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Can AI research ever be objective? I would dare to say “probably not”. So where does all the reality and facts we all believe go? Most of what we believe to be “scientific facts”, can be considered prima facie plausible but are rarely buttressed with scientific objectivity. People are subjective in judgment and often do not notice the negative consequences of some innovation. While we may never know what the real facts are, we can have a better understanding of how we got here.
FACT CHECK: Al is not a mystical box of magic. All the glitters ain’t ResearchGold. can be defined as, the systematic application of a family of methods that are employed to provide trustworthy information about problems. In AI, the objective is always subjective. Subjective research is that provided by participants; as it is personal, it is often not possible to verify, and it assumes participants’ responses are honest and an accurate recall of what they’re asked. In the real world, subjective research is often biased and difficult to interpret accurately. Objectivity is personal neutrality; it allows the facts to speak for themselves and not be influenced by the personal values and biases of the researcher.
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Let’sdeveloped.seehow this works using the Artificial Intelligence cat/not cat
Different people find different sets of behaviors appropriate for replication. Therefore, Caveat Emptor you should always test an AI system someone else
For example, a person who looks at a painting could use objectivity to describe the texture, color, and form. Common facts that cannot be changed. But objectivity is often impossible because every person has some kind of subjectivity in them based on their life experiences and opinions. If his favorite color is blue then blue will be the predominant color he sees in the painting.
Characteristicscharacteristics: of a cat: mewing, two ears, four legs, a tail, and Characteristicswhiskers. of a test module: test or demo. In our sample, we assign an allowable label to each of the three images, similar to how an AI system would classify the three images. The only TWO allowable labels/outputs will be: § Cat § Not Cat
Thetest.identification of a cat is essentially the same as the identification of a test module. Both imply looking for
At its inception, it is up to the software developer to call those subjective shots and for the rest of us to stop believing in “miracles” and understand that AI systems have a lot of subjectivity programmed into them. When we create machine systems based on data, we teach them a sense of our AIvalues.does not set an objective for you that’s a human’s function. If you want AI to cheat, it will cheat; if you want it to discriminate, it will. It will do what the owner of the system wants it to do.
3 2 1 NOTCAT?CAT?
We see that picture one (1) is different from the other two (2) pictures, so is it a “big cat”, or “a kinda cat”? Remember we gave AI only two allowable labels/outputs: Cat or Not Cat. So is picture (1) a cat or not? This basic sample exemplifies how critical the programmer’s intent and input Aare.Software program is subjective, based on or influenced by personal feelings, tastes, or opinions. So was it this programmer's intent to have a “big cat/picture 1” send to their ex spouse or boss or was it to pick a sweet lovable pet? Each person reading this will have a subjective response. AI is a set of tools for writing software a different way, letting you program with examples (data) instead of explicit instructions. Initially, AI is premised on two factors: a core algorithm written by humans and training data developed by humans that inform how those algorithms modify themselves to improve or adapt their performance. It is easy to see how corruption can happen pre or post-release on purpose or by mistake. If human malintent, negligence, and ignorance scare you, then so will AI. Al ethics is a hot topic today, The World Economic Forum is a dynamic organization but I tend to feel their latest list of “ethical issues in artificial intelligence” are premised on idealism and theoretical concepts at best (World Economic Forum Releases Blueprint for Equitable and Inclusive AI > Press releases | World Economic Forum). Many AI ethics talking points today are not specific to AI, they are the same technology issues we have heard about for a long time. I’m not a big fan of lists and according to an article in Forbes, 41 percent of items on an average to-do list are never finished.
6 HUMAN I can learn automaticallyeverythingfromexperiences.CanUlearn? MACHINE Yes, I can also learn from past data with the help of LearningMachine
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Artificial intelligence and machine learning are part of computer science that are correlated with each other. Although these are two related technologies they are often utilized as a synonym for each other, but in fact and application, they are two different terms in various Artificialcases.intelligence
is a field of computer science that makes a computer system which can mimic human intelligence. It is comprised of two words "Artificial" and "intelligence", which means "a human made thinking power.” Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.
Machine learning gives computer systems the ability to automatically learn without being explicitly Theprogrammed.mostimportant thing in the complete process is to understand the problem and to know the purpose of the problem. Machine learning life cycle involves seven major steps, which are given below: § Gathering Data § Data preparation § Data Wrangling § Analyze Data § Train the model § Test the model § Deployment
BuildingLogicalModels
Learndatafrom
Machine Learning with Python 10
AlgorithmMachinelearning dataNew Training Output
DATAPAST Humans learn from their experiences with machines or computers which work on our instructions. Machine Learning is a subset of artificial intelligence that is mainly concerned with the development of algorithms that allow a computer to learn from the data and past experiences on their own without being explicitly Machineprogrammed.learning’s
INPUT
“right” answers are usually in the eye of the beholder, so a system that is designed for one purpose may not work for a different purpose. No test checks the stupidity of subjective definitions and objectives, so choose your project leader
Machinewisely.learning utilizes algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. A machine will learn if it can gain more data. A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it.
§ Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output. § Unsupervised learning is a learning method in which a machine learns without any supervision.
MACHINECLASSIFICATIONOFLEARNING
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10 UnsupervisedSupervisedLearningLearningReinforcementLearning
Every day Machine Learning becomes more and more of a necessity in that it is capable of doing tasks too large and complex for a person to implement Butdirectly.itgoes deeper than that, machine learning can consist of three variations: Supervised learning Unsupervised learning Reinforcement learning.
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§ Reinforcement learning. is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action.
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She has directed Corporate Technology Commercialization through the US National Laboratories. Emerging Diseases,Infectious Restrepo is also the Chief Executive Officer of Professional Global Outreach. Restrepo has advanced degrees from The University of Texas and New Mexico State University. Linda Restrepo is Director of Education and Innovation Human Health Education and Research Foundation. She is a recognized Women in Technology Leader Cybersecurity and Artificial Restrepo'sIntelligence.expertise includes Exponential Technologies, Computer Algorithms, Research, Implementation Management of Complex Human machine Systems, Global Economic Impacts Research. Restrepo is President of a global government and military defense multidisciplinary research and strategic development firm.
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