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.

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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.

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Especiallyduringtheenergytransitionperiodwhenclimatechangeissueshavea significantinfluenceondailylifeandworkinbuildings.

AndweshallfollowNormanFoster(BritishArchitect,87,WinnerofthePritzkerprice) thatsaidon10thMayinaninterviewwiththeNewYorkTimes

“Iamexcitedaboutthefuture,Ithinkthatthefutureismoreinterestingthanthe past,Ileavethepasttootherpeople