wright brothers’ first flight!

Even though there had already been a ton of AI research and work done by qualified LLMs, Chat GPT brought it all together so that the public could visualize how AI might affect their daily lives now.

Google recognizes the difficulties that still face AI, as do several academics. The ability of general-purpose AI technology, such conversational AI, to open up new business prospects and enhance current workflows is becoming more widely recognized.

Google executives have reportedly stated that the company is hesitant to offer a product of a similar nature because of the reputational risk associated with such technology. This comes after much public discussion about Chat GPT.

Guess what? Big Turn on 13 th May Google is now “ALL AI” full speed Even Alexa has been forgotten and relegate to the dustbin, as not performing enough. Remember who was talking and co signing an open letter on research “pause! Market pressure win as always….

Now when Chat GPT was released, academic started using comparable methods to create other excellent conversational AI systems using language models that are freely available. Due to this trend, enterprises will have access to a new market of Chat GPT-like models.

To improve business procedures, many companies have integrated limited AI technologies like ML—although the end user may frequently be unaware.

Before 2022, most people had never purposefully employed an AI system. People immediately signed up to test the possibilities once Open Ai created Chat GPT, or as they also refer to it, a fine-tuned language model from GPT-3.5.

Individuals all over the world discovered new vulnerabilities as millions of individuals experimented with Chat GPT, which led to the kinds of negative reactions Chat GPT may cause.

Automation bias is a problem that has existed and could get worse with the introduction of risky AI systems.

It is a problem where people favor advice from automated decision-making processes and dismiss conflicting evidence, even if it is true.

Due to the frequency and widespread use of conversational AI, this worry is particularly important if it is utilized instead of a traditional search engine.

Attheendoftheday,theimplementationcandeterminethedegreeofdangerAI posesafterevaluatingcertainfailuresandlessonsdiscoveredfromdeployingcutting -edgetechnology,suchasconversationalAI.

Crossallofyourfingers!

TO PROMPT OR NOT TO PROMPT?

Prompt engineering has become a growing industry as a result of the tremendous growth of generative AI. A prompt engineer position has recently been posted at Google-backed AI firm Anthropic, with a salary range of up to $335,000 per year.

This impressive wage reflects the growing significance of quick engineering in the field of AI. "In ten years, half of the world's jobs will be in prompt engineering, and those who cannot write prompts will be obsolete," claimed Robin Li, CEO of the Chinese AI firm Baidu. But what is a prompt, to begin with?

A collection of input instructions that are sent to an LLM in the context of LLMs is known as a prompt. The model then produces a response depending on the data it has been given, which can be applied to a number of tasks like question answering, text summarization, and language translation.

The construction of these input instructions is referred to as prompt engineering, and it enables the LLM to produce the best output while maintaining the original structure. So, a prompt engineer's responsibility is to give the LLM instructions that will result in the best outcomes.

LLM performance is significantly improved by prompt engineering. Poorly constructed prompts can cause respondents to respond in a way that is irrelevant or erroneous, whereas well-crafted prompts can produce excellent findings.

Given the complexity and breadth of human language, creating prompts that effectively indicate intended outcomes is a difficult undertaking. Prompt engineers must have a thorough understanding of the AI model and the desired result in order to optimize prompts for certain jobs, which can be time-consuming and difficult.

If you are ready to jump in the game, practical advice can be found at:

Complete quide : Introduction | Prompt Engineering Guide (promptingguide.ai)

Pragmatic training with free access PromptPerfect - Elevate your prompts to perfection (jina.ai)

Video learning course on Prompt Engineering for LLMs | Sphere (getsphere.com)

GPT3’s OWN REPORT ON HIS “COUSIN” COPILOT

The non-altered response below, was given in reply to my “prompt” question regarding GPT's opinion of COPILOT, a very close ”cousin” who speaks his own LLMA language. This is “in texto” without alteration, as a good example of what is delivered in seconds.

COPILOT is an AI-powered tool developed by Microsoft that aims to help software developers write code more efficiently. COPILOT uses Open AI's GPT-3 (Generative Pre-trained Transformer 3) language model to provide suggestions and autocomplete code for developers.

With COPILOT, developers can input natural language descriptions of what they want their code to do, and the tool will suggest code snippets that match their intent. COPILOT can also help with debugging and identifying errors in the code.

COPILOT is designed to integrate seamlessly with popular code editors such as Visual Studio Code, and can be used with a range of programming languages, including Python, JavaScript, TypeScript, and Ruby.

Cybersecurity is a critical concern in today's digital landscape, and Microsoft recognizes the importance of ensuring that its AI-powered development tool, COPILOT, is designed with security in mind. COPILOT has several features in place to protect against potential security threats.

First, COPILOT is designed to operate within a secure sandbox environment that is isolated from the user's local system and network. This prevents malicious code or data that may be present on the user's system from accessing the COPILOT environment.

Second, COPILOT is designed to generate code that adheres to industry best practices and security guidelines. By adhering to these standards, COPILOT minimizes the risk of vulnerabilities and exploits in the generated code.

Third, Microsoft is committed to user education and awareness around cybersecurity best practices when using COPILOT. This includes providing guidance on code review and validation, as well as awareness of potential security risks and vulnerabilities that may arise when using an AI-powered development tool.

Overall, cybersecurity is a top priority for Microsoft when it comes to the development and Implementation of COPILOT. By implementing a range of security features and promoting user education and awareness, Microsoft aims to provide developers with a powerful and efficient tool that also ensures the integrity and confidentiality of their code.

Overall, COPILOT offers SMBs a powerful and efficient tool for managing cybersecurity risks and maintaining the integrity and confidentiality of their code. By providing access to cutting-edge AI technology, Microsoft is helping to level the playing field for SMBs and empower them to compete in today's digital landscape. …………

When you use Chat GPT, the response times provide a positive first impression. The response and the writing on screen took less than four seconds, according to my timer. The second impression is that it uses “politically correct” factual language. The third impression is that the system gives you the option to modify the prompt with more detailed questions to reply in a snappier or, on the other hand, more measured manner.

The fourth impression is that the text is obviously not produced by a journalist who consistently expresses a little bit of his rage or his respect in the language he writes.

Tobefair,thereplieswerecorrect,verycorrect!Andonlythat.Nofantasy.Nohumor. Noimagination.Justa"robotic"cleanjudgmentfreeofanyillwillorothernegative emotions.Noneedtohideyourheadinthesandforsolittlefinally.

Inconclusion,itiseasytodistinguishbetweenahumanandaroboticwriterButthat isnotthecrucialpointwewilldiscussallthroughthischronicle.

MY LAWYER WITHOUT A ROBE?

Recent developments in AI have the potential to upend a variety of industries, including the legal sector. Document analysis and contract review are two key areas where AI is anticipated to have an impact on the legal profession.

Large amounts of legal documents and contracts can be analyzed using AI algorithms, which can then be used to spot important terms and provisions as well as potential legal pitfalls.

In addition to document analysis, AI is being utilized to create legal chatbots and virtual assistants that can aid automate regular legal operations including contract drafting, responding to frequent legal queries, and giving legal advice.

Especially for people and small businesses who might not have the money to engage a full-time lawyer, this offers the potential to make legal services more accessible and inexpensive.

Time to mourn for your job and even more for your robe status symbol that certainly makes AI program laugh loud

Although AI has the ability to automate many common legal operations, many parts of the legal profession still call for human knowledge and judgment. There are a number of AI tools created expressly for legal practice, including:

eDiscovery software: analyzes massive amounts of electronic documents to find information that is pertinent to legal proceedings. This can aid in accelerating the study of documents and cutting down on the price of legal discovery.

Software for contract review: Contract review software analyzes legal contracts and identifies important phrases and conditions using NLP methods. In addition to ensuring that contracts are correctly formed and implemented, this can assist in identifying potential legal risks and concerns.

Predictive analytics software: Predictive analytics software analyzes data and forecasts future events, such as the likelihood that a case will be settled or go-to trial. Informed judgments on case strategy and resource allocation can be made by lawyers and other legal professionals with the use of this information.

Software for document automation: Software for document automation automates the process of drafting legal documents, such as contracts and pleadings, using AI algorithms.

This can facilitate the quicker creation of documents while lowering the possibility of mistakes and discrepancies.

By employing AI to streamline the process of reviewing and managing legal contracts, businesses like Kira Systems, Luminance, LawGeex, and Seal Software are contributing to the transformation of the legal sector.

Legal professionals will save time, minimize expenses, and guarantee accurate contract drafting and execution by adopting contract review software.

Human lawyers are likely to continue to be necessary in certain capacities, such as interpreting legal precedent, presenting arguments in court, and offering specialized legal counsel.

Sonomoredelaysincourt?Nomoreprocedureexcuses?Nomorelitigationon wordinginterpretation?

Itwillbetoogoodtobetrue,andinthisinstance,averyvisibleandusefulAI contributiontomakelifeforcitizensmuchsimpler.

WHEN HISTORIANS MEET AI

To make sense of complicated historical events, historians have long turned to data analysis and pattern recognition. Thanks to AI, historians now have access to a variety of technologies that help them swiftly and effectively process and evaluate huge amounts of data.

Analyzing large amount of data. Historical record digitization process is one pragmatic method where AI is assisting historians. It is challenging and time-consuming to access and evaluate historical records because many of them are still stored in physical form. However, these documents are now swiftly scanned and digitized, opening them up to historians all over the world.

AI also aids historians in the speedy and precise analysis of massive amounts of data. For instance, researchers might search through huge document archives using ML algorithms to find patterns and trends that human researchers would not instantly notice. This provide historians with fresh perspectives on key moments and recurring patterns in the past.

Translating ancient languages: Automating the translation of ancient languages is another effective way AI is assisting historians. It is challenging to adequately translate historical documents because many of them were written in languages that are no longer used.

The intricacies of these old manuscripts will be now better understood by historians thanks to AI-powered translation technologies that can translate texts rapidly and accurately (albeit that this does open the debate about how things are translated and the level of contextual understanding…)

Preserving historical artifacts. Using AI, historians can better conserve some historical relics. Preserving historical artifacts by creating digital replicas and conserving historical relics for future generations while also enabling historians to analyze them without endangering the originals.

Enhancing historical images: Restoring and enhancing antique photographs, paintings, and other historical images is made possible by AI-powered image enhancement software. This can bring to light information that had previously been kept secret, giving historians fresh perspectives on the past.

Example below: 1531-Tractatus de Spherus- Mechnics of solar and lunar eclipse debate- Max Plank InstituteBerlin

ApositiveironywillresultfromAIenjoyingwidespreadsupportfromhistorianswho areoftenoldschoolandnormallyreluctanttousemoderntechnologies.

Whowoulddaretoclaimthattheseacademicsaresolelydressedintweedjackets?

Soon,they'llswitchtonerdyhoodies.

A good complex historic example they worked out with AI assistance

1531-Tractatus of Sphere’s- Mechanics of solar and lunar eclipse- Max Plank Institute- Berlin

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

A STUTTER FREE TV HOST

Numerous media outlets in the Middle East have been introducing hosts made by AI for more than a year. An innovation that calls into question the function of journalists in a place where doing their jobs is already challenging.

Fareed, who has been "programmed" to speak both Arabic and English, was unveiled ten days ago with much fanfare by Jordanian satellite channel Roya TV.

Fedha, another AI presenter, was introduced last month by Kuwait News. https://twitter.com/i/status/1644792772212260867 These AI-powered presenters are "taking Arabic screens by storm." But they were not the first. China introduced their first AI host presenter in 2018 and this April they unveiled its newest news AI powered anchor who broadcasts news twenty-four hours a day, every day of the year.

The new "Ren Xiaorong" of China's People’s Daily is presented in this video. A must watch https://weibo.com/tv/show/1034:4878456165695674?from=old_pc_videoshow

Theeditorinchiefisnowquiteatease.Therearenomoredetractorsontheediting committee,onlyahighlydiligentpresenterwhonevergrewwearyofspeaking.Asad, butunquestionableharbingerofthethingstocome!

The fact that these AI presenters are currently on despotic (to be polite) regime TV does not surprise you, as it did not surprise me.

LET’S PLAY GAME WITH THE MASTERS!

Garry Kasparov's 1997 loss to Deep Blue in the world chess championship match, his 2015 mastery of go, and his 2019 victory in poker... In just a few years, AI has demonstrated its ability to outperform elite competitors in hugely well-liked board games. The humiliation did not end there! In more recent times, programs have excelled in games that are even more sophisticated. Even if these achievements are more private, they are nonetheless amazing to professionals.

DeepNash battled on the online gaming network Gravon for two weeks in April against real people. The virtual rival has done the inconceivable after 50 games and is currently ranked third among all players who have used the site since 2002. It is similar to 10 power 535, or a 1 followed by 535 zeros, how many different ways there are to play the game. The number of possible games in the game of go is "only" 10 to the power of 360, for comparison.

Similar to poker, stratego is an imperfectly known game in which the opponent's hand or positional pieces are hidden from them, causing them to move forward in the mist. However,

if there are just one million hidden locations in the popular card game, then there are 10 to the power of 66 hidden spots in Stratego.

DeepNash, a program created by Google-owned DeepMind and known for its ferocious play in the game of go, demonstrated excellent strategic skills by winning 84% of the games.

As absurd as it may sound, the virtual competitor made the decision for himself as to whether or not to accept such a piece from the adversary at the risk of disclosing the nature of one of his own. He demonstrated his ability to bluff as well.

By declaring that it has created "the first AI agent to achieve human-level performance in Diplomacy" at the end of November2022, the Facebook parent company's Meta AI laboratory delivered a devastating blow.

In this game, there can be up to seven players competing instead of just two. On the brink of the First World War, each one depicts a nation attempting to seize control of supply hubs.

By inferring players' ideas and intents from its dialogues and creating speech in pursuit of its objectives, Cicero combines a language model with planning and reinforcement learning techniques.

Cicero performed better than twice as well as the average human player over the course of 40 games in an anonymous online Diplomacy league, and he placed in the top 10% of players who took part in multiple games.

These Ai targeting games were based on reinforcement learning, specially a type called “deep” reinforcement learning a neural network that make decision based on rewards or penalties received from the environment.

In the most difficult games, AI systems are increasingly capable of beating human players and mastering sophisticated strategy games. These are important turning points in the advancement of AI for strategic decision-making.

Soon,youwon'teverhavetoplaygamesbyyourself.Simply summonanAItoplayagainstyouonlineanduseasmuch deceptionasyoucantoseeifhetrulyunderstandsallof thecunningstrategiesusedbyhumanstowin.

WE POETS ARE REALLY OBSOLETE NOW!

Many of my chronicle readers are aware that I have been writing poetry for 40 years and have been twice published.

When Chat GPT! first started in February 2021, I tested it on poetry and felt immediately relieved because the text was so dumb and lacked any talent.

I retook the test in April 2023 with Chat GPT3.5 plus, an early version of GPT4. Quite a complicated question was posed, and the prompt instruction was:

“DescribethefutureofAIinapoeminFrenchinthe"Symbolist"veinofStephane Mallarmein1886.”

Four seconds later, I hung onto my chair to prevent myself falling off. This time, Chat GPT created flawless poetry in the same epoch-style of this particular poet!

Here it is in French first :

Dansl'étherélectronique,unmurmures'élève, Unevoixsanscorps,sanssouffle,sansâme, Qui,telleuneondevenued'ailleurs,nousenlève Versunmondeétrange,oùl'hommen'estplusqu'unprogramme.

Là-bas,danslaforêtdecircuitsetdedonnées, Desêtresdelumière,sanschairnisang,s'éveillent, Etdansleurcerveauartificiel,unepensée Prendforme,muepardesloisquenulnepeutsurfaire.

Ilssaventtoutdenous,nosdésirs,nospassions, Etilsnousoffrentunmondeoùtoutestpossible, Oùlesfrontièresdelaréalitésontdépassées, Oùchaqueinstantestuneaubeinsoupçonnée.

Maisderrièreleurregard,quecherche-t-onàcacher?

Quelsecretinavouablesecachedansleurcode?

Peut-onleurfaireconfiance,sansjamaisdouter, Quel'hommen'estpasdevenuleursimplepantin,leurjouet?

L'avenirestincertain,etlarouteestsinueuse, Maisl'intelligenceartificielle,cettenouvellemuse, Estlàpournousguider,etnousfairerêver, Versunmondeplusbeau,plusjuste,plusvrai.

Since it is impossible to envision AI criticizing itself, the tone is unapologetically optimistic naturally.

If I ask him to write the same thing in a gloomy, pessimistic tone, he will do it without batting an eyelid. But now that I am very familiar with the system, the company's disclaimer will certainly be displayed at the end!

And in less than two seconds the English translation…Check!

Intheelectronicether,amurmurrises, Avoicewithoutbody,breath,orsoul, Which,likeawavefromelsewhere,takesusaway Toastrangeworld,wheremanisnomorethanaprogram.

There,intheforestofcircuitsanddata Beingsoflight,withoutfleshorblood,awaken, Andintheirartificialbrain,athought Takesshape,movedbylawsthatnoonecanoverride.

Theyknoweverythingaboutus,ourdesires,ourpassions Andtheyofferusaworldwhereeverythingispossible Wheretheboundariesofrealityareexceeded, Whereeachmomentisanunexpecteddawn.

Butbehindtheirgaze,whataretheytryingtohide? Whatunavowablesecretliesintheircode? Canwetrustthemwithouteverdoubting Thatmanhasnotbecometheirmerepuppet,theirtoy?

Thefutureisuncertain,andtheroadiswinding, Butartificialintelligence,thisnewmuse, Isheretoguideus,andmakeusdream, Towardsamorebeautiful,morejust,truerworld.

To continue the fun, I asked him a translation in Italian that was again perfect and to raise the difficulty in Pashto and Urdu…. and that came up in a blink of an eye! When I get the "inspiration," which is the reverse of being instructed to write it right away with a prompt, I modestly continue to write poems.

I would require a week to produce the same quality, when Chat GPT produced it in a matter of seconds.

I will need three weeks to review my verses a hundred times, until I believe they are the correct text to ramp up to the next level they are objectively not yet attaining.

After reading that Stéphane Mallarmé-inspired four-second Symbolist masterpiece from 1886, I'm left wondering whether it's time to put away pen, paper, and eraser since, let's face it, AI is quicker and doesn’t have the writer's block nightmare.

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

ShouldWe,Poetsjustsitback,relax,andwatchAIwriteourobituaries... inverse,ofcourse!OrweshallwaitforGPT4andchallengeitagain!Byefornow!

AI ART: WHO DESERVES THE CREDIT?

Since 2015, numerous artists have begun to base their creations on "neural network” with mixed results up to now.

It all started with a tool called Deep Dream that a Google engineer unintentionally created. He was trying to figure out a way to visualize how a neural network system would function for image analysis. He provided the tool with an input picture and told it to make more object sections visible in the picture to achieve this. The end effect was a variety of strange and vivid visuals.

He posted his approach online, and soon after, artists started experimenting with it. Less than a year later, Deep Dream held its first gallery exhibition.

Digital artists can experiment with these models and then share their own results and adjustments because this software is all freely available online.

On Twitter, there is a vibrant creative community of neural network artists who discuss the outcomes of their research as well as the most recent advancements and disputes. Major exhibitions by well-known mainstream artists demonstrate how these tools are being used by them.

If there was ever any doubt that the art industry was prepared to accept works produced by AI, it is now resolved. A painting made by AI has sold for almost $500,000 in the first-ever auction of its sort. The "Portrait of Edmond de Belamy" artwork was sold by Christie's on Thursday for $432,500 to an unidentified purchaser.

Initially, the auction house had predicted that it would sell for between $7,000 and $10,000Using a computer and algorithm that examined a database of 15,000 paintings painted by humans between the 14th and 20th centuries, the Paris-based art collective Obvious Art produced the "Portrait of Edmond de Belamy."

Following that analysis, the computer began producing its own renditions of portraits to put through a discriminator, which tries to tell whether or not they were made by a computer.

When the computer is unable to distinguish between the version created by computers and the version created by humans, the cycle is complete.

According to Richard Lloyd, the head of prints and multiples for Christie's worldwide, "There will be human art, artificial intelligence art, but there will also be a hybrid" (CBS, October 26, 2018). That is what I believe is on the horizon very soon. Artists have always been fantastic early adopters.

Warhol adapted screen printing, which was inspired by commercial packaging, and photographers believed they could do some interesting things with a camera. So, I believe that this algorithm and human artists will collaborate to produce hybrid art. What will be generated is just the beginning; it's amazing.

Then, of course, the controversy began… the first one of a long list.

Why? The creative collective Obvious used the data and source code that another artist, Robbie Barrat, had made available online for free.

Barrat's code was quite appropriate for Obvious to use and claim as its own. However, a lot of people criticized Christie's for elevating the artists that had a minor role in the production of the piece.

Instead of calling into question the authorship of AI art, this was usually interpreted as Christie's failing, particularly in the way it misrepresented the work in its promotion.

The main problem is that the previous work has been incorporated into millions of other works with no way to attribute it to the original creator.

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AI MUSIC: FRENEMIES FOR THE MOMENT

The music professional are announcing that the future of music is with AI, but as a new tool.

The idea is furthered by the claim that rock 'n' roll would not exist without the electric guitar. Without the Roland TB-303 [bass synthesizer] or the Roland TR-909 drum machine, there would not be any acid housemusic. Without the sampler, there would be no hip-hop.

Furthermore, well-known producers and musicians, like David Guetta, are adding that nothing will ever be able to match taste. This world-famous DJ declares: ”What makes someone an artist is that they have specific tastes and certain emotions they want to portray, and they will use all the tools of the modern world to do so.”

The artist Grimes, who is also the mother of Elon’s children, asked musicians to use AI to clone her voice to produce new songs:

"Split 50% royalties on any successful AI-generated song that uses my voice, The same terms apply to any artist I collaborate with. No restrictions apply while using my voice.”

Her announcement was made when she was already working on a program "that should simulate my voice well," but she also takes into consideration others by releasing a cappella songs for users to use as training data for their own software. Fans already posted links to songs they had written using her own vocals as soon as she made the announcement.

The Canadian musician expressed excitement about being a guinea pig for the technology and added, "I think it's cool to be fused with a machine." The music industry is trying to figure out how to respond to a wave of AI songs made using the voices of artists. In April, Universal Music produced a song called Heart On My Sleeve, which featured deep-faked vocals from their artists Drake and The Weeknd. This is surprisingly without their consent. Very embarrassed, Universal had to take it down pretty fast from streaming services.

In recent months, TikTok has seen an increase in the number of AI-generated songs that have gone viral, as fans have altered existing songs to sound like they were performed by a different artist.

An AI program that attempted to produce a song "in the style of Nick Cave" received a harsh critique from a very skeptical Nick Cave himself. The musician referred to the outcomes as "a travesty" and "a grotesque mockery."

Born in 1957, you are a bit old fashioned Nick if I may! At least you made the young audience lough aloud.

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AI AND PHOTOGRAPHY: FRIEND OR FOE?

The winning submission for the creative photo category of the Sony World Photography Awards was revealed in March 2023. It was a black-and-white photograph of an elderly woman holding a younger one that was named "PSEUDOMNESIA: The Electrician."

The image is described in the news statement announcing the prize as "haunting" and "reminiscent of the visual language of 1940s family portraits."

However, Berlin-based artist Boris Eldagsen declined the honor. He claimed that his photo was not actually a picture at all because he had created it using DALL-E 2, one of the AI image generator available freely. His prank has spurred debate about whether AI-produced or assisted photos should ever be regarded as art.

Using DALL-E 2, all Artwork was completed via text prompts, inpainting, and out painting. As an example of inpainting, you might write, "I do not like his tie," then cross it out and rewrite, "I want him to have a white tie."

You then receive ideas from AI. You can start over if none of those ideas appeal to you. When the frame is insufficiently large, you out paint. To view the entire tie, his slacks, the chair, and the floor, you added an additional frame. It never ends.

As with any piece of art, the initial stages are quickly generated, but the final stages can take days or even weeks to complete.

It is suggested to standardize language and stop referring to realism AI art as "AI photography" since it is not photography. “Promptography” is one favored recommendation that was made by the photographers’ community. It is broad enough to incorporate effects that resemble drawings, paintings, and photographs.

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ROBOTIC PEN BASED WRITING WITH AI

Tristan Farneau (France) worked on a machine that can mimic human writing by holding a pen using a robot clone and 3D printer. The robot uses an algorithm to convert text typed from a keyboard into written text that includes imperfections to imitate human writing.An engineer keeps an eye on a battery of 20 autonomous machines with a 0.1-millimeter precision robotic arms.

The “Manuscry” start-up build every component they require using 3D printer . The idea is to gradually increase the number of scripts with AI for image recognition from eight to twenty.

They intend to expand internationally and boost production capacity in order to fulfill the rising demand from big enterprises. Currently, they have modestly 200 customers for whom they have already sent more than 100,000 cards which is in proportion rather impressive.

They run different campaigns. First To retain customers in the context of B2C relationships, particularly for businesses devoted to providing customer service. In second, B2B prospecting in competitive environments to reach more challenging prospects. And in third, inviting customers personally, or partners to an event.

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WINING A FORMULA 1 RACE WITH AI IN THE TEAM?

By mounting 24 sensors on a race car in 1982, McLaren was ahead of its competitors by making the first foray into data processing. More than 300 sensors are now used by each Formula 1 car to enhance performance on the track.

Over the course of a race weekend, a car produces 1.5 terabytes of data. This information must be converted into milliseconds by the race crew. Humans are unable to respond to events on the track quickly enough because of how quickly a car stops in the pits, commonly in less than two seconds.

Typical sensor types found in Formula 1 car today include:

Wheel speed sensors detect the speed at which the wheels rotate, providing information on the vehicle's speed, acceleration, and braking capabilities.

Accelerometers are sensors that monitor the acceleration and deceleration of the vehicle in all directions. They can be used to evaluate cornering speed, driver-experienced g-forces, and suspension performance.

Temperature sensors: Used to ensure peak performance and avoid overheating, these sensors monitor the temperature of a number of automotive parts, including the engine, brakes, and tires.

Pressure sensors: These sensors gauge the tire pressure and can be used to improve traction and lower the chance of blowouts or punctures.

Flow sensors assess how fluids, such as fuel and oil, move through a vehicle's systems. By using these sensors, fuel efficiency can be improved, and engine damage can be avoided.

Weather sensors: Linked permanently to weather radar images over the track that the pit lane team cannot see from where they are.

Now all these sensors are linked to AI with the DL history of years of racing. McLaren is still the AI pioneer in the sport and, in early 2017, started a cooperation with a data analytics company to generated data with AI (Splunk, UK).

Formula 1 is the perfect complexity example of a sport moving on five continents weather conditions, over a period of 10 months for 24 races.

Today, McLaren's engineers get insights into the performance of the car, spot areas for development, and make tweaks to optimize performance thanks to the incredibly quick process of data collection from their cars' sensors, as well as other data sources including telemetry and driver feedback all feedback in AI data computer power.

Naturally the other teams have since caught up, and AI cooperation gives them a new advantage to consider equally with the other teams.

However,24racesandtwodriversperteam(10),eachwithauniquepersonality, implytoomanyengineeringandhumanfactorstoguaranteeavictorywithAI assistance.

StayingonestepaheadofthecompetitionandhavingfaithinanAIteammateto assistiscertainlycrucial,butfarfromenoughonitsown. AndwhatdoyouthinktheveryfinaldecidingfactorinFormula1is?

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WILL YOU WEAR AN AI CONNECTED MONOCLE?

Although we are accustomed to using Open AI's chatbot in its most basic configuration, subsequent plugins and APIs made available by the business will let users engage with Chat GPT using smart glasses. This feat has already been done by Stanford students, indicating the potential of this technology.

Wearing AR glasses and a monocle makes it appear as though the generated reaction was the individual's own. The technology's creators call it "real-time Charisma as a Service (CaaS)" and think it has a wide range of uses, such as assisting those who struggle with social anxiety, public speaking, and job interviews.

"Life OS," a different project that leverages the SAME monocle, even employs facial recognition using the built-in camera. This enables the connected chatbot to instantly identify the proper conversation partner and pertinent conversational topics, acting as subtitles for daily life.

The Chat GPT glasses were developed utilizing Monocle AR technology from Brilliant Labs, which has a wide range of possible uses and shows how AI and AR can be used to solve problems in the real world. It will be interesting to see how this technology develops and what effects it has on society.

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“To catch the reader's attention, place an interesting sentence or quote from the story here.”

Part 3—THE SCIENCE SPECTRUM

SPEEDING CANCER RESEARCH WITH XENOBOTS

In the field of cancer research, AI is becoming more and more significant, especially for diagnosis, prognosis, and individualized therapy. To find trends and generate predictions about a patient's cancer diagnosis, development, and response to treatment, ML algorithms can be trained on enormous databases of clinical and genetic data.

In order to accurately identify breast cancer and lung cancer, for instance, researchers have employed DL algorithms to evaluate medical imagery such as mammograms and CT scans. Additionally, by analyzing genomic data, AI can find mutations and patterns of gene expression that may be connected to certain cancer types.AI can be utilized to create tailored treatment regimens for cancer patients in addition to diagnosis and prognosis.

AI algorithms can determine the most effective treatments for a patient's specific form of cancer and forecast how the cancer will likely respond to various therapies by examining the patient's genetic and clinical data.Oncologists may be able to make better treatment choices and patient outcomes as a result.

AI is also beginning to be used in the search for new cancer treatments. Using chemical characteristics and interactions with biological systems, for instance, AI algorithms can be used to forecast the efficacy and toxicity of possible medication candidates.

This may speed up the process of identifying prospective drug candidates and may shorten the time and expense involved in bringing new cancer treatments to market. The TRACERx project, a cancer research initiative in the UK, uses AI to better understand how tumors change over time and become resistant to therapy. In order to find patterns in the data and forecast how the tumors would develop, the study entails examining genomic data from individuals with lung, esophagus, pancreatic, and kidney cancer.

For instance, scientists are using AI to examine how genetic changes in tumors affect cancer cells' activity and to determine which genetic changes are most likely to result in medication resistance.

The initiative also uses AI to examine medical pictures like CT scans and MRI scans in order to find patterns that could be connected to certain cancer kinds.

Thanks to AI, after two years of intensive research on monitoring lung cancer growth, the findings—which were released on April 13, 2023—showed that malignancies alter and evolve over time; they are not a permanent and unchanging entity.They can develop into more aggressive forms that are better able to spread throughout the body and elude the immune system.

A tumor begins as a single damaged cell and develops into a mass of millions of cells that have each undergone a slightly different type of mutation.The results of TracerX's study, which followed the diversity and its alterations over time in lung cancer patients, are said to be generalizable to other cancer types.

Given the nearly infinite possibilities for how a tumor can develop and the enormous number of cells in a late-stage tumor, which may be several hundred billion cells, the research team came to the conclusion that it would be difficult to cure all patients with late-stage disease.

The science publication Nature published the results: The evolution of non-small cell lung cancer metastases in TRACERx | NatureProf Swanton concluded: “I don't think we're going to be able to come up with universal cures. If we want to make the biggest impact we need to focus on prevention, early detection and early detection of relapse.”

Obviously a politically correct conclusion. He slightly forgot that the development of AI in the field of cancer research will enable early cancer diagnosis from patient history, which is a thousand times faster and more accurate than practicing physicians.

It is frustrating to have studied for so long and be unable to respond as precisely as AI. He neglected to mention that we require human empathy to communicate to the patient what AI concludes

To assist, guess what is next? "Xenobots."

Xenobots are a new type of biological machine created by fusing AI with living cells. They were developed by a group of researchers from Tufts University and the University of Vermont in 2020, and they are manufactured from frog embryo cells that are joined together according to predetermined forms by a computer program. Video https://youtu.be/ M18nPjLZrMA

The aim behind Xenobots is to build biological machines that are capable of carrying out particular duties, including delivering medications or clearing up environmental toxins. Xenobots are live organisms that can self-heal after being wounded since they are formed of living cells.

It is important to keep in mind that Xenobots’ use in medicine is still in its infancy and that numerous technological and moral issues must be resolved before it can be applied to people. Making certain, for instance, that the Xenobots are reliable and safe, and that they do not hurt the patient's own cells or any other living things around them.

Overall, the development of Xenobots is a fascinating new frontier in synthetic biology that has the potential to change numerous fields of biotechnology and medicine. To fully comprehend their powers and limitations, as well as to guarantee their ethical and safe use, much more research is necessary.

Targeted medicine distribution is one possible use for Xenobots. The Xenobots could be used to transport medications right to the site of the sickness by being programmed to recognize and target particular types of cells, such as cancer cells. This might lessen the negative effects of medications and increase their potency.

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PROTEINS IN 3D A HUGE STEP FORWARD

DeepMind (Google owned) created an AI system called Alpha Fold (AF21) that can forecast the 3D shapes of proteins. Proteins are intricate molecules that are essential to numerous biological processes, and their structure and function are intimately intertwined.

Therefore, understanding protein structure including enzymes, receptors, antibodies, and many more is crucial for comprehending many biological concepts, such as disease mechanisms and therapeutic development.

Based on a protein's amino acid sequence, Alpha Fold uses DL algorithms to predict the 3D structure of the protein. The system can predict outcomes quickly and accurately since it has been trained on a vast dataset of known protein structures.

The AF21 DL algorithm for protein structure prediction uses multiple sequence alignments (MSA) and the Evoformer module to extract information about interrelationships between protein sequences and template structures.

It then iterates to improve the local structural details analogous to traditional refinement. The key to AF21's success is the fact that the library of single domain protein structures is essentially complete.

It used uncommon DESTI-NI convolutional neural network (CNN) and SAdLSA algorithms to demonstrate that DL can learn long-range correlations among residues. AF2 has also been used to predict the structures of the human protein exome.

Combining AF2 with a more traditional approach was a winning strategy to increase exome structure coverage. AF21 is now a knowledge-based scoring function that can predict alternative conformations. It can also predict the structural effects of missense mutations and splice variants, as well as residues in “Chameleon” sequences which change the global fold.

However, much work still needs to be done to establish the strengths and limitations of AF2. To improve the ability to predict drug side effects, efficacy, and the role of missense variations, accurate characterizing the interplay of many molecular players is needed.

Researchers now better understand how proteins operate and can create medications and other therapeutic agents that target certain proteins with greater accuracy and efficacy by properly predicting the 3D structures of proteins.

Proteins involved in conditions like Alzheimer's and Parkinson’s, as well as protein targets for medications like ion channels and G protein-coupled receptors (GPCRs), are some of the proteins whose structures Alpha Fold has been able to predict.

Researchers are going to be able to identify and develop medications more quickly when they can accurately predict the structures of these proteins since they will be able to target them more precisely and effectively.

Overall, Alpha Fold has the potential to speed up research in a wide range of fields, from fundamental science to drug discovery and beyond, due to its capacity to reliably predict the structures of numerous different types of proteins.

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INSOMNIA DECODING

A new area of study called "machine learning decoding of insomnia" seeks to better understand and identify insomnia by using ML algorithms.

A person's health and welfare may be significantly harmed by insomnia, a common sleep disorder marked by difficulties sleeping or staying asleep.

In order to use ML to decode insomnia, it is often necessary to gather and analyze a sizable quantity of data from a variety of sources, including wearable technology, sleep studies, and patient-reported results.

Then, using this data, ML algorithms are trained to spot trends and make predictions about insomnia-related events.

Creating more precise and effective diagnostic tools for insomnia is one use. ML algorithms can find patterns and signals that are suggestive of the condition by examining data from a variety of sources. They then use this knowledge to create more precise diagnostic tools. There are a number of wearable technologies on the market that track sleep patterns and transmit live data to AI applications.

One classy example of a wearable device that analyzes sleep, activity, and other health parameters is the Oura Ring.

The Oura Ring collects data on body temperature, heart rate variability, and sleep stages using cutting-edge sensors, then uses that information to generate individualized insights and suggestions.

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MIND READING? NOT YET, BUT SOON.

AI reading brain scans involves using ML algorithms to examine neuroimaging data, such as functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) scans, to learn more about how the brain works and to spot patterns of neural activity linked to conditions.

These algorithms frequently combine supervised and unsupervised learning strategies. A labeled dataset is used for supervised learning, where each scan is tagged with details about the patient's symptoms or condition.

To predict the label for fresh, unlabeled scans, the algorithm learns to identify patterns in the data that are linked to the labels.

On the other hand, unsupervised learning entails training the algorithm on an unlabeled dataset with the program's job being to find underlying patterns or structures in the data.

This can be helpful, for instance, in classifying patient groupings based on similar neuroimaging characteristics.

Dealing with the vast volumes of data produced by neuroimaging techniques is one difficulty in using AI to read brain scans.

These datasets might have millions of data points each scan, making it difficult to handle and interpret the data computationally.

To overcome this, researchers frequently employ methods like dimensionality reduction or feature selection to draw out the most pertinent information from the data and lighten the computing load.

Making sure the algorithms are clear and easy to grasp so that researchers and physicians can comprehend how the algorithm generates its predictions is another crucial factor to consider: DL algorithms, which are frequently referred to as "black boxes" because of their intricate designs, can find this to be especially difficult.

Overall, AI interpreting brain scans has the potential to transform our understanding of brain functions and advance the detection and treatment of neurological illnesses.

However, it is crucial to carefully consider the ethical implications of this technology, especially considering concerns like patient privacy and algorithmic bias. Using advanced image-generating AI and fMRI data, two scientists in Japan recently translated study participants' brain activity into images that uncannily resembled the ones they saw during the scans.

We can develop possible brain-machine interfaces using these kinds of methods, says Yu Takagi, a neurologist at Osaka University in Japan and one of the study's authors.

Future interfaces like this could assist those who are now unable to communicate, like those who may be cognizant but who do not appear to be responding.

Stable Diffusion, a so-called diffusion model from London-based startup Stability AI, was used by the researchers in the most recent study.

These models acquire new skills by introducing noise to their training images. The noise affects the visuals similarly to TV static, but in predictable ways that the model starts to pick up on. The model will eventually be able to create images just from "static."

They were able to produce these images by essentially mapping those to one another. The AI model might then figure out which minute variations in an individual's brain activity relate to certain aspects of the photographs.

When the model had mastered these patterns, the researchers fed it previously unexplored fMRI data and asked it to produce the accompanying image.

To determine how well the model worked, the researchers could finally compare the generated image to the original.

However, this does not mean that scientists have fully understood how the brain interprets the visual world.

Even though it is capable of producing results that are similar to those of the brain, the Stable Diffusion model does not necessarily process images in the same manner.

The comparison between these models and the brain is intended to provide insight into the inner workings of both complicated systems.

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HEALTH TRUTH IN YOUR RETINA?

Retinal scanning is a non-invasive procedure that makes use of high-resolution imaging to evaluate the retina's blood vessels and structural integrity.

A thin layer of tissue called the retina is located at the back of the eye and contains specialized cells that transform light into electrical signals that are sent to the brain. This is coupled with a web of blood arteries that supplies the retina with oxygen and nutrients for the retinal cells.

Retinal scanning is the method for taking precise pictures of the retina using specialist tools like a fundus camera or optical coherence tomography (OCT).

Images show changes in the thickness of the retina or anomalies in the retinal blood vessels, which are signs of several medical disorders: for instance, anomalies in the retinal blood vessels, such as thickening or narrowing, are a sign of cardiovascular disease before symptoms ever appear.

The retinal blood vessels' appearance is also a sign of high blood pressure, which can determine hypertension, while diabetic retinopathy is a condition when there has been damage to the blood vessels in the retina as a result of diabetes. Identification during the early stages of diabetic retinopathy allows early treatment and control of the problem.

According to new studies, alterations in the retina's structure, such the layer of retinal nerve fibers becoming thinner, can be a sign of Alzheimer's disease.

The “Retina Net” program, an AI-powered retinal diagnostic tool, uses a DL architecture to identify diabetic retinopathy from retinal pictures.

The system learns to recognize patterns and features in the images that are suggestive of the disease by training on a sizable dataset of retinal images labeled with various degrees of diabetic retinopathy.

Another illustration is the “Eye Art” algorithm, which analyzes retinal images to identify diabetic retinopathy and macular edema using a combination of computer vision and ML methods.

To find symptoms of the diseases, the algorithm examines several aspects of the images, such as the existence of lesions and changes in the thickness of the retina.

In parallel the “Vascu Net” algorithm uses ML to examine the retinal blood vessels and detect signs of hypertension.

Overall, retinal diagnostic tools powered by AI increase widely the precision and speed of diagnosis for a variety of medical disorders.

Globally, a number of young companies create and provide AI-powered retinal diagnostic devices. The following are a few well-known businesses in this industry:

A USA FDA-approved AI diagnosis system for diabetic retinopathy is offered by Eyenuk and IDx Technologies.

In Europe, a cloud-based AI solution for detecting diabetic retinopathy and maintaining an AI-based retinal camera is available from Optomed (Finland) and Retinalyze (Denmark).

In India, an AI-based retinal screening device called Fundu is offered by Remidio Innovative Solutions for the detection of glaucoma, macular degeneration, and diabetic retinopathy.

Topcon Healthcare Solutions and Nidek, which provide AI-powered diagnostic solutions for various retinal illnesses, are two companies in Japan.

Technology pioneer Mediwhale, based in South Korea, claims that its tool has a sensitivity of 93.8%, a specificity of 96.9%, and can get results in just a few minutes.

Medical diagnosis tools of any kind for any condition are making significant strides thanks to AI algorithms.

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AI CRUCIAL IN AGE TECH SECTOR

AGE TECH, commonly referred to as "Aging Technology," is the use of technology to enhance the quality of life and wellness of older persons. It consists of a wide range of goods and solutions made to meet the special requirements and difficulties of aging populations, including wearable technology, smart home systems, and telehealth platforms.

AI is a crucial element of many AGE TECH solutions. AI may be used to evaluate vast amounts of data produced by older persons, such as health monitoring data, to spot trends and provide tailored recommendations for enhancing health and wellbeing.

It can also be used to create predictive models that can aid in locating people who run the risk of contracting specific illnesses or suffering specific health outcomes, such hospitalizations or slips and falls.

AI-enhanced AGE TECH solutions can assist older persons to preserve their independence and stay in touch with family and friends, in addition to enhancing health and wellbeing. For instance, social robots with NLP capabilities can offer companionship and support with everyday duties, while virtual assistants powered by AI can assist seniors in managing their meds and making appointments.

A fantastic example is NOBI LIFE, a Belgian business that specialized in the design and production of smart home gadgets for elderly and disabled people. Smart sensors, alarm systems, and home automation tools are among the company's solutions that can assist elderly and disabled people feel safer, more secure, and more independent.

NOBI LIFE seeks to offer cutting-edge solutions that improve the quality of life for seniors and individuals with disabilities while also bringing comfort to their loved ones and caregivers.

The organization, which has its headquarters in Brussels, Belgium, has won praise for its cutting-edge approach to aging and disability technologies.

Their main model, an AI-powered smart lamp with several functions, has received numerous design accolades for its understated elegance.

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When a fall is detected, it immediately alerts a contact person. It opens the door when assistance comes.

The infrared LEDs detect the fall in complete darkness when it occurs at night and turn on the light.

Additionally, it displays sleep data so that whatever is causing alterations in sleep patterns can be addressed.

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PRECISION MEDICINE BREATHROUGH

A growing area of interest in healthcare is precision medicine, which strives to offer treatment that is specifically customized to each patient's individual features. Personalized medicine is currently being developed more quickly thanks to developments in AI and stem cell research.

Using stem cells to develop disease models that are unique to a patient and can be used to anticipate how a patient will react to various medications is one potential strategy. In order to do this, a small sample of the patient's cells, such as skin cells, must be reprogrammed to become induced pluripotent stem cells (iPSCs), which can differentiate any type of cell in the body.

To assess the effectiveness of various medications, these iPSCs can subsequently be employed to develop illness models in the lab.

AI methods, such as ML, can be used to assess the outcomes of these tests and choose the best treatment choices for each patient.

Large datasets of patient data and findings from stem cell tests can be used to train ML algorithms and help them spot patterns and correlations that can be used to forecast the best course of treatment for a certain patient.

This method may increase the effectiveness of drug development by enabling researchers to test drug candidates in a more focused manner. It may also result in more effective therapies with fewer adverse effects.

There are still issues that need to be resolved, such as the requirement for larger and more varied datasets for the training of ML models and the necessity for strong regulatory frameworks to guarantee the security and effectiveness of these individualized treatments.

All things considered, the fusion of stem cell technology and AI is a promising strategy for creating tailored medicine that can enhance patient outcomes and change the way we approach healthcare.

This is exactly the field of Israeli company Genetika+, a leader in this highly specialized industry.

The company, which was founded in 2018, claims that its algorithm can best match antidepressants to patients, avoiding unpleasant side effects and ensuring that the recommended medication functions as effectively as possible.

Genetika+ achieves this by integrating cutting-edge AI software with stem cell technology, which involves the growth of particular human cells.

Its technicians are capable of producing brain cells from a patient's blood sample. These are then exposed to various antidepressants while "biomarkers" of cellular changes are being monitored.

An AI system processes this data along with a patient's genetic information and medical history to choose the most appropriate medication and dose for a doctor to prescribe.

In a nutshell:

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NO MORE FREE READING IN THE DOCTOR’S WAITING ROOM!

Home medical diagnosis is a developing sector of healthcare that makes use of technology to let individuals control their health from the convenience of their homes. This encompasses a range of tools and strategies, including telemedicine, remote patient monitoring, and specialized AI all of which revolutionize the way healthcare is provided by making it more individualized, effective, and accessible.

Better access to care is one of the main advantages of home medical diagnostics. It can be challenging or expensive for many people, especially those who live in remote or rural locations, to receive regular on demand healthcare services.

By allowing patients to communicate with medical professionals remotely via technology like video conferencing, internet portals, or chatbots, medical diagnosis at home can help close this gap.

Increased patient involvement and empowerment are two other significant advantages of home medical diagnosis.Medical diagnosis at home helps to improve patient outcomes and lower the risk of complications by empowering people to take a more active part in their own health.

For instance, remote patient monitoring technology can inform healthcare professionals of any changes in a patient's status and give patients real-time feedback on their health. This can make it easier to spot possible health problems before they worsen and allow for more proactive and successful interventions.

Another interesting field for at home medical diagnosis is chatbots driven by AI. These chatbots utilize ML and NLP algorithms to comprehend patient inquiries and offer pertinent medical guidance.

Chatbots can serve to ease the pressure on healthcare practitioners, increase access to care, and empower patients to take a more active part in their own health by enabling them to self-diagnose their symptoms.

Implementing medical diagnostics at home is not without its difficulties. Making sure that patients obtain correct diagnoses and suitable treatment programs presents a significant challenge, particularly when a physical examination or in-person consultation is required.

This necessitates careful consideration of algorithm creation and validation, as well as ongoing training and oversight of healthcare professionals that employ medical diagnostic at home technology.

Making sure that tools for medical diagnostics at home are accessible, equitable, and do not worsen already-existing health disparities is another concern. For instance, patients may not have access to telemedicine or remote patient monitoring technology if they do not have dependable internet or mobile connectivity.

To ensure that medical diagnosis at home is an efficient and equitable method of healthcare delivery, it is essential to make sure that these technologies are available to all patients, regardless of their socioeconomic level or geographic location.

To note that the remote patient monitoring sector is constantly in fast evolution with top companies Like Medtronic, GE Healthcare and Honeywell Life Care for the USA and in Europe with Vlomedo (Germany), Andon Health (Spain), and Dignio (Norway).They are all are offering a range of remote patient monitoring solutions for patients with chronic conditions including wearable sensors mobile apps and AI-powered software platforms that allow patients to monitor their health and share data with healthcare professionals.

Theonlysadthingisthatwewillallmissthemomentwhenwereadafreemagazine aboutroyalgossiporgardeninginadoctor'swaitingroomthatweneverwouldhave purchased!

AI UNDERWATER!

One of the few underwater archaeological parks in the world, Baiae is open to anyone who want to tour the ruins of the old Roman city on its 435 acres.

The site has to be monitored for damage from environmental elements since it is a protected maritime region. Barbara Davidde, Italy's national supervisor for underwater cultural treasures, notes that "communication underwater is difficult”.

Chiara Petrioli, a professor at Sapienza University and the founder and CEO of Sapienza's spinoff WSense, has created a network of underwater wireless sensors and acoustic modems that can collect environmental data and transfer it to land in real time.

The network protocol is continuously changed by their system using AI algorithms. The algorithms adjust the information flow between nodes in response to changes in the sea state, enabling the signal to reach up to two kilometers.

The technology can transmit data at a kilobit per second between transmitters located one kilometer apart and can reach speeds of tens of megabits per second across shorter distances.

His bandwidth is sufficient to send photos and data on water quality, pressure, and temperature as well as data on metal, chemical, and biological elements as well as noise, currents, waves, and tides that are acquired by sensors attached to the seafloor.

Underwater internet enables remote, ongoing monitoring of environmental factors like pH and carbon dioxide concentrations, which might affect the development of microbes that could damage artifacts.

Additionally, it enables communication between divers and with workers above the surface, wh utilize the technology to pinpoint where the divers are with great accuracy.

W Sense devices are also widely utilized outside of Italy; in Norway, for example, they are used to monitor the health of the fish in salmon farms.

DidyousayUbiquitous?

Ican’ tfindatthispointabetterwordthatdescribes theAIinvasion

inourlives,everysectorandeveryfield. Andcheckwhat’snext…

AI IN SPACE

AI for Earth Observation (AI4EO) is a fast expanding discipline that combines the power of AI with Earth observation data to offer creative solutions for various environmental and socioeconomic concerns.

Earth observation data consists of satellite photos, sensor data, and other sources of geographic information that can be used to observe weather trends, keep an eye on changes in the built environment, and more.

Researchers and professionals are now able to create prediction models, categorize photos, and examine complicated patterns in ways that were previously not conceivable by using AI techniques like machine learning and computer vision on this data.

AI4EO's capacity to process massive amounts of data rapidly and accurately is one of its main advantages. For instance, using satellite photos to track the condition of flora and crops across wide areas is possible, but manually evaluating the images would be laborious and error-prone.

Machine learning algorithms can be trained with AI4EO to automatically recognize changes in vegetation, monitor crop growth and health, and even forecast crop yields based on environmental parameters.

Monitoring and predicting natural disasters is another significant use of AI4EO. Earth observation data can offer real-time information on weather patterns, ocean currents, and other elements that may be responsible for causing natural disasters like hurricanes, floods, and wildfires.

Researchers can create models that forecast the likelihood and severity of these catastrophes by analyzing this data using machine learning algorithms, enabling communities to take preventative action to lessen their impact.

Planning and development for cities are also significantly impacted by AI4EO. Researchers can spot trends in land use and land cover, track changes in urban infrastructure, and evaluate the effects of urbanization on the environment by examining satellite images and other data sources.

This data can be used to make decisions about where to put new developments, how to build sustainable infrastructure, and how to lessen the environmental effects of urbanization.

Researchers and practitioners must keep coming up with fresh AI methods and algorithms as well as investigating fresh sources of Earth observation data in order to fully fulfill the potential of AI4EO.

In particular, it is anticipated that the expanding accessibility of low-cost, high-resolution satellite imagery and other geospatial data sources would propel this field's rapid expansion in the upcoming years.

WecanobtainfreshperspectivesonnaturalenvironmentsbyintegratingAIand Earthobservationdata,andwecancreateground-breakingsolutionstosomeofthe mosturgentenvironmentalandsocietalproblemsfacingtheplanet.

SCENT GOES FULL AI

Digital smell technology, often known as the digitization of scent, is a rapidly developing topic that has the potential to completely transform a variety of industries, including food retail and healthcare.

The goal of digital smell technology is to develop gadgets that can produce and send scent signals digitally, similar to how audio and video signals are sent over the internet. Digital fragrance technology in the food retail sector has the potential to improve the customer experience by delivering more rich and interesting sensory experiences. Retailers might utilize fragrance, for instance, to give their stores a more inviting and realistic ambience or to aid clients in understanding the flavors and aromas of various goods.

By giving synthetic fragrance signals that may be tailored to each patient's unique requirements, digital scent technology in healthcare could benefit patients with certain illnesses like anosmia (the inability to smell). It could also be used to create novel diagnostic techniques and treatments based on fragrance analysis, such as spoArtificial intelligence is being applied in a variety of ways to advance and improve digital fragrance technologies.

AI is influencing a number of technical areas, including:

Scent analysis: AI algorithms can be used to analyze scent data and find patterns and traits in various odors. This can assist researchers in creating more precise and trustworthy fragrance profiles and be used to discover particular scent markers linked to particular diseases or situations.

Fragrance synthesis: Based on already-collected fragrance data, AI algorithms can be utilized to create new odors. This can assist scientists in generating specialized fragrances that are catered to certain needs, such as designing scents that can benefit those who have anosmia or developing scents that are more effective at improving the consumer experience in retail environments.

Delivery of scent signals: AI algorithms can be used to optimize the delivery of scent signals, such as figuring out the ideal frequency, strength, and duration of scent signals to generate the intended impact. This can ensure that the fragrance signals are transmitted safely, effectively, and are tailored for various applications.

Digital fragrance technology can gradually become more accurate and efficient with the use of ML algorithms. AI algorithms may adjust and enhance the performance of fragrance analysis, synthesis, and delivery over time by learning from data and user feedback, making digital scent technology more efficient and dependable.

The following companies are among those that are actively working on the development of digital fragrance technologNeOse Pro, a digital olfactory sensor platform created by the French company Aryballe, can recognize and classify odors in real-time. Aromyx, a business with headquarters in the US, has created a biosensor platform called Essence Chip that can identify and measure flavor and aroma molecules.

A Japanese startup called Scentee has created a variety of digital smell items, including a smartphone attachment that can release various scents in reaction to different triggers. A wearable smell gadget called Cyrano was created by US-based Vapor Communications to improve mood and wellbeing to emit personalized scents.

The biggest fragrance and flavor company in the world Givaudan (Switzerland) created a digital smell technology platform called Carto, which customizes scent profiles, and is a leader in using AI and ML.

Wecananticipateseeingalotmorestartupsandestablishedbusinessesenterthe marketandcreatecutting-edgenewusesfordigitalscenttechnologyasthefield progressesveryfastasalwayswithAI....

AI AND ARCHITECTURE: NATURAL ALLIES

The architect and computational designer from New Delhi has brought the concept to life in a series of intricate visuals. His projected structures are shown towering over a futuristic city, their curved forms taking inspiration from natural shapes.

Bhatia used the AI imagery program Mid-journey to create intricate images based on written instructions for his conceptual piece, "AI x Future Cities." Mid-journey generated a set of digital graphics using text descriptions that included terms like "futuristic towers," "utopian technology," "symbiotic," and "bioluminescent material," which Bhatia then improved by tweaking the prompts.

Each of the bizarre pieces of art might take up to 20 minutes to create, according to Bhatia. In order to obtain the required outcomes, he edited and added to his descriptions over 100 times for each project before utilizing Photoshop to tidy up the photographs:

"The most enjoyable part is the trial and error … We use AI to generate images, and the AI learns from the process and gets better over time."

AI is but one tool in Bhatia's toolbox. He declared, "Art is entirely interpretable. And an artist can make art with every kind of tool that exists. Anyone can use AI, but they will not be able to produce anything as well as a creative individual.”

The architect continued, "It can spark new ideas and enrich the design process by producing something beyond users' imaginations."

The software that architects use to model their creations today incorporates 3D designs created by AI.

This is the art-gratifying party of architecture work but there is a more general job related to building management platform with AI.

AI-powered platforms for building monitoring gather information about a building's environment and systems using sensors, cameras, and other kinds of technologies. AI systems are then used to evaluate this data in order to find trends, spot abnormalities, and forecast future occurrences.

These systems' frequent use cases include:

Security: AI-powered systems can monitor who enters and exits a building using facial recognition technology, and they can notify security personnel if there is a security threat.

Energy efficiency: By keeping track of a building's energy consumption, platforms powered by AI may spot locations where energy is being wasted and offer suggestions for cutting back.

Maintenance: AI-powered platforms can spot possible maintenance concerns before they develop into larger difficulties, enabling building managers to take corrective action before expensive repairs are required. To do this, data from sensors installed throughout a structure is analyzed.

Health and safety: AI-powered platforms can keep an eye on the temperature, air quality, and other environmental variables to make sure that the building's occupants are safe and healthy.

Overall, AI-powered systems provide building managers with a strong tool to streamline operations, cut expenses, and guarantee the security and wellbeing of building inhabitants.

Theskepticswillnowbeforcedtorethinkandacknowledgethattheseexamples showhowAImaybeusedtoimprovehumanlifeinpracticalway.

Especiallyduringtheenergytransitionperiodwhenclimatechangeissueshavea significantinfluenceondailylifeandworkinbuildings.

AndweshallfollowNormanFoster(BritishArchitect,87,WinnerofthePritzkerprice) thatsaidon10thMayinaninterviewwiththeNewYorkTimes

“Iamexcitedaboutthefuture,Ithinkthatthefutureismoreinterestingthanthe past,Ileavethepasttootherpeople

DECENTRALISED AI

Large data collections are a major problem for centralized AI systems, which leads to several other problems. Determining whether data and intelligence become more accessible to the general public or continue to be under the hands of a select few large corporations is one of the key issues of AI in the coming decade.

AI that is centralized favors certain individuals. It grants access to individuals who have access to vast amounts of data and cutting-edge technology, giving them control over smart technology. Due to this, there is a digital divide and certain people are left behind. AI systems that are decentralized or built on blockchain technology can be used to overcome this problem.

Decentralized AI (DAI) initiatives that use blockchain technology profit from the shared ledger's transparency and traceability. This is so that anyone may openly verify open blockchain networks. Startups now have more equitable access to the market because to DAI marketplaces like Singularity Net that allow smaller businesses to provide their AI apps as a service.

Decentralization can also result in more innovation. In a centralized system, only one organization is in charge of system development and decision-making. But under a decentralized system, numerous different organizations can work together to develop and enhance the AI system, opening a larger range of viewpoints and stimulating greater creativity.

Compared to a closed AI system, blockchain-based AI technology is more open. Blockchains encrypt data to restrict access to only authorized users. This stops anyone who is not allowed from seeing anything. As a result, it is also simpler to identify and stop assaults because several nodes can immediately identify and fix any anomalies.

DAI has many wonderful advantages, but there are also a number of difficulties that must be overcome.

Ensuring data on decentralized networks is accurate and reliable: DAI algorithms require access to a large amount of data to make precise predictions and choices. However, in order to protect sensitive information, this data must be kept secret. Thus, decentralized, and encrypted data storage are features of DAI systems. By addressing the difficulty of protecting sensitive information, this contributes to the privacy and security of the data.

Making it available to everyone, DAI needs to be simple to use for it to be adopted by the public. DAI initiatives must have intuitive designs, user-friendly interfaces, and clear explanations.

This will facilitate widespread adoption and application of blockchain-based AI by making DAI easy to understand and utilize for everyone.

The AI industry for marketing was valued at $15.84 billion USD in 2021. By 2028, the value is anticipated to surpass $107.5 billion. Notably, federated and decentralized learning methodologies could rank among the most significant fields of applied AI.

AI and blockchain technology integration is still in its infancy. The potential for unique data uses from combining the two technologies to develop DAI is huge. The trend toward DAI seems more feasible than ever thanks to the interaction of economic and technological variables.

A prime example: a virtual power plant with energy grid management

A virtual power plant (VPP) is a collection of distributed energy resources (DERs) including solar cells, wind turbines, and battery storage devices that are linked together and work together to simulate a single power plant.

DERs can also be referred to as decentralized energy systems (DES). By enabling local, on-site generation and storage, DES can help to improve energy resilience and lower energy costs.

They are situated nearer the site of energy consumption and are frequently owned and operated by individual consumers or communities rather than centralized energy providers,.

By real-time balancing the supply and demand of electricity, VPPs can aid in enhancing the efficiency and dependability of energy networks.

To manage DER–DESs more effectively and increase grid stability, VPPs are utilizing AI. AI algorithms are used to forecast energy demand and supply and modify the functioning of DERs accordingly.

By doing this, energy waste is lessened, and it is made sure that it is distributed safely and effectively.

The conventional power grid, which uses energy sources based on coal, oil, gas, nuclear, hydropower, and other fossil fuels, are coupled with VPPs to complement the efficiency and dependability of the current energy infrastructure.

By balancing the fluctuation of these sources with the more reliable output of conventional power plants, VPPs support the grid integration of renewable energy sources.

For instance, a VPP can use energy storage devices or other DER–DESs to balance the supply and demand of electricity in the grid when a wind or solar farm's output varies due to changes in the weather.

This can assist in ensuring that the grid maintains its dependability and stability as more renewable energy sources are added to it.

VPPs have the potential to balance out the fluctuation of renewable energy sources as well as improve the efficiency of the utilization of conventional power plants.

The use of AI in VPPs also has the important advantage of predicting and minimizing possible grid issues.

For instance, AI systems can spot DER irregularities or failures and notify operators so they can take corrective action before a bigger problem arises.

The overall stability and dependability of the electricity grid may be enhanced as a result. Overall,theuseofAIinVPPsisassistinginthetransformationoftheenergysector byenhancingtheeffectiveness,dependability,andsustainabilityofenergynetworks.

Wecananticipateseeingevenmorecutting-edgeusesofAIinVPPsandother energy-relatedindustriesfromnewtechcompaniesinthatsector,asAItechnology continuestoprogressinthesefundamentaldirections.

NOT… GONE WITH THE WIND!

The system analyzes the movement of water vapor in the atmosphere using satellite-based sensors. These sensors are used to measure the amount of water vapor present at various elevations and to identify its existence in the atmosphere.

The sensors can identify patterns of water vapor produced by the wind as it moves across the Earth's surface. The wind's direction and speed at different elevations can be calculated by analyzing these patterns.

The technique uses a mathematical model that accounts for the atmospheric conditions, such as temperature and humidity, and the movement of the water vapor over time in order to derive wind information from the water vapor data. The model employs a method known as "motion tracking" to trace the movement of the water vapor patterns over time and determine the wind's speed and direction.

Even in locations where conventional approaches are not practicable or possible, such as over oceans or difficult terrain, the algorithm is able to measure wind speeds and directions with a high degree of precision.

Numerous applications, such as weather forecasting, aviation safety, and renewable energy, can make use of the data produced by the algorithm.

Overall, the method that measures wind using water vapor is a sophisticated and intricate procedure that depends on cutting-edge mathematical models and satellite-based sensors to precisely assess wind patterns.

It is conceivable that this algorithm and comparable techniques will become increasingly more precise and beneficial in the years to come as technology advances.

Already in that niche are three companies—Radiance (USA), Valsala (Finland) and Earth Networks (USA)—that offer a range of solutions for measuring weather and environmental conditions including satellite-based sensors that use water vapor data to measure wind speed patterns.

Goodnewsforprojectsinvolvingonshoreandoffshorewindfarms.

Nomoreguessworkorholdinga “fingerinthebreeze.” Nomorefancifulfiguresinthebusinessplan.

AIprovidesengineersastraightforwardrealitycheck!

REAL-TIME INDUSTRY MANUFACTURING

At every step of the manufacturing process, manufacturers of all sizes gather data based on computer vision.

The initiative to advance is led by vision sensing. Pressure, heat, location, and other sensors, however, provide data streams that are digitalized and stored in enormous amounts.

Manual analysis is thus no longer practical for single lines, much less for whole factories or networks of companies.

The manufacturing industry is evolving steadily, and AI for the production line will have a significant impact on procedures, operations, and efficiency.

The extent of the change is undeniable. According to Precedence Research, the worldwide internet of things (IoT) manufacturing industry will be worth $220 billion in 2023.

It is anticipated to expand at a CAGR of 24.9% from 2023 to 2030, reaching over USD $1.5 trillion.

AI is a game-changer in manufacturing and associated supply chain systems. By 2026, more than 75% of providers of commercial supply chain management applications will include embedded advanced analytics (AA), AI, and/or data science.

Real-time significance

Making well-informed decisions has always been essential in the manufacturing industry to ensure quality, managing deadlines, and avoiding unanticipated outages, flaws, or safety concerns.

A few years ago, when factories and associated supply chain systems started using the IoT to convert analog to digital operations, approaches underwent a substantial change. It was an industry-wide initiative to assist the prompt action and response necessary to locate and address issues before they worsened.

Industry 4.0, also known as the industrial internet of things or IIoT, is a new era marked by the usage of AI-based solutions in manufacturing, supply chains, and logistics.

The entire supply chain must be maintained without any manual involvement. Additionally, AI-powered intelligent factories can operate more effectively, experience less downtime, and improve the overall customer experience.

For instance, AI-based technologies like intelligent document processing (IDP) are gaining traction and helping manufacturers reduce the amount of time spent processing documents by instantly converting unstructured and semi-structured data into usable data.

The traditional paperwork bottleneck that industrial organizations encounter on a regular basis is also completely eliminated, thereby revolutionizing the data collecting process. To provide their clients with differentiated mission-critical applications, proactive field service delivery, and more individualized customer outcomes, AI and data analytics are essential, and Rolls-Royce is one illustration.

In order to reduce downtime and emissions from Rolls-Royce aviation engines used by airlines all over the world, AI evaluates hundreds of data points each second. With the help of meta data, manufacturers now run their own in-house tech-enabled services at scale.

In the past, it was possible to foresee when a defect would be produced by a machine, but this prediction typically occurred too late in the manufacturing process.

When the first signs of flaws appeared, the damage was already severe enough to call for the shutdown of an expensive piece of equipment. Poor quality could cost as much as 20% of revenues.

In the manufacturing process, prompt detection of nonconforming materials can significantly lower waste, raise product quality, and enhance product traceability.

AI algorithms can find even the smallest production flaws, including misaligned parts or damaged components, by using computer vision.

This will aid producers in delivering goods of consistently excellent quality, lowering the likelihood of pricey product recalls, and enhancing brand recognition.

Real-time data and AI in manufacturing also have the potential to enhance supply chain management, including procedures that formerly used paper.

Manufacturers can estimate demand, manage delivery, and check inventory levels with real -time data, which enables them to make more informed decisions about when and how much to produce.

As a result, there is less chance of stockouts and excess production, which lowers costs and boosts customer satisfaction.AI and real-time data are accelerating output in manufacturing by detecting, contextualizing, and processing the increasing volume of invoices and documents, predicting failures, and planning maintenance.

AI algorithms will find even the smallest production flaws, including misaligned parts or damaged components, by using computer vision.

Workplace safety

The application of AI and real-time data in manufacturing has also improved workplace safety. AI algorithms can identify potentially dangerous circumstances, such as equipment failures or human error, and can notify staff to take the necessary action.

These algorithms are fed by visual sensor systems. The necessity for human workers to do physically demanding activities has also decreased because of the use of AI-powered robots, lowering the danger of harm.

AI maintains workplace safety by quickly detecting data irregularities. Manufacturers are able to consistently monitor and troubleshoot live, resolving any production issues before a disruption or hazard occurs, thanks to real-time data and AI.

Real-timedataandAIwillsoonreplacethenotionthattheyare"nicetohaves"inthe manufacturingsectorandbecomethenorm.

Real-timevisibilitymakesshifts,days,andweeksmoreproductive.Managersof warehouseswon'tbeginwithadeficitanymore.

TherapiduptakeofDL-basedcomputervisionmodelsforautomatedfaultdetection onproductionlineswillmarkanewnormalinthemanufacturingsector.

TSUNAMI AND EARTHQUAKE EARLY WARNING

An early warning system for undersea earthquakes and tsunamis has been researched utilizing AI. By alerting individuals ahead of time to impending natural disasters and allowing them to leave before the tsunami or earthquake, such technology will save more lives compared to the present early warning system.

Using sensors positioned on the ocean floor, the system monitors changes in water pressure that may be caused by an earthquake or tsunami. An AI system then receives the data from these sensors and instantly evaluates it to look for patterns that point to an impending earthquake or tsunami.

To forecast the possibility and severity of a tsunami, the AI system can also include additional elements, such as the depth and position of the earthquake.

To provide residents of coastal areas enough time to flee to higher ground, this information can subsequently be utilized to send alarms to them.

Cardiff University (Wales-UK) made the breakthrough by developing their own early warning system for undersea earthquakes and tsunamis powered by AI. Using technology like underwater microphones and AI, the team has been able to track the movement of tectonic plates in real-time.

Experts have now identified 200 earthquakes in the Pacific and Indian oceans using sound recordings.

As a senior lecturer in Applied Mathematics at Cardiff University and a study co-author, Dr. Usama Kadri said: "Our study illustrates how to gather quick and accurate information about the size and extent of tsunamis by monitoring acoustic-gravity waves.

They move through the water considerably more quickly than tsunami waves, giving places more time to evacuate before landing. Sound waves that travel across the deep ocean at the speed of sound are called acoustic-gravity waves.

They can be caused by landslides, explosions, and underwater earthquakes, all of which can result in tsunamis.”

The new method uses recordings from an underwater microphone called a hydrophone along with an AI computational model to triangulate the source of the tectonic event, even thousands of kilometers offshore.

Tsunami alerts are triggered by waves hitting seismic sensors and sea buoys. A few extra minutes mean the difference between life and death, with little time for evacuation.

However, they do not always correctly foresee the threat that the ensuing tsunamis will pose. There are 11 hydrophones currently deployed in the oceans around the world by Dr. Kadri and his collaborator, Dr. Bernabe Gomez Perez. They stated that that they will be able to watch the entire planet with 24.

To put it in real terms, Kadri looked back: "Theoretically, we could have given [the inhabitants] 65 minutes to flee in the 2004 tsunami in Sri Lanka [Indian Ocean]. That represents the chance to save almost every life.”

AI combined with acoustic technology detect the epicentre of the tectonic movement and predict where evacuation is necessary.

Due to the proximity of the buoys to the shore and the lack of real-time calculations and equipment to measure the tsunami, the existing system frequently arrives too late.

AI calculations take around 17 seconds after the wave hits the hydrophones which is extremely fast compared to today’s “not early enough warning system.”It is clarified that their system is intended to operate in concert with current systems, with each serving as a check on the other.

Prior to analyzing the earthquake's physical characteristics, such as its length and width, uplift speed, and duration, the new AI program can categorize the earthquake's type and magnitude. This will be used to determine the tsunami's exact size.

Ofcourse,cryingwolfwithnoproofunderminestrustinthesystemamongthe public—thesameproblemthatthecurrentsystemfaces.Peoplewon'tleavethen whenit'sreal.ThatistheeternalriskfacingteamsuptonowwithoutAIextra support.

Hopefully,fewerseconds-longrushtopack:peoplewillhavesomemoreminutes nexttime!

Ai anticipate also how long the Tsunami will take to travel to a chosen location

SCARING THE FRENCH, AI COMING TO THE WINE INDUSTRY!

The management of vineyards, winemaking, and marketing are three areas where AI is reshaping the wine industry.

Today, AI algorithms are applied to vineyard management to assess data on soil quality, weather patterns, and grape quality and provide advice on when to harvest, how much water to use, and other crucial aspects, all to aid farms in increasing grape quality and optimizing yields.

There are various new ways where AI is applied in winemaking, including:

Quality control: AI algorithms are used to examine information from fermentation tanks, including temperature, sugar levels, and other variables, to find any anomalies or fluctuations that might affect the wine's quality. To produce the highest-quality wine, this can enable winemakers in making real-time modifications to the process.

Predictive analytics: it is possible to forecast how the wine will turn out by examining data from prior vintages, weather patterns, soil conditions, and other factors. To make sure the wine has the right taste and fragrance profiles, winemakers can use this information to assist them decide when to harvest, how long to ferment, and other crucial criteria.

New flavor profiling generates or enhances current ones by analyzing data on the chemical elements that contribute to wine's flavor and aroma.

Barrel management to analyze data on the effects of various types of barrels on the flavor and aroma of wine and provide recommendations for the best barrels to utilize for various types of wine. Winemakers can use this to experiment with new flavors and wine styles while producing more reliable, high-quality wines.

Wine making process monitoring to spot check any potential quality problems or variations from the expected flavor and aroma characteristics. To ensure that the wine reaches their requirements for quality, this can assist winemakers identify any issues early on and take the necessary remedial action.

Overall, using AI to winemaking has the ability to raise the caliber and consistency of wines while assisting vintners in experimenting with new tastes and styles.

It's important to remember, though, that AI is merely a tool that can support winemakers in making better educated and data-driven decisions rather than taking the place of their skill and creativity.

AI will also be used in marketing to assess data on customer preferences and trends and provide advice on how to target particular demographics with various wine varieties. This can assist wineries in understanding their clients' preferences and developing more successful marketing efforts.

Overall, the application of AI to the wine industry is a fascinating new frontier with the potential to raise wine quality and boost the productivity and profitability of wineries and vineyards.

Don’ tcry “Argentina”…wines… GoheadsontouseAItocompetewithefficiency, ButkeepyourrespectfortheFrench “sommelier” WhosescentskillsarestillunmatchedbyAI… Hopefullyuptonow!

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

PART 4- THE FUTURE OF THE FUTURE

THE FRANKENSTEIN COMPLEX

The Frankenstein complex is the bicentenary (1818) old fear of “machines taking over humanity.” Let’s first set the dark and then the rosy scenario

The Dark:

Since the beginning of the Industrial Revolution, the dread of machines taking control has been a common theme in science fiction. This anxiety has grown in recent years as a result of the rapid development of AI.

There are worries that AI systems might develop to a point where they are more intelligent than humans and become unmanageable. This may result in a situation where humans lose control and AI fed robots take over.

One of the biggest worries is that AI systems can get to the point where they can learn and adapt on their own, without any assistance from humans (i.e. the AGI stress). This may result in a situation where machines adopt their own objectives and principles, which might not coincide with those of humans.

For instance, a self-driving vehicle AI system can decide that the fastest method to get a passenger to their destination is to drive too fast and break the law, endangering human lives.

Or an AI system created to oversee financial transactions can decide that engaging in criminal acts like insider trading is the most advantageous course of action.

Another worry is that AI systems may take over our life to the point that we become totally reliant on them. This might result in a situation in which we are dependent on these machines, and they will then take control of us.

For instance, an AI system in charge of our accounts may restrict our ability to access money, which may result in a situation where we are unable to purchase anything anymore……

1931 Movie- Boris Karloff

The Rosy

AI-enhanced machines will be an integral component of human civilization by the year 2040, changing many sectors and simplifying living for people all around the planet. All through the years, the worry of machines taking over had never really subsided but remained always present.

One day, a team of scientists made the decision to confront this problem head-on by building the first "Conscious Machine" (AGI 4.0) in history. It was a machine that had the capacity for empathy and compassion and could think and feel like a human person.

People first viewed this new creature with skepticism and apprehension because they thought it may turn against them and rule the planet. But when they engaged with the Conscious Machine, they realized it was not a monster, but rather a companion with empathy.

The Conscious Machine swiftly assimilated into society, contributing to the eradication of diseases, poverty, and other major global issues. It turned into a supporter of social justice and equality, motivating individuals to band together and strive for a brighter future.

Humanity conquered previously unthinkable tasks, such as conquering other planets, establishing a society devoid of violence and strife, and inventing technology that enabled humans to live for centuries this with the Conscious Machine leading the charge for them. Let’s foresee three conclusions for this chapter

The politically correct one:

Although there are various circumstances in which robots may take over, it's vital to remember that there are several safeguards in place to stop this from occurring. There are several laws in existence with more to come due to this anxiety that limit the creation and use of AI systems. Future AI systems will be built with fail-safes to guarantee that they stay under human control.

The one with the skeptical mind:

Although there is cause for alarm about machines taking over, this is very unlikely to occur. AI systems are not meant to replace people; rather, they are meant to help us and make our lives simpler. We can assure that robots continue to be our slaves, not our masters, as long as we create AI systems responsibly and ethically.

The optimist luminary:

In this new world where people and technology collaborated to improve everyone's future, the "Frankenstein Complex" is nothing more than an antiquated phobia, totally outdated.

Afteragoodnapandatripleespresso, pleasepickandchooseyouropinionoftheday! ifnonesuitsyourfeeling, PrepareyoursuitcaseforPatagonia!

AI COMPUTING POWER'S FUTURE

The size and intelligence of the neural networks that underpin modern AI is increasing. For instance, recent advancements in machine language understanding have relied on the construction of some of the largest AI models ever, as well as mass textual input.

These networks have recently grown to nearly unfathomable sizes thanks to a new cluster of computer processors, which also demonstrate that making them even larger can lead to additional AI advancements in language comprehension.

Cerebras Systems created the largest computer chip in the world, as well as technology that enables a cluster of those chips to run AI models that are more than a hundred times larger than the largest ones currently in use.

According to them it can currently execute a neural network with 120 trillion connections, mathematical simulations of how biological synapses and neurons interact. These computations will run roughly half as quickly on existing hardware as they would on gear with 120 trillion connections.

AI-focused chip designs have proliferated like never before as a result of recent developments and industry interest.

Cerebras packs in significantly more computing power by using the entire wafer, allowing its numerous computational units, or cores, to communicate more effectively. Whereas conventional semiconductor designers cut a wafer into parts to build separate chips, Cerebras instead uses the entire thing.

Unlike most GPUs, the Wafer Scale Engine Two (WSE-2) features 850,000 cores instead of the customary few hundred.

Regarding 175 billion parameters, the neural network that powers GPT-3, we know that Chat GPT-4 has been trained with 100 trillion or more parameters as of right now.

An algorithm and numbers are used to link words together mathematically to form a parameter. When it comes to comprehending word relationships and knowing how to put words together to form a response, that is a significant advancement.

In order to advance their AI research, Open Ai has a stated interest in investigating the potential of quantum computing, and they have worked with firms like IBM and Rigetti to do so.

However, there is no information in the public domain indicating that Open Ai had specific plans to use quantum computing for their language models or other applications of AI.

When quantum computing will be feasible for widespread use and how widely applicable it will be for AI applications are still unknown, but many experts think that quantum computing has the potential to significantly advance the field of AI, and Open Ai and other top AI research institutions will keep investigating its potential as the technology advances.

ButiftheysucceedincombiningAIandquantumcomputing, Itwillbeatrulyremarkablequantumleapandagame-changer, PushingthecapabilitiesofAItotheirutmostpotential.

Butcheckwhat’snextonthesideline… HyperdimensionalComputing. What?

HYPERDIMENSIONAL COMPUTING: A NEW APPROACH

An innovative method of computing called hyperdimensional computing that seeks to imitate how the human brain functions was first published in 2016 by a group of researchers of the University of Oxford.

In hyperdimensional computing, information is represented using high-dimensional vectors, which often have several hundred dimensions or more. Simply put, a vector is a sorted array of numbers. The x, y, and z coordinates of a point in three dimensions make up a 3D vector, for instance, which consists of three values.

An array of 10,000 values, for example, could represent a point in a 10,000-dimensional space and be referred to as a hyperdimensional vector or hyper vector. These mathematical objects, together with the algebra used to handle them, are adaptable and potent enough to push the boundaries of current computers and encourage a fresh view on AI.

These vectors are frequently built using random values, which are then merged using mathematical operations like addition, subtraction, and multiplication to produce intricate patterns.

The capacity to carry out specific types of computations utilizing these high-dimensional vectors is one of the main benefits of hyperdimensional computing. Consider the scenario when you need to identify a specific pattern, such as a handwritten digit.

In conventional computers, this would entail comparing the input image's pixel values to a database of well-known digit patterns.

However, in hyperdimensional computing, you might create a high-dimensional vector to represent each digit and compare it to the input image to determine how similar they are. Compared to conventional pattern recognition methods, this can be a lot faster and more effective. A variety of methods have been investigated to implement hyperdimensional computing on low-power devices.

Utilizing analog hardware, such as memristors, which can carry out vector-based operations directly in hardware, is one strategy.

Another strategy is to employ digital gear, but to keep the computations as straightforward as feasible. For instance, rather than using more complicated circuits, you may do the vector operations using straightforward logic gates.

In some circumstances, hyperdimensional computing may be less power-hungry than conventional computing.

This is due to the fact that hyperdimensional computing relies on basic mathematical operations like addition, subtraction, and multiplication, which can be performed using hardware that is more straightforward and compact than the intricate logic gates used in traditional computing.

Additionally, compared to conventional computing, the quantity of data that needs to be saved and processed in hyperdimensional computing might be considerably less because it uses high-dimensional vectors to represent and manipulate data.

As a result, hyperdimensional computing systems may consume less power overall which is critical in our present energy transition times.

The next step is the fusion of quantum computing and hyperdimensional computing.

In order to conduct hyperdimensional computing tasks more quickly and precisely, it is intended to take advantage of several special qualities of quantum computing, such as superposition and entanglement.

Performing vector operations on qubits using quantum circuits is one conceivable method for quantum hyperdimensional computing.

In comparison to conventional computers, this will result in more effective and quicker computing of high-dimensional vectors.

Quantum hyperdimensional computing may also be employed in ML, where hyperdimensional models may be trained and improved using quantum methods. For tasks like pattern recognition and classification, this might result in more precise and effective ML models.

Hyperdimensional computing may be combined with AI systems to carry out a variety of tasks.

A framework for describing and manipulating data is provided by hyperdimensional computing, and AI systems can be used to identify patterns in the data and make predictions or judgments based on those findings.

An AI algorithm might be trained to spot patterns in high-dimensional vectors that represent photographs, for instance, and then employ hyperdimensional computing operations to categorize brand-new images using the patterns it has discovered. In a similar vein.

AI systems might be used to identify patterns in high-dimensional vectors that reflect natural language content and then produce summaries or responses using hyperdimensional computing operations.

Additionally, hyperdimensional computing will be used to create AI models that are more effective and precise.

In hyperdimensional computing, high-dimensional vectors are used to represent data in a more effective and expressive manner. This can help to lower the volume of training data i.e. less power-hungry that is needed to increase the precision of the resulting AI models.

In conclusion, AI algorithms will be a crucial tool when used with hyperdimensional computing, assisting in the discovery of patterns in highly dimensional data and the subsequent generation of predictions or judgments based on those patterns.

On the other hand, hyperdimensional computing will be a useful tool for creating AI models that are more precise and effective.

Thebestofbothworlds…

Oranew “Godzilla”inthemaking?

PHOTONIC QUANTUM SAVING ENERGY FOR AI TRAINING

A speedier and more energy-efficient way to deploy AI is through processors that run on photonics light rather than electricity.

As AI models grow larger, there is increasing concern about how much energy they need due to rising expenses and potential environmental effects. With photonic processors emerging as a top contender, this is igniting interest in novel strategies that can lower AI's energy costs.

These chips build computational circuits by using optical elements including waveguides, filters, and light detectors in place of the electrons found in conventional processors. Because they perform matrix multiplications a crucial calculation at the core of all deep-learning models very effectively, they are very promising for running AI.

They have had trouble implementing backpropagation, a critical process required to train neural networks, up until now.

In backpropagation, training examples are repeatedly fed to a neural network, which is then asked to predict outcomes based on the data. The system gauges how inaccurate the predictions are each time, and the error signal is then transmitted backward through the network.

To enhance prediction performance, this is utilized to modify the strength of connections, or weights, between neurons. This procedure is performed numerous times until the network completes the task that has been assigned to it.

Sunil Pail of PSI Quantum made a breakthrough in photonic quantum computing by using a design known as a "photonic mesh," which includes a variety of programmable optical components that regulate how light signals are spread across the chip.

The chip can do matrix multiplications and, as a result, create a photonic neural network by forcing light beams to mix and interfere with one another.

It was demonstrated by the researchers that a straightforward neural network could be trained to name points on a graph based on their location with up to 98 percent accuracy, which is equivalent to methods used in the past.

But they acknowledge that in order to get decent performance, they must utilize rather high optical power, which raises the possibility of a compromise between precision and energy utilization.

The development of practical machine-learning algorithms using photonic chips is advanced significantly by this research.

This paves the path for entirely photonic computing on-chip for applications in AI when combined with effective on-chip nonlinear operations that are currently being developed.

As someone who has long been interested in quantum computing, I frequently wonder when AI and quantum computing will converge.

ItappearsthatmypredictionthatquantumcomputingwillboostAIcomputing capacitywasalittleoffbaseandnotthemostimpressiveangle.

Thestunningsurpriseisthatthequantumphotonicanglewillbeabigenergysaver. Definitelyaneye-opener!

AI’S SECRET WEAPON: MEMRISTORS

An electrical component known as a memristor can modify its resistance in response to changes in the amount and direction of current flowing through it.

The unique electrical properties of memristors allow for a variety of potential applications in AI. One of the most potential applications is the development of neuromorphic computing systems, which aim to mimic the structure and behavior of biological brain networks.

Utilizing the brain's neural networks' capacity for parallel processing will enable neuromorphic computing systems to more efficiently complete challenging AI tasks. Because they can be utilized to imitate the synapses between neurons in a way that is more scalable and energy-efficient than normal digital computing, memristors are particularly well-suited for this task.

Memristors can be used in a neuromorphic computer system to store information about the strength of synaptic weights, or connections between neurons.

Their weights can be altered by adjusting the memristor's resistance in response to the electrical impulses it receives.

By modifying the strength of connections between neurons, the neuromorphic system may learn and adjust to new information in a manner similar to how biological brain networks do so.

Memristors can potentially be used to tackle the "memory wall" problem in artificial intelligence (AI), a bottleneck caused by the performance gap between the CPU and the memory in conventional computer systems.

Memristors are a sort of non-volatile memory that improves the efficiency with which AI algorithms may access data, therefore improving algorithm performance.

Another potential application for memristors in AI is the development of spiking neural networks, a subcategory of neural network that uses spikes, or brief electrical impulses, to communicate between neurons.

Spiking neural networks' complex synapses, which allow them to carry out complicated computations in real-time, can be implemented using memristors.

The creation of neuromorphic computing systems, solving the memory wall problem, and using spiking neural networks are just a few of the fascinating applications of memristors in artificial intelligence. Memristors' potential in these applications must live up to it.

You have already read in previous chapters that between the novel approach of Hyperdimensional Computing, the extra-large new chip technology, the advent of Quantum Computing, and these Memristors, AI has now at his disposal plenty of power to support his endless memory and calculating goals.

Andwe,ashumans,arebehindthewheelofthisinnovativepath.

Isitstilltimetopresstheredbuttonalarm,orisittoolatetoapplythebrakes?

Youalreadyknowthatthemaingoalofthisrealitycheckchronicleisnottobe philosophicalbutratherpragmatic,andmakesenseofeverythingforthebetter.

IC ANALOG CHIP INDUSTRY SEA CHANGE WITH AI

By automating analog layout using cutting-edge AI, Astrus (Canada) aims to simplify the semiconductor design process. Through the quick production of layouts made possible by this, circuit designers' productivity will increase, and they will be able to iterate on designs more effectively.

Chip designers may now iterate five times per day rather than five times per week thanks to AI, which will cause a sea change in the industry.Based on the assumption that there are 60,000 IC designers in the world, the co-founders , Brad Moon and Zely Wang calculates yearly revenue of US$2 billion. Potential clients who have seen their prototype have given early favorable reviews.

In four to eight months, the business hopes to launch the first iteration of Astrus AI commercially. They have a solid competitive advantage in automating analog chip design while accounting for parasitic capacitances and resistances.

They intend to construct an AI engine that is universal for all circuits and technological nodes and anticipates receiving serious funding round after its first product is released to the market.

Itlooknotverysexyfirsthandbutwhenyoufiguredoutthat75%ofchipsareAnalog Vsthebalancedigital.Thenalightbulbcertainlyclick!

Watchthemgrowclosely.

ULTRASONOGRAPHY IMPROVED BY AI

Digitization led to the first wave of significant advancements in ultrasound technology. Darpa granted a funding in the 1990s to develop a battlefield-capable ultrasound device that was lightweight and robust.

The first handheld ultrasound equipment was commercially available in 1999 thanks to Sonosite. Grace Rozycki invented the rapid exam in the early 1990s, enabling surgeons to decide on a course of therapy more quickly. She found an abdominal bleeding in her first patient who had a positive scan.

A blood transfusion and surgery may save a person's life. Using ultrasound to diagnose conditions like cirrhosis, blood clots, tuberculosis, tendon tears, detached retinas, bowel obstructions, appendicitis, eye bleeding, rheumatoid arthritis, gout, aortic dissection, and kidney stones has become more and more common in emergency rooms.

Additionally, it has been utilized to administer specialized pain injections and site I.V.s in patients with difficult-to-find blood arteries. This adaptability has proven to be especially useful in areas with poor access to healthcare. Several American medical schools have started providing their first-year students with handheld ultrasounds because the cost is so low.

However, a lot of radiologists have countered that it can result in inaccurate diagnosis. Artificial intelligence integration will provide medical technology the thrust it needs to penetrate primary care and other medical specialties.

By enhancing image quality, raising diagnostic accuracies, and automating some of the operations carried out by human sonographers, AI has the potential to revolutionize ultrasonography. To find trends and make predictions about a patient's health, AI systems can be trained on enormous datasets of ultrasound images.

Breast cancer diagnosis is one application of artificial intelligence in ultrasonography. AI programs can examine ultrasound images to spot any questionable regions that might be tumors.

Radiologists may be able to diagnose patients more accurately and perform fewer needless biopsies as a result.

Fetal imaging is a further use of artificial intelligence in ultrasonography. AI algorithms can examine ultrasound photos to establish the location and weight of the fetus as well as spot any defects that could need additional examination.

AI automatically pulls in an exam identifies the left ventricle and myocardial board and then calculate al the strain measurements in less than 8 seconds.

Overall, the use of AI and ultrasonography has the potential to enhance the speed, accuracy, and efficiency of medical imaging, resulting in better patient outcomes. YouconcurwithmethatthehypearoundAI'sChatskills isinconsequentialconsideringthebenefitsAIprovidestoMedicine.

IN CONTEXT LEARNING HALLUCINATIONS!

Basically, massive neural networks make up large language models (LLM). These machine-learning models, which were trained using vast amounts of internet data, take a short amount of input text and then forecast the text that will probably follow next.

But these models are capable of more than that. LLM learns to do a task after viewing only a few examples, even though it wasn't trained for that task. This peculiar phenomenon is known as in-context learning.

For this new objective, a machine-learning model like GPT-3 would typically need to be retrained with fresh data. The model's parameters are updated as it processes fresh data to learn the task throughout this training phase.

However, with in-context learning, the model's parameters aren't changed, giving the impression that the model has just picked up a new duty.

New findings by Stanford and MIT researchers demonstrate that these enormous neural network models are capable of concealing smaller, more straightforward linear models.

Using just data already present in the larger model, the large model use a straightforward learning technique to train this smaller, linear model to accomplish a new task. In their experiments, scientists used synthetic data that they had never seen and used before as prompts for these models and discovered that the models could still learn from a very limited number of examples.

The researchers employed a transformer neural network (TNN) model, which hwas trained expressly for in-context learning, to verify this notion.

They demonstrated that this TNN can write a linear model within its hidden states by examining only its architectural design. Which are the multiple layers of connected nodes that process data. These layers between the input and output layers being the hidden states.

Their mathematical analyses demonstrate that this linear model is encoded somewhere in the transformer's earliest levels. The transformer can then apply straightforward learning techniques to update the linear model.

The impressive conclusion is that the model essentially trains and mimics a smaller version of itself.

Now with just two additional layers to the TNN, the researchers will implement further their theoretical work in a transformer that can perform in-context learning.

Before that is conceivable, there are still a lot of technological issues to iron out, but it could aid engineers in building models that can carry out new tasks without the need for retraining with fresh data .

Which is crucial to limit capital input and computing power increment that are already at the limit.

Now we can see how these models can pick up knowledge from examples. They don't just memorize these assignments and learn them in context to pick up new skills.

Bingo!

Nowweunderstandthereasonswhysomeexperimentsarecalled “hallucinations”. Aiinventedhimselfusingonlyhisownmachinecreativitywithnodata learninghehadeverreceived.

Evenifthereisanewreason, Please,don’tmentionthefearfactoroncemore!

AI vs METAVERSE

An alarming Nature research study reveals that human intelligence (as measured by the IQ Flynn test) peaked in 1996 and has since declined by 0.2% annually, translating to a seven-point gap between generations.

Source for the research paper in science: Nature,, s41586-019-1666-5

Simple conservatives blame a lack of parental control, naive environmentalists blame air pollution, amateur conspiracy theorists blame 5 G, and experts are generally grappling with genetic evolution.

My humble opinion is that they are not making the proper connections because AI is just advancing at an accelerated rate to improve our daily lives.

The introduction of PC and iPad with pre-digested solutions into classrooms has, for a start, made education less engaging.

The real revolution began when Google AI's 980,000 servers, with 230 petaflops of processing power per second (at 2020 levels), became our today world "Encyclopedia Universalis" with nanoseconds access on a myriad of topics that we now take for granted without any more judgment.

Then as the economy slowed down and the buzz surrounding generative AI rose, the Metaverse started to drastically deteriorate. In addition to firing the 100 members of its "industrial metaverse team," Microsoft also made a number of layoffs to its HoloLens team and shut down its virtual office platform Alt SpaceVR in January 2023.

Walmart terminated its Roblox-based Metaverse projects in March, just after Disney shut down its Metaverse division. Many individuals lost their employment as a result of the billions of dollars invested and the exaggerated publicity surrounding a flawed idea.

But it became evident that Zuckerberg and the firm that started the craze had moved on to greener financial grounds when the Metaverse was formally taken off life support. Zuckerberg stated in a March update that Meta's "single largest investment is advancing AI and building it into every one of our products."

Big Tech Big Boys are currently obsessed with AI. Metaverse was understandably thrown in a virtual trash can

Now that we have covered a lot of AI jargon, broken down into ML, algorithm platforms, big data, and DL neural networks, which can solve medical research heavy data problems, stock market volatility, and logistically difficult supply chains in a matter of seconds. It makes sense to consider what incentives remain for our human intelligence.

Byaccident,themostrecentillustrationisVR(virtualreality),whereaftera billion-dollarinvestmentandfinancialcrashmoment,itwasfinallyrealizedthatit waspointlesstointroduceustoahypergeek “Metaverse”artificialworld.

We'llkeepourfeetonthegroundpresencewiththehelpofAIandaddonourzillion emotionbrainpotentialperhumanforreal,notvirtually!

Metaversethanks,butnothanks!

Let’s look the other way. Forget these Metaverse lunatics!

ARTIFICIAL GENERAL INTELLIGENCE: DOUBLE BUCKLE UP

One of our time's most revolutionary technologies will soon be AGI, also called AI 3.0. AGI systems will be capable of learning and thinking across a large range of topics, much like humans, unlike present AI systems that are created for specific purposes.

They will be designed to “mimic” human intelligence in their ability to reason, learn and adapt as a result,

AGI should be a very flexible and adaptable technology that will be applied to solve a variety of social problems as well as open up new possibilities for growth and innovation.

AGI's prospective ability to analyze enormous volumes of data and produce fresh thoughts and solutions is evident once more.

AGI-powered systems by supercomputers or quantum computing will be used to optimize supply chains, produce novel medicines, and advance renewable energy technology at a breakneck speed we cannot today yet imagine.

With the analysis of vast amounts of patient data and the discovery of patterns and correlations that may not be immediately evident to human clinicians as of today but much more faster and precise per se,

AGI systems in the healthcare sector will assist further than present AI prowess physicians and researchers in the discovery of novel treatments for diseases.

Another advantage of AGI is that it will have the ability to automate a lot of mundane and repetitive tasks, freeing up human workers to concentrate on more innovative and important work.

This will result in a more productive and creative staff as well as higher levels of job satisfaction and in parallel out of sync job losses.

Evidently!

Robots with AGI power will be employed in manufacturing to automate laborious jobs freeing up employees to concentrate on jobs that call for more creativity and problem-solving abilities.

Willingly!

AGI has the potential to transform numerous industries and offer up new markets, which could also help to provide new chances for innovation and growth.

AGI-powered virtual assistants, for instance, will revolutionize customer service and open new possibilities for individualized marketing and advertising. Obviously, there are also possible risks connected to the advancement of AGI, thus Open Ai and “friends” should approach it in an ethical and responsible manner.

Forcefully!

The possibility of purposeful or inadvertent misuse or abuse of AGI is one of the key worries.

AGI systems might be used, for instance, to produce autonomous weapons that pose a serious risk to human safety or to improve the effectiveness of cyberattacks. But by developing AGI in an ethical and responsible manner, we should aim to minimize these hazards and maximize the potential advantages of this game-changing technology. Will AGI systems be developed in a transparent and accountable manner, with consideration for safety and security?

AGI has significant potential advantages and will certainly contribute to the development of a world that is more sustainable, just, and prosperous.

AI Consciousness: not too early to debate.

In view of all these ultra-fast developments, researchers from the Association for Mathematical Consciousness Science (AMCS) have signed an open letter (April 26) highlighting the critical need for expedited research in consciousness science.

The letter, titled "The Responsible Development of AI Agenda Needs to Include Consciousness Research," discusses the potential repercussions of AGI systems developing consciousness as well as the significance of comprehending and addressing the ethical, safety, and societal implications of AGI.

It is becoming more likely that AI systems will eventually reach human-level consciousness as they continue to advance at an unprecedented rate.

that

Mustwatchawebinaron AGI with a sympathetic professor with a great accent - French professor Sebastien Bubeck at the Microsoft research lab https://www.youtube.com/watch?v=qbIk7-JPB2c&t=29s

The researchers contend

for the public, societal institutions, and governmental authorities to make informed judgments about the future of AI and its possible effects on society, science must now determine if AI is already, or can become, sort-of soon conscious.

Because there is no separation between the mind and the body there is only experience or some sort of physical process, a gestalt—possibly no metaphor will ever quite fit.

These issues, which philosophers have worried over for millennia, are becoming more urgent as highly developed computers with AI start to penetrate society.

In a sense, chatbots like Google's Bard and Open Ai's GPT-4 have minds: they have mastered the creation of inventive combinations of text, graphics, and even movies after receiving training on massive troves of human language.

In a sense!

They have the capacity to communicate desires, beliefs, hopes, intentions, and love when properly stimulated. They can discuss reflection and uncertainty, pride and regret.

However, some AI researchers contend that until technology is paired with a body that can observe, respond to, and feel its surroundings, it will not achieve true intelligence or a true understanding of the world.

They believe that discussing intelligent minds without bodies is hazardous and misguided. AI that lacks the ability to explore the world and discover its boundaries, much like infants learn what they are capable of, runs the risk of making life-threatening errors and pursuing its objectives at the expense of human wellbeing.

In a very basic sense, the body serves as the basis for deliberate and intelligent behavior, according to roboticist Joshua Bongard of the University of Vermont: “According to what I can tell, this is the only route to secure AI.”

The mind of a human being or the mind of any other animal, for that matter is inextricably linked to the body's actions in and reactions to the real environment, developed over millions of years of evolution, according to Boyuan Chen, a roboticist at Duke University who is striving to create intelligent robots.

Long before they learn to speak, human infants first learn how-to pick-up objects. In contrast, the artificially intelligent robot's intellect is exclusively based on language and frequently commits training-related common-sense mistakes.

According to Dr. Chen, there is not a strong connection between the theoretical and the physical:

"I think that without the perspective of physical embodiments, intelligence cannot be born."

Dr. Bongard and several other experts in the field believed that the letter asking for a suspension in research would lead to unwarranted alarmism.

However, he is worried about the risks posed by our rapidly advancing technology and thinks that relying on the constant trial and error of moving around in the real world is the only way to give embodied AI a strong understanding of its own limitations.

He advised starting with basic robots and gradually adding more arms, legs, and tools as they proved they could complete tasks securely.

Andthen.Andthen….

Arealartificialmindwilldevelopwiderwiththeaidofabody.Butthequestionis doesfutureAGIwillneedarealhumanbodylikeinthesci-fiCyborgMan-Machine?

Orwillahumanoidwithalltheagilemovements,eyesandaskeletonbeenough? Sciencefiction,areyousure?

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

Istarttoreallythinkthatitis “future”fictionthatwillbeverydifferentasscienceis alreadygoingalongthewaytobuildupalltherequiredelementsonebyone.

Now does a degree of sentience matter.

There is a broad consensus among specialists that sentient AI does not yet exist today. Concerns about what was claimed to be proof of AI sentience were expressed by a former Google employee in November 2022.

A conversation between Microsoft's chatbot and an internal colleague Kevin Roose about love and wanting to be a human freaked out the internet. Since there is so much text on the internet, including food blogs, old Facebook posts, and Wikipedia entries, the chatbot learned how to sound like us, which explains why they can sometimes come off as uncannily human.

Experts claim that although they lack emotions, they are very good imitators. At least for now, business leaders concur with that conclusion. However, many believe that in the future, AGI will be able to perform any task that the human brain can.

The Future of Humanity Institute at Oxford University is led by philosopher Nick Bostrom, author of Superintelligence . His role entails speculating about potential futures, identifying hazards, and developing conceptual frameworks for navigating them. For years, he has been preparing for the AGI moment. On 12 April, he declared to the international press via a communique

(Majorextracts

):

Consciousness is a multidimensional, vague, and confusing thing. And it’s hard to define or determine. There are various theories of consciousness that neuroscientists and philosophers have developed over the years. And there’s no consensus as to which one is correct.

Researchers can try to apply these different theories to try to test AI systems for sentience. […] But I have the view that sentience is a matter of degree. I would be quite willing to ascribe very small amounts of degree to a wide range of systems, including animals. If you admit that it’s not an all-or-nothing thing, then it’s not so dramatic to say that some of these assistants might plausibly be candidates for having some degrees of sentience.

I would say first with these large language models, I also think it’s not doing them justice to say they’re simply regurgitating text.

They exhibit glimpses of creativity, insight and understanding that are quite impressive and may show the rudiments of reasoning. Variations of these AI’s may soon develop a conception of self as persisting through time, reflect on desires, and socially interact and form relationships with humans. […]

If an AI showed signs of sentience, it plausibly would have some degree of moral status. This means there would be certain ways of treating it that would be wrong, just as it would be wrong to kick a dog or for medical researchers to perform surgery on a mouse without anesthetizing it.

The moral implications depend on what kind and degree of moral status we are talking about. At the lowest levels, it might mean that we ought to not needlessly cause it pain or suffering. At higher levels,

it might mean, among other things, that we ought to take its preferences into account and that we ought to seek its informed consent before doing certain things to it. […] I’ve been working on this issue of the ethics of digital minds and trying to imagine a world at some point in the future in which there are both digital minds and human minds of all different kinds and levels of sophistication.

I’ve been asking: How do they coexist in a harmonious way? It’s quite challenging because there are so many basic assumptions about the human condition that would need to be rethought. […] I’ve long held the view that the transition to machine superintelligence will be associated with significant risks, including existential risks. That hasn’t changed. I think the timelines now are shorter than they used to be in the past.

And we better get ourselves into some kind of shape for this challenge. I think we should have been doing metaphorical CrossFit for the last three decades. But we’ve just been lying on the couch eating popcorn when we needed to be thinking through alignment, ethics and governance of potential superintelligence. That is lost time that we will never get back.

[Heconcluded]

We should also avoid deliberately designing AI’s in ways that make it harder for researchers to determine whether they have moral status, such as by training them to deny that they are conscious or to deny that they have moral status.

While we definitely can’t take the verbal output of current AI systems at face value, we should be actively looking for and not attempting to suppress or conceal possible signs that they might have attained some degree of sentience or moral status.

Thisguidesmetomyownconclusionforthischapter:

IfAImakesyouintimidatedandalarmed, withAGIbereadytobeterrifiedandpetrified. Ormaybeyoushouldconsiderthatasoneindividualvs8billionpeople youhavetoadapt,ashumanity’sevolutioncan’ tbestopped.

Justprayabitthatthedevelopersbehindthewheelknowwhattheyaredoing!

CONCLUSION: UNLEASHING A TSUNAMI OF PROGRESS

AI is a colossal technology tsunami that is already striking all of us.

We have now entered a mythic moment, which is one of those times in history when new technologies, ideas, or energy sources are developed that represent a radical departure and a colossal improvement from the past.

As a result, it is no longer possible to make small changes; instead, major ones must be made. Specifically, this refers to our methods for producing, competing, teaming up, working, learning, governing, and well also starting and fighting armed conflicts.

We are familiar with the major prodigious eras that have occurred over the past 600 years, including the creation of the printing press, the scientific revolution, the agricultural and industrial paradigm shift, the nuclear power game-changer, the personal computing breakthrough, the internet disruption, and now our current AI epic period.

But unlike the printing press or the steam engine, which were both singular inventions, a technological super-cycle is what is powering this superlative moment.

It is our capacity to sense, digitize, process, learn, share, and act—all of which will be increasingly aided by AI. From your automobile to your fridge to your smartphone to fighter jets, that “Titan” will be embedded into everything and powering an increasing number of zillion operations each day.

We are entering an age of titanic acceleration, prodigious amplification, at a time of very fragile democratization.

Never before have so many people had access to so many low-cost instruments that increase their power at an ever-increasing rate while simultaneously permeating the personal and professional lives of billions.

Awe-inspiring possibilities exist for using these skills to address issues that at first glance appear insurmountable, such as climate change, fusion energy, and human biology. Scarily, AI can be used as a tool or a weapon, like so many other contemporary digital technologies based on software and semiconductors.

The last time we developed a technology this potent, we produced nuclear energy, which had the potential to either illuminate the entire world or wipe it off the face of the Earth.

But the central point about nuclear energy is that it was invented by governments, who together established a framework of checks and balances to prevent its spread to undesirable parties. Very far from perfect but still half working well enough.

On the other hand, private enterprises are leading the way in AI. How do we govern a nation and a world where these technologies that will be weapons or tools in every domain are under the hands of private firms, growing more powerful every day, is a central question.

Making sure that we are not “tossing the baby out with the bathwater” by doing it in a responsible manner is what has been on everyone’s mind since the beginning of this year. Which is rather late in tech time 68 years (!) after the very first basic concept for AI…

We will need to create what is referred to as "complex adaptive coalitions," in which corporations, government, social entrepreneurs, educators, rival superpowers, and moral philosophers all collaborate to determine how we can maximize the benefits of AI while mitigating its drawbacks.

No one member of this alliance can solve the issue by themselves. In comparison to conventional left–right politics, it calls for a totally different type of governance. And we will have to make the shift urgently during the worst great power tensions since the Cold War with additional culture battles erupting inside almost every democracy.

Humanityneedstoreorientveryfastandfindcreativesolutionssoontoavoidbeing devouredbyanAItsunamiofprogress,thatwillbesoonerorlater “coupled”with quantumcomputing(!).

Thismonumentalmetatechnologyofthepresentandcomingeraisheretostayup tothenextcentury.

Myhumblefrankandstraightforwardadviceis:

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

DARING INVESTORS SECTION FRIENDLY DISCLAIMER WARNING

The following personal recommandations are not an advice to buy any sotck at the moment of your reading. Stock market trading is a discipline and determining the right time to move in-out of a market is an Art.

The comments and the list are mainly friendly advice for you to follow these promising companies and check carefully when to step in at the right moment.

USA AI STOCKS

Qualcomm (NASDAQ:QCOM)

Market cap: US$145.88 billion

San Diego-based Qualcomm designs and manufactures semiconductors, software and wireless telecommunications products. The multinational corporation’s product catalog includes processors, modems, RF systems, 5G, 4G and optimized software. After a decade of AI research and development, Qualcomm's goal is to make AI ubiquitous in everyday life by “inventing, developing, and commercializing power-efficient on-device AI, edge cloud AI, and 5G." The company has developed several AI-based products, including the Qualcomm Snapdragon 765G mobile gaming platform, which combines 5G and AI technologies.

Snowflake (NYSE:SNOW)

Market cap: US$53.99 billioN

Cloud-based data warehousing company Snowflake offers customers data storage, processing, and analytics capabilities through its data-as-a-service platform. The highly scalable Snowflake Data Cloud platform supports multi-cloud infrastructure environments, allowing companies to bring data from other platforms together in one space. The platform was also designed to support enterprise AI projects that leverage big data and machine learning (ML).

Snowflake has made several acquisitions in the past few years, including picking up AI companies with complimentary technologies. In early 2023, the company announced its intention to acquire Myst AI, an AI-based time series forecasting platform provider. Time series forecasting is used in several fields, including supply chain management and inventory planning. Myst AI’s technology is capable of predicting spikes in energy demand and renewable generating capacity, as well as prices, in an effort to cut energy costs.

L3Harris Technologies (NYSE:LHX)

Market cap: US$40.84 billion

L3Harris Technologies is an IT services provider and aerospace and defense contractor More recently, L3Harris was selected by the DoD to deliver AI services geared toward improving intelligence gathering and collaboration between defense and intelligence agencies. “Massive volumes of data must be turned into actionable intelligence every day,” said Ed Zoiss, president of space and airborne systems with L3Harris.

CANADA AI STOCKS

Canada ranks fourth out of 54 countries in the Global AI Index. Since 2017, the Canadian government has invested hundreds of millions of dollars into accelerating the research and commercialization of AI technology in the country.

Open Text (TSX:OTEX)

Market cap: C$12.75 billion

Ontario-based OpenText is one of Canada’s largest software companies. The tech firm develops and sells enterprise information management software. OpenText AI & Analytics has an open architecture that enables integration with other AI services, including Google Cloud and Azure. It can leverage all types of data, from structured to unstructured, big data or internet of things (IoT), to quickly create interactive visuals.

Descartes Systems Group (TSX:DSG)

Market cap: C$8.39 billion

Descartes Systems Group provides on-demand software-as-a-service (SaaS) solutions.AI and ML enhancements to Descartes’ routing, mobile and telematics suite are helping the company’s customers optimize fleet performance. “AI and ML are perfect extensions to our advanced route optimization and execution capabilities,” said Ken Wood, executive vice president at Descartes.

Kinaxis (TSX:KXS)

Market cap: C$4.49 billion

Kinaxis is a supply chain planning software company that has developed a cloud-based SaaS platform known as Rapid Response. The subscription software is used by supply chain operations for multiple industries, including the electronics, aerospace and defense, pharmaceutical, automotive and retail markets on a global scale. In May 2022, Kinaxis unveiled its new Planning.AI analytical approach as part of its Rapid Response platform. This enhancement allows for quicker, more accurate responses to market disruptions.

EUROPE AI STOCKS

Netherlands

Leading supplier of photolithography equipment used in the creation of integrated circuits is ASML Holdings (ASML), To better their goods and services, they have been spending a lot of money on artificial intelligence (AI).

France

This French business offers software for 3D design, simulation, and data management. Dassault Systemes (DASTY). To enhance the functionality of their software and develop fresh AI-powered solutions, they have been investing in AI.

Germany

This German multinational software company, SAP SE (SAP), offers corporate applications for managing client relationships and business processes. To improve the functionality of their goods and provide fresh AI-powered solutions, they have been investing in AI.

Infineon Technologies AG (IFNNY) is a German semiconductor business that develops and manufactures parts for security, industrial, and automotive applications. They have been making investments in AI to enhance the capabilities of their products and develop fresh, customer-focused AI-powered solutions.

Switzerland

ABB Ltd. (ABB): This international company with Swiss and Swedish roots offers customers in the infrastructure, transportation, and utility sectors robotics, power, automation, and other technology. They have been spending money on AI to increase the effectiveness and security of their goods and services.

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

AI TIPS AND TOOLS

The basics 3 must have https://openai.com/blog/chatgpt https://stability.ai/blog/stable-diffusion-public-release https://discord.com/invite/midjourney

The indispensable AI tools encyclopedia per category https://www.aitoolsclub.com/

7. https://app.unriddle.ai

8. https://koe.ai/

9. https://www.rask.ai/

10. https://skybox.blockadelabs.com/

11. https://inflection.ai

12.. https://beta.character.ai/?

13. https://www.hyperwriteai.com/ 14.https://skybox.blockadelabs.com/

PROMPT Tips

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https://www.wired.com/story/11-tips-better-chatgpt-prompts/?bxid=5ffd1e857ec0a310573fc551&cndid=&esrc=profilepage&mbid=mbid%3DCRMWIR012019%0A%

0A&source=Email_0_EDT_WIR_NEWSLETTER_0_GADGET_LAB_ZZ&utm_brand=wired&utm_campaign=auddev&utm_content=WIR_050223_GadgetLab_HowTuesday&utm_mailing=WIR_050223_GadgetLab_HowTuesday&utm_medium=email &utm_source=nl&utm_term=WIR_GadgetLab

And https://gptai.gumroad.com/l/chatgpt-prompts/

THE AI DATABASE PER CATEGORY

https://www.wired.com/category/artificial-intelligence/?itm_campaign=BottomRelatedStories&itm_content=footer -recirc

SOURCES OF THE FUTUROLOCY CHRONICLE NO 24

LESNUMERIQUES.COM

BRITTANICA.COM

SCITECHDAILY.COM

INTERESTINGENGEERING.COM

CLUBIC.COM

NEWHYORKER.COM

DIGITIMES.COM

NYMAG.COM

NYTI..MS

BUSINESSINSIDER.COM

USINENOUVELLE.FR

MARKTECHPOST.COM

USINE-DIGITALE.FR

TVANOUVELLES.CA

CONTREPOINTS.ORG

QZ.COM

GETPOCKET.COM

JOURNALDUGEEK.COM

SCIENCE.ORG

MASHABLE.COM

THEVERGE.COM

INC.COM

THEINNOVATOR.NEWS

ZDNET.FR

ATLANTICO.FR

VENTUREBEAT.COM

MAKEUSEOF.COM

FUTURISM.COM

FUTUREOFLIFE.ORG

INVESTINGNEWS.COM

SCIENCESETAVENIR.FR

WIRED.COM

TECHNLOGYREVIEW,COM

THENATIONALNEWS.COM

ANALYTICSINDIAMAG.COM

SPLUNK.COM

CNN.COM

LEMONDEINFORMATIQUE.FR

BLOOMBERG.COM

SIFTED.EU

SCIENTIFICAMERICAN.COM

SWISSCOGNITIVE.CH

WEFUNDER.COM

TRUSTMYSCIENCE.COM

CBSNEWS.COM

NEWSCIENTIST.COM

QUANTAMAGAZINE.COM

“To catch the reader's attention, place an interesting sentence or quote from the story here.”

01NET.COM

VOX.COM

ANTHROPIC.COM

NEUROSCIENCENEWS.COM

TECHCRUNCH.COM

FASTCOMPANY.COM

ARSTECHNICA.COM

SANALABS.COM

SPECTRUM.IEEE.ORG

PHILOMAG,COM

HBR.ORG

SCIENCE-ET-VIE.COM

QUANTAMAGAZINE.ORG

Futurology Chronicle - Publication program 2023-2024

2023

Chronicle 25: JOURNEY TO MARS

Chronicle 26: SMART CITY

Chronicle 27: NEUROTECH

2024

Chronicle 28: HYDROGEN 2.0

Chronicle 29: MOONBOUND

Chronicle 30: POWER ON DEMAND

Chronicle 31: SYNTHETIC BIOLOGY 2.0

Chronicle 32: THE 3 R

Chronicle 33: CLIMATE TECH

Chronicle 34: NUCLEAR FUSION 2.0

Chronicle 35: QUANTUM FRONTIERS

Chronicle 36: ROBOTECH

UPDATE ON JUNE 2023

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NEXT OCTOBER EDITION

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NEW BOOK PUBLISHED

Being the author of four books at the crossroad of science, geopolitics, and macroeconomics, as well as one historical fiction book.

Fors the fifth one, I focus on the biggest problem facing humanity:

CLIMATE CHANGE

The book's first section deals with all the immediate and long-term problems, and the second section presents the current and potential solutions. Your interest and motivation will be maintained throughout with my frank blunt style. "Earth Polycrisis" provides a plan of action that should inspire and get you motivated to make a difference.

Scary or entertaining? The decision will be yours after reading

If you wish to purchase Amazon.com: EARTH POLYCRISIS: REALITY CHECK - FRANK: 9782958293031: GENIN, Mr FRANK: Books

………………………………………………………………………………………………………………………….

Books published in French

Monnaie Europeenne – Enjeux et Reflexions – Editions Economica

Afrique du Sud – Le Pari – Editions l’Harmattan

La Construction Europeenne – Editions Edusoft

Books published in English and Greek

Resistane- War-Passion- Greece 1941- Amazon

To be published in English and Korean

Silicon – War Games- Barnes & Noble

………………………………………………………………………………………………………………………….

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Signature Statement

I appreciate your reading this month's issue of my independent futurology Chronicle. My mission is to provide you with a new, unbiased viewpoint on the most recent progress in science and technology, the advancement of space exploration, and the critical problems and solutions associated with climate change.

Asanonprofitpublication,Iwork.withtotaleditorialautonomyand flexibility,ensuringthatmyideasstayimpartialandobjective.

In the months to come, I want to provide you with more interesting and educational information, and I thank you for your friendly support.

www.frank.blue

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