Category Archives: Artificial Intelligence

Bosch AI or the Bosch Center for Artificial Intelligence is focused on deploying advanced technologies such as AI across a wide range of Bosch products and services. AI strategies of Bosch are implemented in more than 185 projects in seven locations such as India, the US, Israel, Germany, and China. Bosch AI collects real-time data from different business departments and conducts AI research on safe, reliable, and explainable AI. Lets explore how Bosch leverages AI in different ways for meeting client satisfaction.

Bosch AI is popular for applying big data and machine learning to different varieties of Bosch products and services for accurate AI solutions. It helps to increase efficiency, optimize the supply chain, and enhance quality while reducing costs. The deep learning techniques are one of the AI strategies of Bosch to implement multiple smart functionalities such as automated optical inspection, anomaly detection, root cause analysis, production scheduling, and many more.

Bosch AI leverage artificial intelligence to extract value from the available real-time data to improve functionalities and perform efficiently and effectively. AI can streamline and improve technical capabilities to explore innovative approaches in this global tech-driven market. Supply chain management can utilize the power of Bosch AI for effective inventory management, demand forecasting, optimizing packaging sizes, and ensuring the availability of high-quality products and services.

Bosch has brought a transformation in the tech industry with the combination of state-of-the-art artificial intelligence technologies and domain expertise. Bosch AI is determined to recruit employees who are qualified professionals and graduates with sufficient experience in AI to employ the best in the field of artificial intelligence.

One of the AI strategies of Bosch AI is to create differentiating artificial intelligence solutions with concrete lead applications like AI-based dynamics modeling like Gaussian process-based models, control optimization through reinforcement learning, and large-scale deep learning. The research field of Bosch AI is focused on learning AI-based models with optimal data acquisition without destroying the system and investigating multiple machine learning approaches to control the system while guaranteeing controller stability.

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Originally posted here:

Bosch AI: Artificial Intelligence Technologies at the Heart of Bosch Products - Analytics Insight

Central Saint MartinsgraduateJann Choy has created an inflatable maskthat reacts to your online behaviour. This one-minute video shows it in action.

Named Lin, the mask is an experimental design piece that aims to explore the performance of our virtual selves and the relationship between our online and offline personas.

The mask uses a form of artificial intelligence known as sentiment analysis to understand your online interactions.

Depending on whether you choose to act positively or negatively, different corresponding areas of the mask swell, changing the structure of the mask.

The mask was created by Choy as her graduate project while studying at Central Saint Martins College. Choy was inspired by the traditional Chinese performance art bin lin.

Bin lin, which directly translates as "face changing", is a subgenre of Chinese opera. Performers wear a series of bold masks each representing a different emotion.

During the performance, actors switch between masks seemingly instantaneously.

Choy's design draws a parallel between this performance and our online behaviours.

The project was recently awarded the Mullenlowe Nova Awards for Fresh Creative Talent.

The rest is here:

Lin mask uses artificial intelligence to respond to your emotions - Dezeen

Technological progress has enabled unprecedented developments in the field of research. Especially in recent years. This is how the world of biology and medicine benefit from technological innovation.

The application of computer science to the world of biology and medicine has been absolutely revolutionary for both branches and has helped significantly in the improvement of treatments. As indicated by the Instituto de Salud Carlos III, bioinformatics has been fundamental in the analysis and interpretation of SARS-CoV-2 data. During 2020, the research carried out by the Bioinformatics Unit of the aforementioned centre was essential, since it shed light on such important issues as the sequencing of the genome of the new coronavirus and the automation of diagnostic tests.

Bioinformatics researches, develops and applies informatics and computational tools to improve the management of biological data, by using tools that collect, store, organise, analyse and interpret all these data.

In this sense, Big Data development has been one of the great driving forces for the improvement of research work in recent years. Other important contributions regarding innovation within the framework of connectivity and digitisation have been Artificial Intelligence and Machine Learning, supported by the 5G network, as they offer new perspectives for tackling an increasing number of biomedical problems, thanks to the creation of algorithms and mathematical models that extract maximum knowledge from data.

The analysis of massive data applied to health allows for a better understanding of the DNA and genome of different organisms in addition to human DNA, as well as of proteins, enzymes and amino acids. But some of the areas where the progress of bioinformatics has been truly significant, in addition to the information it has given scientists regarding the coronavirus, are the identification of mutations associated with tumours, of pathogens causing infectious outbreaks, and the study of rare diseases.

The use of new technologies for the early detection of rare diseases is improving many patients' quality of life. This is because thanks to them it is possible to obtain a quick and reliable diagnosis. Rare diseases affect fewer than 5 in 10,000 inhabitants, and according to data from the Spanish Federation for Rare Diseases, FEDER, they affect more than three million inhabitants in Spain. Due to their low prevalence, studies and research are very limited. This is why the emergence of Artificial Intelligence in this area of study has been so important.

According to an interview published by Agencia SINC with Julin Isla, President of the Foundation 29, it can take between five and six years for rare disease patients to get a correct diagnosis. This time is vital for these patients, given that in the meantime they may receive incorrect treatments that worsen their health condition. As a software engineer, Isla created an AI-based platform that helps doctors carry out a quick diagnosis by comparing data between symptoms and genetic information. It should be noted that 80% of rare diseases have a genetic component. Thus, with this platform, Isla has managed to reduce the diagnostic process to around 10 minutes by automating the genetic analysis.

Although bioinformatics has been around for a long time, the 4.0 revolution has been a turning point for all health-related sciences. This discipline has its origins in the 1960s, when computational models began to be applied to the study of proteins. Later, with the introduction of large-scale communication structures, it continued to grow until it reached personalised medicine.

For some years now, medicine has been using supercomputing to tailor treatments based on the origin of the disease and the patient's genome. One of the greatest examples of this feat is HIV treatments. According to the UN, AIDS has killed nearly 39 million people, and some 78 million have been infected since the first cases of the human immunodeficiency virus were diagnosed in the early 1980s in the United States.

HIV is characterised by rapid mutation, which allows it to evade antiretroviral treatment. To find a solution, scientists at the Barcelona Supercomputing Centre and IrsiCaixa have developed a bioinformatics platform capable of predicting these mutations in order to predict treatment effectiveness. Thus, the study of each patient's clinical experience helps to develop new therapies and to develop innovative treatments to be applied in a precise way, thereby promoting the implementation of personalised medicine.

The design of new medicinal products is another benefit of this discipline. It seeks a therapeutic target capable of modifying the course of a disease based on the study of biological data. This is one of the highest hopes of finding a cure for cancer in the not too distant future.

The scientific community already has powerful tools at its disposal, such as Big Data. to study the genome of diagnosed individuals, which will help understand the origin of tumours in the future. To this end, there is already an initiative called the International Cancer Genome Consortium, in which Spanish scientists are collaborating, which studies genetic, transcriptomic and epigenetic changes in more than 50 different types of tumours. This project has identified almost 4 million genetic mutations in participating patients, which could prove invaluable in the fight against cancer in the years to come.

Thanks to all these studies, new generations of drugs will be more effective and safer, and will be developed according to the genetic characteristics of each patient and thus save more lives.

Read this article:

Big Data, Artificial Intelligence and bioinformatics: three tools that save lives - marketscreener.com

Share

Share

Share

Email

The global Artificial Intelligence (AI) in food and beverage market size is expected to reach USD 62.83 billion in 2028 and register a robust CAGR of 44.4% throughout the forecast period. Consumer shift towards fast, easily accessible, and affordable food products has led to an alteration in the food & beverage industry.

Technological advancements such as machine learning and AI has influenced market growth. AI has been gaining traction over the last few years, with various companies actively investing in discovering potential of technology in the global industry. AI helps in supply chain management, predictive logistics, and analytics. Many market players dealing in perishable food products like vegetables, fruits, eggs, meat, poultry are utilizing AI to provide advanced forecasting models and improved quality assurance which is boosting market growth. Increasing need in food & beverages companies to decrease operational costs across supply chain is enhancing growth of the AI in food & beverage market. Growing need for reducing human errors is another driver propelling growth of the AI in food and beverage market.

Grab Your Free Sample PDF Copy Now

Some key highlights in the report:

For the purpose of this report, Emergen Research has segmented the global Artificial Intelligence (AI) in food and beverage market on the basis of end-use, application, and region:

End-use Outlook (Revenue, USD Billion; 20182028)

Application Outlook (Revenue, USD Billion; 20182028)

Regional Outlook (Revenue, USD Billion; 20182028)

North America

Europe

Asia Pacific

Latin America

Middle East & Africa

To get leading market solutions, visit the link below: https://www.emergenresearch.com/industry-report/artificial-intelligence-in-food-and-beverage-market

Link:

Artificial Intelligence in Food and Beverage Market Size Forecasted to Reach Valuation of USD 62.83 Billion... - TechBullion

Commentary: The house always wins in gambling, and the house is getting even tougher through machine learning.

Image: iStock/Igor Kutyaev

"On the Internet, nobody knows you are a dog," is easily one of the top 10 New Yorker cartoons of all time. Why? Because it captured the upsides and downsides of online anonymity. All good, right? Well, maybe. What if you are online, and you like to gamble? Who's on the other side? You have no idea, and that might be more of a problem than you might suspect.

SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)

For one thing, more and more you may be betting against machine learning algorithms, and if the "house always wins" in the offline world, guess what? It's even worse in an ML/artificial intelligence-driven online gambling world. Still, understanding the odds helps you understand the potential risks involved as the gambling industry consolidates. So, let's take a look at how one person used ML to fight back.

Go to any casino in person and the best odds you can get range from the housetaking from 1.5% to 5% off the top (craps, baccarat, slot machines and Big Six can take more than 20%). You are essentially renting access to their game. The money you bet allows you to earn back about 95 to 98 cents on the dollar (the card game blackjack, by the way, is your best bet). But any way you choose, over time you almost certainly go broke. Why? Because ... math.

SEE: Research: Increased use of low-code/no-code platforms poses no threat to developers (TechRepublic Premium)

The casino industry willargue that AI/ML helps gamblers by identifying cheats faster. That might be true, so far as it goes, but there is another side to this argument.

I came across an intriguing example of a regular person using ML to see if they could do better at the racetrack betting on the ponies (a $15 billion annual industry in the U.S.). In this example, the regular person is Craig Smith, a noted former New York Times foreign correspondent who left journalism to explore AI/ML.

To test the efficacy of ML and horse racing, he tried Akkio, a no-code ML service I've written about a few times before. His goal? To show how their approach canfoster AI adoption and how it is alreadyimproving productivity in mundane but important matters. Akkio is not designed for gambling but rather for business analysts who want insights quickly into their data without hiring developers and data scientists. Turns out it's also helpful for Smith's purposes.

So much so, in fact, that Smithdoubled his money using an ML recommendation model Akkiocreated in minutes. It's a fascinating read. It also sheds light on the dark side of ML and gambling.

In his article, Smith interviewed Chris Rossi. He's the horse betting expert who helped build a thoroughbred data system that was eventually bought by the horse racing information conglomerate DRF (Daily Racing Form). He now consults for people in the horse-racing world, including what he described as teams of quantitative analysts who use machine learning to game the races betting billions annually and making big buckssome of it from volume rebates on losing bets by the tracks who encourage the practice.

"Horse racing gambling is basically the suckers against the quants," Rossi said. "And the quants are kicking the ---- out of the suckers."

Not many years ago, sports betting sat in a legally dubious place in the U.S. Then in 2018 the U.S. Supreme Court cleared the way for states tolegalize the practice, striking down a 1992 federal law that largely restricted gambling and sports books to Nevada. That decision arrived just in the nick of time. During the pandemic, as casinos shuttered their doors and consumers looked for activities to eat up their free time, online gambling and sports betting took off. Shares of DraftKings, which went public via a SPAC merger, for instance, have risen 350% since the start of the coronavirus' spread, valuing the company at about $22 billion.

SEE:Metaverse cheat sheet: Everything you need to know (free PDF)(TechRepublic)

DraftKings has also been looking to diversify away from business that concentrates around the sports season. The online betting customer is apparently more valuable than a sports betting customer.

More recently,MGM Resorts International, a major Las Vegas player, sought to acquire Entain for about $11.1 billion in January, though the latter rebuffed the bid for being too low. Caesars Entertainment in September announced plans to acquire U.K.-based online betting business, William Hill, for about $4 billion. And to drive the point home on just how hot the space has gotten, media brand Sports Illustrated has gotten into the online sports betting space.

All of this money sits awkwardly next to rising use of ML. Yes, ML can help clean up online gambling by kicking off cheaters. But it can also be the other side of the bet you are making. As one commentatornoted, "AI can analyze player behavior and create highly customized game suggestions." Such customized gaming may make it more engaging for gamblers to keep betting, but don't think for a minute that it will help them to win. Online or offline, the house always wins. If anything, the new ML-driven gambling future just means gamblers may have incentive to gamble longer and lose more.

Could you, like Smith, put ML to work on your behalf? Sure. But at some point, the house wins, and the house will improve its use of ML faster than any average bettor can.

Disclosure: I work for MongoDB, but the views expressed herein are mine.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

Read more:

How machine learning is skewing the odds in online gambling - TechRepublic

Tzu Chi Med J. 2020 Oct-Dec; 32(4): 339343.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Received 2019 Dec 19; Revised 2020 Jan 30; Accepted 2020 Apr 9.

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

KEYWORDS: Artificial intelligence, Bioethics, Principles of artificial intelligence bioethics

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as a system with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [1].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the cognitive abilities of the natural intelligence of human minds [2].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [3].

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use AI are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [4].

In summary, we can see these different functions of AI [5,6]:

Automation: What makes a system or process to function automatically

Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing

Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate

Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines

Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [7]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [8,9]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delaysetc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [10,11]:

A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication

Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor

Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called M shape wealth distribution will be more obvious

New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller

The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [7,11]:

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [12].

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [9]. All those are the contribution of the technology of AI.

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [13].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [14]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [14]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethicsetc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [15]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [16].

The question isdo we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes truly ubiquitous, it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,. What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them [17]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

Lawful-respecting all applicable laws and regulations

Ethical-respecting ethical principles and values

Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [18].

Seven requirements are recommended [18]:

AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes

AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable

Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen

Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be understood and traced by human beings. In other words, operators should be able to explain the decisions their AI systems make

Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines

AI systems should be sustainable (i.e., they should be ecologically responsible) and enhance positive social change

AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or AI humanities. To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [19] as criteria for the computerized society to think about.

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient [20]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [20].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature

Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further

Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't explain its work may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required

Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [10]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI A European approach to excellence and trust: AI must serve people, and therefore, AI must always comply with people's rights. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market [21].

Nil.

There are no conflicts of interest.

12. Scoping study on the emerging use of Artificial Intelligence (AI) and robotics in social care published by Skills for Care. [Last accessed on 2019 Aug 15]. Available from: wwwskillsforcareorguk .

Go here to read the rest:

The impact of artificial intelligence on human society and ...

Main Examples of Artificial Intelligence Takeaways:

The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examples of artificial intelligence in our daily lives.

From Netflixs movie recommendation to Amazons Alexa, we now rely on various AI models without knowing it. In this post, well consider eight examples of how were already using artificial intelligence.

Artificial intelligence is an expansive branch of computer science that focuses on building smart machines. Thanks to AI, these machines can learn from experience, adjust to new inputs, and perform human-like tasks. For example, chess-playing computers and self-driving cars rely heavily on natural language processing and deep learning to function.

American computer scientist John McCarthy coined the term artificial intelligence back in 1956. At the time, McCarthy only created the term to distinguish the AI field from cybernetics.

However, AI is more popular than ever today due to:

Hollywood movies tend to depict artificial intelligence as a villainous technology that is destined to take over the world.

One example is the artificial superintelligence system, Skynet, from the film franchise Terminator. Theres also VIKI, an AI supercomputer from the movie I, Robot, who deemed that humans cant be trusted with their own survival.

Holywood has also depicted AI as superintelligent robots, like in movies I Am Mother and Ex Machina.

However, the current AI technologies are not as sinister or quite as advanced. With that said, these depictions raise an essential question:

No, not exactly. Artificial intelligence and robotics are two entirely separate fields. Robotics is a technology branch that deals with physical robots programmable machines designed to perform a series of tasks. On the other hand, AI involves developing programs to complete tasks that would otherwise require human intelligence. However, the two fields can overlap to create artificially intelligent robots.

Most robots are not artificially intelligent. For example, industrial robots are usually programmed to perform the same repetitive tasks. As a result, they typically have limited functionality.

However, introducing an AI algorithm to an industrial robot can enable it to perform more complex tasks. For instance, it can use a path-finding algorithm to navigate around a warehouse autonomously.

To understand how thats possible, we must address another question:

The four artificial intelligence types are reactive machines, limited memory, Theory of Mind, and self-aware. These AI types exist as a type of hierarchy, where the simplest level requires basic functioning, and the most advanced level is well, all-knowing. Other subsets of AI include big data, machine learning, and natural language processing.

The simplest types of AI systems are reactive. They can neither learn from experiences nor form memories. Instead, reactive machines react to some inputs with some output.

Examples of artificial intelligence machines in this category include Googles AlphaGo and IBMs chess-playing supercomputer, Deep Blue.

Deep Blue can identify chess pieces and knows how each of them moves. While the machine can choose the most optimal move from several possibilities, it cant predict the opponents moves.

A reactive machine doesnt rely on an internal concept of the world. Instead, it perceives the world directly and acts on what it sees.

Limited memory refers to an AIs ability to store previous data and use it to make better predictions. In other words, these types of artificial intelligence can look at the recent past to make immediate decisions.

Note that limited memory is required to create every machine learning model. However, the model can get deployed as a reactive machine type.

The three significant examples of artificial intelligence in this category are:

Self-driving cars are limited memory AI that makes immediate decisions using data from the recent past.

For example, self-driving cars use sensors to identify steep roads, traffic signals, and civilians crossing the streets. The vehicles can then use this information to make better driving decisions and avoid accidents.

In Psychology, theory of mind refers to the ability to attribute mental state beliefs, intent, desires, emotion, knowledge to oneself and others. Its the fundamental reason we can have social interactions.

Unfortunately, were yet to reach the Theory of Mind artificial intelligence type. Although voice assistants exhibit such capabilities, its still a one-way relationship.

For example, you could yell angrily at Google Maps to take you in another direction. However, itll neither show concern for your distress nor offer emotional support. Instead, the map application will return the same traffic report and ETA.

An AI system with Theory of Mind would understand that humans have thoughts, feelings, and expectations for how to be treated. That way, it can adjust its response accordingly.

The final step of AI development is to build self-aware machines that can form representations of themselves. Its an extension and advancement of the Theory of Mind AI.

A self-aware machine has human-level consciousness, with the ability to think, desire, and understand its feelings. At the moment, these types of artificial intelligence only exist in movies and comic book pages. Self-aware machines do not exist.

Although self-aware machines are still decades away, several artificial intelligence examples already exist in our everyday lives.

Several examples of artificial intelligence impact our lives today. These include FaceID on iPhones, the search algorithm on Google, and the recommendation algorithm on Netflix. Youll also find other examples of how AI is in use today on social media, digital assistants like Alexa, and ride-hailing apps such as Uber.

Virtual filters on Snapchat and the FaceID unlock on iPhones are two examples of AI applications today. While the former uses face detection technology to identify any face, the latter relies on face recognition.

So, how does it work?

The TrueDepth camera on the Apple devices projects over 30,000 invisible dots to create a depth map of your face. It also captures an infrared image of the users face.

After that, a machine learning algorithm compares the scan of your face with what a previously enrolled facial data. That way, it can determine whether to unlock the device or not.

According to Apple, FaceID automatically adapts to changes in the users appearance. These include wearing cosmetic makeup, growing facial hair, or wearing hats, glasses, or contact lens.

The Cupertino-based tech giant also stated that the chance of fooling FaceID is one in a million.

Several text editors today rely on artificial intelligence to provide the best writing experience.

For example, document editors use an NLP algorithm to identify incorrect grammar usage and suggest corrections. Besides auto-correction, some writing tools also provide readability and plagiarism grades.

However, editors such as INK took AI usage a bit further to provide specialized functions. It uses artificial intelligence to offer smart web content optimization recommendations.

Just recently, INK has released a study showing how its AI-powered writing platform can improve content relevance and help drive traffic to sites. You can read their full study here.

Social media platforms such as Facebook, Twitter, and Instagram rely heavily on artificial intelligence for various tasks.

Currently, these social media platforms use AI to personalize what you see on your feeds. The model identifies users interests and recommends similar content to keep them engaged.

Also, researchers trained AI models to recognize hate keywords, phrases, and symbols in different languages. That way, the algorithm can swiftly take down social media posts that contain hate speech.

Other examples of artificial intelligence in social media include:

Plans for social media platform involve using artificial intelligence to identify mental health problems. For example, an algorithm could analyze content posted and consumed to detect suicidal tendencies.

Getting queries directly from a customer representative can be very time-consuming. Thats where artificial intelligence comes in.

Computer scientists train chat robots or chatbots to impersonate the conversational styles of customer representatives using natural language processing.

Chatbots can now answer questions that require a detailed response in place of a specific yes or no answer. Whats more, the bots can learn from previous bad ratings to ensure maximum customer satisfaction.

As a result, machines now perform basic tasks such as answering FAQs or taking and tracking orders.

Media streaming platforms such as Netflix, YouTube, and Spotify rely on a smart recommendation system thats powered by AI.

First, the system collects data on users interests and behavior using various online activities. After that, machine learning and deep learning algorithms analyze the data to predict preferences.

Thats why youll always find movies that youre likely to watch on Netflixs recommendation. And you wont have to search any further.

Search algorithms ensure that the top results on the search engine result page (SERP) have the answers to our queries. But how does this happen?

Search companies usually include some type of quality control algorithm to recognize high-quality content. It then provides a list of search results that best answer the query and offers the best user experience.

Since search engines are made entirely of codes, they rely on natural language processing (NLP) technology to understand queries.

Last year, Google announced Bidirectional Encoder Representations from Transformers (BERT), an NLP pre-training technique. Now, the technology powers almost all English-based query on Google Search.

In October 2011, Apples Siri became the first digital assistant to be standard on a smartphone. However, voice assistants have come a long way since then.

Today, Google Assistant incorporates advanced NLP and ML to become well-versed in human language. Not only does it understand complex commands, but it also provides satisfactory outputs.

Also, digital assistants now have adaptive capabilities for analyzing user preferences, habits, and schedules. That way, they can organize and plan actions such as reminders, prompts, and schedules.

Various smart home devices now use AI applications to conserve energy.

For example, smart thermostats such as Nest use our daily habits and heating/cooling preferences to adjust home temperatures. Likewise, smart refrigerators can create shopping lists based on whats absent on the fridges shelves.

The way we use artificial intelligence at home is still evolving. More AI solutions now analyze human behavior and function accordingly.

We encounter AI daily, whether youre surfing the internet or listening to music on Spotify.

Other examples of artificial intelligence are visible in smart email apps, e-commerce, smart keyboard apps, as well as banking and finance. Artificial intelligence now plays a significant role in our decisions and lifestyle.

The media may have portrayed AI as a competition to human workers or a concept thatll eventually take over the world. But thats not the case.

Instead, artificial intelligence is helping humans become more productive and helping us live a better life.

More:

8 Examples of Artificial Intelligence in our Everyday Lives

A long-term fallout of the Covid crisis has been the rise of the contactless enterprise, in which customers, and likely employees, interact with systems to get what they need or request. This means a pronounced role for artificial intelligence and machine learning, or conversational AI, which add the intelligence needed to deliver superior customer or employee experience.

Deloitterecently analyzed patents in the area of conversational AI to assess the direction of the market -- and the technology has been developing quickly.

"Rapid adoption of conversational AI will likely be underpinned by innovations in the various steps of chatbot development that have the potential to hasten the creation and training of chatbots and enable them to efficiently handle complex requests -- with a personal touch," the analyst team, led by Deloitte's Sherry Comes, writes.

Conversational AI is a ground-breaking application for AI, agrees Chris Hausler, director of data science for Zendesk. "Organizations saw a massive 81% increase in customer interactions with automated bots last year, and no doubt these will continue to be key to delivering great experiences."

Deloitte's data from conversational AI vendors "showed that the volume of interactions handled by conversational agents increased by as much as 250% in multiple industries... Around 90% of companies mentioned faster complaint resolution and over 80% reported increased call volume processing using conversational AI solutions."

With AI-enabled messaging, "companies can be available to customers 24/7, which is especially helpful as companies experience surges in volumes," says Hausler. "AI has helped scale with businesses as they manage increases in digital interactions with customers."

There's still much work to be done, but people throughout the industry remain optimistic.

"One successful and growing area for AI is in customer service applications where AI is being used to help customer service reps be more productive and efficient. However, it's essential to have these AI avatars be as real as possible, through the creation of 'AI humans.' While conversing with AI humans has been a long-time feature of science fiction, it's now a reality, especially in customer service," saysEric Jang, CEO and co-founder ofDeepbrain AI.

The use of AI in contactless customer service "will be highly effective in delivering on the true promise of AI," according to Jang. "Due to the Covid-19 crisis, the contactless industry is growing rapidly. For traditional high touch industries, like customer service, contactless solutions must exhibit a human-like experience unlike any other technologies."

Conversational AI also is playing a role in improving the employee experience, "Additionally, AI-powered bots are the best way to ease workers' stress when they have to answer the same question over and over again," Hausler points out. "In particular, healthcare organizations were even more overwhelmed when the Covid vaccines initially became available, but they would be able to delegate frequently asked questions like, 'When can I get a vaccine?' to bots, and as a result, consumers get faster answers."

Conversational AI "can also do more complex things like schedule appointments," Hausler says. "By using automation to handle a high volume of repetitive tasks, workers can focus on the more difficult and complex tasks which makes a huge difference to organizations."

"Attempts to build general-purpose bots have generally produced poor results. Another area of innovation aims to sidestep this challenge by describing efficient methods for composing multiple specialized chatbots into an ensemble," Deloitte says. This could take the form of "an enterprise assistant with a single master interface that can route users to virtual assistant specialists for CRM, ERP, and human capital management."

Here is the original post:

AI is learning to talk back. How that's changing the customer and employee experience - ZDNet

WASHINGTON, Nov. 11, 2021 /PRNewswire/ -- These days, several sectors are investing in Top Artificial Intelligence (AI) Companies at GoodFirms to help them in their digital transformation. Today AI is everywhere and assisting businesses in various forms. It includes the digital assistants on a website chat to respond to messages quickly, track the user's journey as they navigate through the website, analyze behaviour using AI tools and much more.

List of Top AI Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce Companies at GoodFirms.

Using this AI technology, organizations can simplify several processes like extracting new insights, transforming decision making, driving improved business outcomes and creating a more efficient and profitable business. Thus, companies adopt AI technology to automate their manual and time-consuming tasks to focus on higher-value work.

Presently, many businesses seek leading AI companies to help them implement AI technology to gain a competitive benefit within e-commerce, manufacturing, human resources, accounting, customer relations, marketing and many more. Therefore, GoodFirms has unveiled the list of Top AI Companies from various industries like Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce, and Transportation.

Take a Look at List of Top AI Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce, and Transportation Companies at GoodFirms:

Top Artificial Intelligence (AI) Companies:

MobiDev, Talentica Software, Sigma Data Systems, SPEC INDIA, Avenga, 7EDGE, SoluLab, Cyber Infrastructure Inc., Redwerk.

https://www.goodfirms.co/artificial-intelligence

Best Healthcare AI Companies:

AiCure, AltexSoft, Apixio, Maxwell Plus, Arterys, Atomwise, CloudMedx, Enlitic, Turbine, Jvion.

https://www.goodfirms.co/artificial-intelligence/healthcare

Best AI Companies in Financial Sector:

Sigmoidal, Kensho, DataVisor, PROWLER.io, Zest AI, Symphony AyasdiAI, Kavout, Alpaca, Vectra, DLabs.

Story continues

https://www.goodfirms.co/artificial-intelligence/finance

Best AI Companies for Insurance Industry:

H20.ai, Azati Software Corporation, Chisel AI, Gradient AI, Avaamo, daotData, Shift Technology, Fadata, Neutrinos, OSP Labs.

https://www.goodfirms.co/artificial-intelligence/insurance

Best AI Companies for Marketing Industry:

Datorama, Avaus, MindLytiX, GumGum, Albert, NEUON AI, Amplero, Node, BrancoSoft Private Limited, Exemplary Marketing LLC.

https://www.goodfirms.co/artificial-intelligence/marketing

Best AI Companies for Manufacturing:

LeewayHertz, Citadel Analytics, World Wide Technology, 2021.AI, Uptake, Quantellix ML, Wizata, Hacarus, Emerton Data, Augmentir.

https://www.goodfirms.co/artificial-intelligence/manufacturing

Best Retail & Ecommerce AI Companies:

Redwerk, AltexSoft, Peak, Rsystems, Datamatics, Digifutura Technologies, ThoughtSpot, Unicsoft, Chop Dawg, Hey Machine Learning.

https://www.goodfirms.co/artificial-intelligence/retail-ecommerce

Best AI Companies In Transportation:

Trigent, Endive Software, TechSpeed, Space-O Technologies, Django Stars, IntelliCompute, Prakash Software Solutions Pvt. Ltd., Celadon, PerfectionGeeks Technologies, TechnoYuga Pvt. Ltd.

https://www.goodfirms.co/artificial-intelligence/transportation

Internationally recognized GoodFirms is a maverick B2B research, ratings, and reviews platform. It builds a bridge for the service seekers to associate with the most excellent partners. The research team of GoodFirms evaluates each firm through several qualitative and quantitative measures.

The research mainly includes three main factors that are Quality, Reliability, and Ability. Further, these components are subdivided into numerous metrics, such as verifying the past and present portfolio of each agency, years of experience in the expertise area, online market penetration, and reviews from clients.

Focusing on overall research, every agency is assessed and provided with a set of scores that are out of a total of 60. Hence, according to these points, all the firms are indexed in the list of top development companies, most excellent software, and varied sectors of industries.

Moreover, GoodFirms supports the service providers by asking them to engage in the research process and show evidence of their work. Thus, grab an opportunity to Get Listed for free in the list of top companies as per the categories. Obtaining the position at GoodFirms among the best service providers helps firms to expand their reach to new prospects globally, increase their productivity and sales

About GoodFirms:

GoodFirms is a Washington, D.C. based research firm that aligns its efforts in identifying the most prominent and efficient Artificial Intelligence (AI) companies that deliver results to their clients. GoodFirms research is a confluence of new age consumer reference processes and conventional industry-wide review & rankings that help service seekers leap further and multiply their industry-wide value and credibility.

Rachael Ray(360) 326-2243 rachael@goodfirms.co

Cision

View original content:https://www.prnewswire.com/news-releases/goodfirms-announces-the-list-of-top-artificial-intelligence-ai-companies-globally-for-varied-industries---2021-301421994.html

SOURCE GoodFirms

Read more here:

GoodFirms Announces the List of Top Artificial Intelligence (AI) Companies Globally for Varied Industries - 2021 - Yahoo Finance

Two researchers from Mila, the Quebec Artificial Intelligence Institute, have contributed to a major report offering specific recommendations on how AI can help mitigate climate challenges.

David Rolnick, Mila core academic member and Assistant Professor at McGill University, is one of the lead authors of the report.

Mila researcher Sasha Luccioni also contributed to the report as one of the project authors, focusing on the responsible development, deployment, and governance of AI in the context of climate change.

MONTREAL, Nov. 11, 2021 /CNW Telbec/ - A new report produced by the Centre for AI & Climate and Climate Change AI for the Global Partnership on AI (GPAI), outlines the potential for artificial intelligence (AI) to power climate action and strategy. Earlier this week, the report, Climate Change and AI: Recommendations for Government, was presented at COP26 in Glasgow, the 2021 United Nations Climate Change Conference. The report, co-authored by 15 leading AI researchers from around the world, calls for governments and policy-makers to expedite AI-driven climate solutions.

Mila (CNW Group/Mila - Quebec AI Institute)

David Rolnick, Canada CIFAR AI Chair and a Co-founder and Chair of Climate Change AI, is one of the report's lead authors alongside Climate Change AI's Priya Donti (Carnegie Mellon University, USA) and Lynn Kaack (Hertie School, Germany) and Centre for AI & Climate's Peter Clutton-Brock (Radiance International, UK). Rolnick is widely recognized within the AI community for his work at the intersection of machine learning and sustainability, being named earlier this year in the Pioneer category of MIT Technology Review's annual list of Innovators Under 35

"There are many ways that AI can be a powerful tool in enabling and accelerating climate action, from monitoring carbon stock using satellite imagery, to optimizing heating and cooling in buildings, forecasting crop yield, and helping design next-generation batteries," said Rolnick.

Story continues

Project author Sasha Luccioni focused on the responsible development, deployment, and governance of AI in the context of climate change. She is one of the leading researchers behind the recently launched website, This Climate Does Not Exist, an interactive experience that allows people to see the potential impact of climate change on any address worldwide.

The report was commissioned by the GPAI which brings together experts across multiple sectors from 18 countries and the European Union to bridge the gap between AI theory and practice by supporting AI research and applications.

Report Summary

The authors offer 48 actionable recommendations for governments with a broad array of use cases highlighted throughout the report. To support AI applications in climate change mitigation and adaptation, the report calls for governments to:

Improve data ecosystems, particularly in sectors critical to climate transition such as the energy sector.

Increase support for research, innovation, and deployment through targeted funding, infrastructure, and improved market designs.

Make climate change a central consideration in AI strategies to shape the responsible development of AI.

Support greater international collaboration and capacity building to facilitate the development and governance of AI-for-climate solutions.

Click here to view the official press release from CAIC and CCAI. The report is available here: Climate Change and AI: Recommendations for Government.See page 3 of the report for a complete list of authors and their affiliations.

SOURCE Mila - Quebec AI Institute

Cision

View original content to download multimedia: http://www.newswire.ca/en/releases/archive/November2021/11/c0397.html

Read more from the original source:

Artificial Intelligence in the Fight against Climate Change: A new report presented at COP26 - Yahoo Finance