Category Archives: Artificial Intelligence

What GAO Found

The Department of Defense (DOD) is actively pursuing artificial intelligence (AI) capabilities. AI refers to computer systems designed to replicate a range of human functions and continually get better at their assigned tasks. GAO previously identified three waves or types of AI, shown below.

Types of Artificial Intelligence (AI) and Associated DOD Examples

DOD recognizes that developing and using AI differs from traditional software. Traditional software is programmed to perform tasks based on static instructions, whereas AI is programmed to learn to improve at its given tasks. This requires large data sets, computing power, and continuous monitoring to ensure the capability performs as intended. The majority of AI capabilities that support DOD's warfighting mission are still in development. These capabilities largely focus on analyzing intelligence, enhancing weapon system platforms such as aircraft and ships that do not require human operators, and providing recommendations on the battlefield (such as where to move troops).

When acquiring new capabilities that depend on complex software, DOD has historically faced challenges, such as long acquisition processes and a shortage of skilled workers. GAO found that it continues to face these challenges along with others specific to AI, including having usable data available to train the AI. For example, AI for detecting an adversary's submarines requires gathering many images of various submarines and labeling them so the AI can learn to identify one on its own. DOD also faces difficulties integrating trained AI into existing weapon systems that were not designed for it and building trust in AI among its personnel. DOD initiated a variety of effortssuch as establishing a cross-service digital platform for AI and AI-specific trainingsto address these challenges and support its pursuit of AI, but it is too soon to assess effectiveness.

DOD has reported that AI is poised to change future battlefields and the pace of threats the U.S. faces. AI capabilities could enable machines to perform tasks that usually require human intelligence, such as identifying potential threats or targets on the battlefield. DOD designated AI a top modernization area and is investing heavily in AI tools and capabilities. Other nations are making significant investments in this area that threaten to erode the U.S. military technological and operational advantage.

The National Security Commission on Artificial Intelligence concluded in its March 2021 report that the U.S. needs to act quickly to ensure AI readiness. AI experts from inside and outside DOD agree that ensuring the department has the necessary infrastructure in place will be essential to developing, acquiring, and scaling AI for weapon systems effectively.

Senate Report 116-236 includes a provision for GAO to review DOD's AI warfighting acquisition-related efforts. This report examines (1) the unique nature of AI and current status of AI capabilities that support weapon systems, and (2) how DOD is addressing challenges in developing, acquiring, and deploying AI capabilities for weapon systems.

To do this work, GAO interviewed officials from over 20 DOD entities and reviewed DOD documentation as well as reports and recommendations from the National Security Commission on Artificial Intelligence, among others.

For more information, contact Jon Ludwigson at (202) 512-4841 or ludwigsonj@gao.govor Candice N. Wright at (202) 512-6888 or wrightc@gao.gov.

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Artificial Intelligence: Status of Developing and Acquiring Capabilities for Weapon Systems - Government Accountability Office

While future developments in artificial intelligence research are likely to yield useful industrial applications, in the medium term the technology may turn out to be less revolutionary than many expect.

Artificial intelligence (AI) can beat humans at complex tasks like chess and video games, but it still cannot reproduce behaviors that come naturally to people, like making small talk about the weather.

In his 1994 book The Language Instinct, linguist and cognitive scientist Steven Pinker concluded that: The main lesson of thirty-five years of AI research is that the hard problems are easy, and the easy problems are hard.

This paradox has led researchers to divide AI into two different types: artificial general intelligence or strong AI, and weak (or narrow) AI.

Strong AI means the ability to learn any task that people can perform. In contrast, weak AI is not intended to have cognitive abilities; it is a program designed to solve a single problem, like computers that play chess.

There are already several ways to program AI, like machine learning, deep learning or artificial neural networks. Programming, however, does not equal intelligence it is only part of the input necessary to generate intelligence. Advances in programming have led to better and broader applications of weak AI. For example, Googles AlphaGo is designed to play the board game Go using a mix of deep learning and statistical simulations. While it can outperform humans at the game, it cannot do any other task.

Despite the growing sophistication of programming, strong AI is slow to develop.

Despite the growing sophistication of programming, strong AI is slow to develop. The bar for intelligence can be set at different levels: sentience, conscience, acting as if understanding or merely interacting. The lowest benchmark is coping with a task involving unforeseen parameters, like driving a car. And even by this measure, artificial general intelligence still has a long way to go.

Driving is relatively easy: it involves anticipating and executing different moves. However, the driver also needs two additional abilities: estimating physical interactions and predicting human movements. In other words, a driver needs intuitive physics and psychology.

Intuitive physics is the human capacity to understand how objects interact. Researchers believe that people have an instinctive knowledge of physics that allows them to navigate the world and make predictions even in entirely new situations. AI, however, currently cannot reproduce this behavior. Different experiments have shown that it fails to predict and react to physical interactions within a 200-meter radius.

Intuitive psychology is the ability to gain insight into the motives of animate agents and to make predictions based on inferences. This does not mean reading minds, but rather understanding that other people have mental states like goals and beliefs. This intuition allows humans to anticipate other peoples actions and to plan their reactions. Here too, AI struggles. In this area, many animals perform much better than AI at this stage.

A self-driving car requires both of these skills. It must be able to judge whether it is safe to drive over an object on the road to evaluate whether something is a plastic bag or a brick. And it must be able to anticipate the intentions of human drivers whether a car is only trying to pass by or whether it is out of control and might collide. And even in an environment where all drivers are automated, the issue of predicting physical interactions remains.

It is possible that part of human knowledge is simply not transferable to machines.

Intuitive physics and psychology come easily to humans because they are acquired not through conscious learning but as a result of adaptive behavior. It is difficult to teach AI similar skills because there is no learning program for them. Researchers struggle to reproduce the adaptive behavior process that creates these abilities for the sake of teaching AI. And it is still unclear whether this is the right path maybe AI needs a different method to acquire these competencies.

Some analysts even claim that it is impossible for AI to learn these abilities because AI is not human and will therefore never be able to act and behave like people. AI, as its name indicates, is artificial. All it knows is, in principle, programmed by humans. And it is possible that part of human knowledge is simply not transferable to machines.

There are three basic scenarios for the medium-term development of AI. A first base scenario can be combined with either of the other two.

The base scenario is that the development of weak AI will continue. Programming will lead to increasingly refined single-task feats. This type of AI can be used in manufacturing, healthcare, administration and some services, but its constraints are apparent. Once it is dedicated to a function, it is not able to perform another. And it can only perform tasks with limited scope for unforeseen factors. Still, weak AI can free up human labor capabilities and work more precisely. It can also potentially lower costs.

Building on the base scenario, one possibility is the development of strong AI in an upward concave curve. The first developments would make larger progress than subsequent ones. There would be a diminishing marginal increase of benefits to strong AI innovation with time. This scenario would confirm the claim that there are limits to what machines can learn. If there is a fundamental difference between human and nonhuman intelligence, then strong AI will develop only to a point. This is not necessarily bad news. While teaching computers, scientists could find out a lot about human intelligence and discover new programming principles. These benefits could make it worth pursuing artificial general intelligence even if research falls short of creating strong AI. It could also lead to humans specializing in some areas and machines in others, which would bring welfare gains.

Strong AI could also develop in an upward convex curve. The first steps would be arduous but later developments would accelerate. In this scenario, there would be no difference between human and machine intelligence, and therefore no limitations to what strong AI can learn. Coming up with the first building blocks of artificial general intelligence is difficult because it involves discovering new programming principles and new hardware. But once those are in place, innovation could speed up. During the Industrial Revolution, building the first machines and connecting them to a power grid was difficult. But once the technology became widespread, new inventions multiplied. In this scenario, strong AI could usher in a new way of organizing human life most likely for the better.

Hypothetically, research in AI could implode because of government intervention or spiraling costs. This is, however, extremely unlikely. Even if governments curb AI research, the potential rewards are high enough to attract resources.

It is also extremely unlikely that the singularity would occur, with artificial agents becoming more intelligent than humans and ultimately taking over. Even if artificial intelligence were to surpass human capabilities, machines would not necessarily conquer the world. Intelligence is only one aspect of life some people are more intelligent than others and it does not determine social order.

Overall, AI and its implementations hold much promise. However, its proponents have been overpromising. Weak AI is the frontier of innovation at the moment, and the technology has more benefits than its name implies. Strong AI is more science fiction than reality for now. It is not even clear whether it can ever become reality. But this, too, is not bad news. Researching strong AI can still lead to many beneficial innovations.

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The artificial intelligence of the future - Geopolitical Intelligence Services AG

SOME OF Africas leading female business leaders will gather in Johannesburg next month for a special conference examining the future role that artificial intelligence (AI) will play in the continents economy.

What Women CEOs in Africa Need to Know about Artificial Intelligence,will feature global experts who will share insights on how the latest developments in AI are likely to affect some of Africas key industries such as financial services, telecoms, natural resources, agriculture, and technology.

The conference will be hosted by Harvard Business School professor Karim Lakhani and Tsedal Neeley, also a professor at Harvard Business school who will give a presentation on how to get large organisations to adopt an AI mindset.

The event, which will take place on March 8 to mark International Women Day, will also see the launch of Africa.coms Definitive List of Women CEOs. The list highlights the women who lead corporations in Africa with over $100 million in revenue based on data provided by Bloomberg.

Among the high profile speakers at the event are Lillian Barnard, Managing Director, Microsoft, South Africa; Brenda Mbathi, President, GE East Africa; Aida Diarra, Head of Sub-Saharan Africa, VISA; Amelia Beattie, Chief Executive, Liberty Two Degrees and Helene Echevin, CEO, CIEL Healthcare.

Africa.com ChairTeresa Clarke said: We are thrilled to empower African women CEOs by providing them with a leading edge understanding of how AI is changing business throughout the world. We expect that African male CEOs will also join this event to take advantage of the valuable content being presented.

A 2019 study from the Boston Consulting Group found that in Africa alone, data-driven online marketplaces that rely on AI in fields such as fintech and e-commerce could create 3 million jobs by 2025, while dramatically expanding access to goods and services across the region.

Some of the leading players in the global tech sector are already positioning themselves to tap into Africas digital potential.

In 2019 Google openedits first Africa-based artificial intelligence research center in Ghana.

Other big tech companies such as IBM and Microssoft have also committed to developing Africas talent and contributing to its digital growth.

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Africa's women business leaders to examine the future role of artificial intelligence - The Voice Online

Advanced forms of artificial intelligence may already be displaying glimmers of consciousness, according to leading computer scientists.

MIT researcher Tamay Besiroglu joined OpenAI cofounder Ilya Sutskever in warning that some machine learning AI may have achieved a limited form of sentience, sparking debate among neuroscientists and AI researchers.

It may be that todays large neural networks are slightly conscious, tweeted Mr Sutskever, who co-founded OpenAI alongside tech billionaire Elon Musk.

The comment drew a strong response from leaders in the field, including Professor Murray Shanahan from Imperial College London, who said: In the same sense that it may be that a large field of wheat is slightly pasta.

Mr Besiroglu, defended Dr Sutskevers idea, claiming that such possibilities should not be derided or dismissed.

Seeing so many prominent machine learning folks ridiculing this idea is disappointing, he tweeted. It makes me less hopeful in the fields ability to seriously take on some of the profound, weird and important questions that theyll undoubtedly be faced with over the next few decades.

Attempts to define consciousness have divided neuroscientists and philosophers for centuries, though a broad way of describing it is as a narrative constructed by our brains, capable of perception through senses and imagination. Some definitions also include the ability to experience positive and negative emotions, like love and hate.

A recent study attempted to track advances in machine learning over the last decade, showing a clear trend in major advances in vision and language

One of the authors of the research was Mr Besiroglu, who drew a line across across a trend graph in a tongue-in-cheek attempt to classify which leading AI algorithms could be considered to have some form of consciousness.

OpenAIs sophisticated text generator GPT-3 was placed in the maybe slightly conscious category, as well as AlphaGo Zero, developed by Googles DeepMind AI division.

MIT researcher Tamay Besiroglu

(Tamay Besiroglu/ MIT)

I dont actually think we can draw a clear line between models that are not conscious vs. maybe slightly conscious. Im also not sure any of these models are conscious, Dr Besiroglu told Futurism.

That said, I do think the question could be a meaningful one that shouldnt just be neglected.

OpenAI CEO Sam Altman offered his thoughts on his companys most powerful AI, tweeting: I think GPT-3 or -4 will very, very likely not be conscious in any way we use that word. If they are, its a very alien form of consciousness.

The prospect of artificial consciousness rather than simply artificial intelligence raises ethical and practical questions: If machines achieve sentience, then would it be ethically wrong to destroy them or turn them off if they malfunction or are no longer useful?

Jacy Reese Anthis, who researches technology and ethics, described such a dilemma as one of the most important questions for the long-term future.

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Artificial intelligence may already be slightly conscious, AI scientists warn - The Independent

University of Birmingham Dubai invites applications forBSc and MSc in Artificial Intelligence and Computer Science

The University of Birmingham, Dubai, is inviting applications for its BSc and MSc courses in Artificial Intelligence and Computer Science.

Artificial intelligence (AI) is a multidisciplinary field that connects computing, psychology, neuroscience, philosophy, mathematics and linguistics. Using state-of-the-art facilities in the new, technology enhanced 'smart' campus, the degree programmes provide students with computing skills to enter industry, while also allowing them to acquire scientific skills in order to pursue research. The wide range of modules focus on areas of AI that interest students the most and will help kick-start a great career.

Both the programmes by the university have been designed in such a way that people even from a variety of academic backgrounds can get upto speed with AI and computer science without any issues.

University of Birmingham Dubai graduates are highly sought after by leading industry players, and the range of job opportunities available to them are infinite.

Programme delivery: With teaching being a joint exercise between academics on Dubai campus and Edgbaston Campus, the programmes aim to provide students with world class education. Students also have the opportunity to interact and work alongside students in the Edgbaston campus in the UK whilst they study at the Dubai campus. The BSc programme is a three-year UAE Ministry of Education accredited Computer Science programme making The University of Birmingham, the only British institution in Dubai offering three year courses. The MSc programme is the standard international course spanning across one year.

Course date:September 2022

Eligibility:

Artificial Intelligence and Computer Science BSc students with 75% from Class XII from ISC, CBSE, Maharashtra and West Bengal State Board or 80% from other state boards will be eligible to apply.

Mathematics: This course also requires students to have 85% or above in Mathematics in Class XII. If students do not meet the entry requirements for maths, they may be eligible to take a maths entry test.

Artificial Intelligence and Computer Science MScafour-year or a three-year bachelors degree in any subject other than computing with 55-60% from a recognised institution in India. Applicants can be considered on a case-by-case basis.

Application process and admission details:Applications for September 2022 are open and there is no application fee. To submit an online application, please click on the Apply Now button on the course page.

Link -https://www.birmingham.ac.uk/dubai/departments/computer-science/index.aspx

The following information will usually be needed before a decision can be made on an application:

One academic reference (or if appropriate to the programme applied for, it could be from applicants employer).

Academic transcript(s) for all prior degrees - originals or certified photocopies.

Personal Statement, approximately 5000 characters, explaining why you are interested in studying on your chosen programme.

Certificate to show competency in the English language if English is not your first language and you have already taken a test (not mandatory to submit at the time of application). Students can be considered for a waiver.

Applicants may be required to submit further documents in support of their application.

Tuition:

BSc Artificial Intelligence and Computer Science AED 120,294 (Approx. 24,162 ) per year.

MSc Artificial Intelligence and Computer Science AED 130,270 (Approx. 26,165) for 1 year full-time.

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BSc and MSc in Artificial Intelligence and Computer Science - The Tribune

BOSTON, Feb. 18, 2022 /PRNewswire-PRWeb/ --ODSC is pleased to announce that its AI Expo & Demo Hall will be returning this April 19th-21st both in-person in Boston and virtually. Co-located with ODSC East 2022, this event is solution-focused, allowing decision-makers to learn more about what's trending in data science and artificial intelligence in 2022, AI solutions, and machine learning platforms and services. Attending this event is now free, granting attendees access to all partner booths, keynotes, and demo talks.

The AI Expo is open to all. The theme for the 2022 AI Expo & Demo Hall is Build AI Better. While many companies are already exploring the AI space, the AI Expo & Demo Hall provides a more interactive opportunity for attendees to learn how AI can improve ROI. Focus areas include machine learning, deep learning, NLP, and MLOps/data engineering. New tracks for 2022 include Responsible AI, AI Safety, AI for Biotech & Pharma.

Attendees will be able to hear from a series of keynote speakers. This year, keynotes include Padhraic Smyth of UC Irvine, Hilary Mason of Hidden Door, Luis Vargas of Microsoft, and Dr. Hari Bhaskar and Jean-Rene Gauthier of Oracle. Additionally, attendees can benefit from a number of extra events, including a networking reception, the AI Startups Showcase, the Women in Data Science Ignite session, and the AI Investors Reverse Pitch.

A key part of the AI Expo & Demo Hall is the attending vendors. This year, a few standout companies include Oracle, Microsoft Azure, Z by HP, SAS, Neo4j, iterative, Cloudera, and Iguazio. More than 40 companies will be participating this year. In the expo hall, partners will provide thought leadership and insights into current developments of data science and artificial intelligence, as well as provide examples of their most recent work. In-person and virtual offerings will differ, encouraging attendees to experience each option.

Interested attendees can register for their free ticket by going to the ODSC East 2022 AI Expo & Demo Hall page here, and experience the event across three days from April 19th-21st.

More on ODSC:

Open Data Science Conference (ODSC) is the leader of applied data science conferences. Our conferences bring industry leaders, key executives, start-up companies, engineers, and investors on the threshold of innovation together.

Media Contact

Alex Landa, ODSC, 1 8454712635, alex.l@odsc.com

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ODSC Announces Artificial Intelligence Expo is Back In-Person with Focus on MLOPs, AI Safety, and Responsible AI - WFMZ Allentown

Just what you've always wanted: A computer that falls in love with you. As refinements in artificial intelligence continue to progress the technology upward and out of the uncanny valley, getting an AI to interact with humans in a way that feels, well, human has remained one of the elusive milestones in earning it widespread practical adoption.

That all (quite literally) sounds like it's changing, and after hearing the wispy, yearning voice that AI startup Sonantic has crafted from nothing but ones and zeroes, were not sure were ready. To commemorate Valentines Day, the company shared a lengthy audio clip that shows off the emotive power of its AI-enabled voice 'bot. While it's still just working off a pre-programmed script, lets just say the gap between human and AI expression is closing fast.

Check it out:

As Sonantics cooing AI shows, were not dealing with some 1980s-vintage sci-fi cyborg that pines for human company in a cold metallic voice. The fact that it sounds so real is what weirds us out the most, in fact: If this bot were dishing out such convincing TMI on the other end of a phone call, how would you even know you werent talking to a machine?

Maybe were just hung up on love in particular, because its just one of many human emotions Sonantics AI is designed to persuasively vocalize. Speaking with The Verge, company CEO Zeena Qureshi described its algorithm tech as Photoshop for voice, capable of emulating a range of feelings from anger and fear to happiness and exuberant joy.

Though other companies are pursuing their own versions of AI voices that mimic people, co-founder John Flynn told The Verge that Sonantics technology has achieved an extra measure of user-controlled customization that helps it stand out. [O]ur research goal was to see if we could model subtle emotions. Bigger emotions are a little easier to capture, he said. I think thats the main difference our ability to direct and control and edit and sculpt a performance.

The company reportedly took inspiration for its romantic robo-voice from Samantha, the Scarlett Johansson-portrayedAI assistant in Spike Jonzes Her that main character Theordore (Joaquin Phoenix) falls in love with and for now, entertainment and marketing remain the primary fields where the tech is likely to be used. Our clients are mostly AAA game studios, entertainment studios, and were branching out into other industries, Flynn said. We recently did a partnership with Mercedes [to customize its in-car digital assistant] earlier this year.

Hey, its tough to find true love in a world that moves as fast as ours. And just like Hers Theodore, there are probably real people out there whore ready to fall for an all-too-real-sounding robot. We just hope Sonantics next-level techthe first AI that can flirt, as the company puts it doesnt end up getting its own heart broken in the process.

The rest is here:

This cutting-edge, emotional AI voice can flirt and say I love you - and sounds a little too real - Syfy

In brief

In the not too distant past, many were convinced that Artificial Intelligence (AI) or Machine Learning (ML) would not substantially change the practice of law. The legal profession was considered to be by its very nature requiring specialist skills and nuanced judgment that only humans could provide and would therefore be immune to the disruptive changes brought about by the digital transformation. However, the application of ML technology in the legal sector is now increasingly mainstream, particularly as a tool to save time for lawyers and provide a richer analysis of ever-larger datasets to aid legal decision-making in judicial systems throughout the world.

One key area of ML application in judicial systems is in predictive justice. This involves using ML algorithms that perform a probabilistic analysis of any given particular legal dispute using case law precedents. In order to work correctly, these systems must rely on huge databases of previous judicial decisions which have to be translated into a standardized language that, in turn, is able to create predetermined models. These will ultimately help the machine learning software generate the prediction.

Does this technology mean trials ending at the speed of the light, lawyers being able to know in advance whether or not to start a lawsuit, courts immediately deciding a case? Well, there is still a long way to go and we need to also balance the risks inherent to the use of these technology tools. For example, the data used to train the ML system could result in bias and consolidate stereotypes and inequality that would be validated merely because they were produced several times by the AI. Watch out, then, for possible added complexity in creating new precedents and case law against all the odds!

To assess the opportunities and challenges brought about by predictive justice systems using ML tools, it is instructive to look at case law examples, as often history is a proxy to understand the future.

The first time predictive justicestarted to see the light was in the United States way back in 2013 inState v. Loomiswhere it was used by the court in the context of sentencing. In that case, Mr. Loomis, a US citizen, was charged with driving a car in a drive-by shooting, receiving stolen goods and resisting arrest. During the trial, the circuit court was assisted in its sentencing decision by a predictive machine learning tool and the ultimate result was the judge imposing a custodial sentence. Apparently, the judge was convinced by the fact that the machine learning software tool had suggested there was a high probability that the defendant would re-offend in the same manner.

On appeal, theSupreme Court of Wisconsinaffirmed the legitimacy of the software as the judge would have reached the same result with or without the machine learning software. The decision included finding that the risk assessment provided by the AI software, although not determinative in itself, may be used as one tool to enhance a judges evaluation, weighing in the application of other sentencing evidence when deciding on appropriate sentencing for a defendant.

In essence, the Supreme Court of Wisconsin recognized the importance of the role of the judge, stating that this kind of machine learning software would not replace their role, but may be used to assist them. As we can imagine, this case opened the door to a new way of delivering justice.

Indeed, fast forward to today and we read about news from Shanghai telling us the story of the first robot ever created to analyze case files and charge defendants based on a verbal description of the case. AI scientists honed the robot using a huge amount of cases so that the machine would be able to identify various types of crimes (i.e., fraud, theft, gambling) with a claimed 97% accuracy.

AI-based predictions used to assist the courts are increasingly prevalent and may raise significant concerns (including bias and transparency). Several regulatory authorities are cooperating to advance a set of rules, principles and guidance to regulate AI platforms in judicial systems and more generally.

For example, in Europe, a significant step towards digital innovation in judicial systems was taken with the creation of the European Commission for the Efficiency of Justice (CEPEJ) which published the European Ethical Charter on the use of Artificial Intelligence in judicial systems and their environment, one of the first regulatory documents on AI (Charter). The Charter provides a set of principles to be used by legislators, law professionals and policymakers when working with AI/ML tools aimed at ensuring that the use of AI in judicial systems is compatible with the fundamental rights, including those in theEuropean Convention on Human Rightsand theConvention for the Protection of Individuals with regard to Automatic Processing of Personal Data.

Recently, CEPEJ has laid down its 2022 to2025 Action plan for the Digitalisation for a better justiceidentifying a three-step path aimed at guaranteeing a fair use of AI in courts as per the visual representation below:

Source:EUROPEAN COMMISSION FOR THE EFFICIENCY OF JUSTICE(CEPEJ) Revised roadmap for ensuring an appropriate follow-up of the CEPEJ Ethical Charter on the use of artificial intelligence in judicial systems and their environment.

CEPEJs commitment does not stop there. Indeed, the table below shows a glimpse of how information technology tools are catching on in the judicial systems of the EU Member States (civil and criminal) and how the use of IT in EU courts is accelerating.

Source:Dynamic database of European judicial systems.

More broadly, the European Commission is currently focused on developing a set of provisions to regulate AI systems which are outlined in a draftAI Regulation(Regulation) published in 2021. The Regulation proposes harmonized rules for applications of AI systems. It follows a proportionate risk-based approach differentiating among prohibited, high-risk, limited and minimal-risk uses of AI systems. Regulatory intervention, therefore, increases along with the increase in the potential of algorithmic systems to cause harm. For more, see our alertNew Draft Rules on the Use of Artificial Intelligence.

AI systems used for law enforcement or in the administration of justice are defined as high-risk AI systems under the Regulation. Note that the use of real-time biometric identification systems in public places by law enforcement is (subject to certain exceptions) prohibited. High-risk AI systems are subject to requirements, including ensuring the quality of data sets used to train the AI systems, applying human oversight, creating records to enable compliance checks and providing relevant information to users. Various stakeholders, including providers, importers, distributors and users of AI systems, are subject to individual requirements, including in relation to compliance of the AI systems with the requirements of the Regulation and CE marking of such systems to indicate conformity with the Regulation.

The Regulation has still a long way to go before being finally approved and becoming binding on Member States, but it is already a step forward in regulating AI not only as it may be used in the administration of justice, but as it may also impact deeply on the way we work, communicate, play, live in the digital era.

Camilla Ambrosinohashelped in preparing this editorial.

This article was originally published in theJanuary 2022 edition of LegalBytes.

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International: Artificial Intelligence in the administration of justice - GlobalComplianceNews

Overview

If you wonder if there is a relationship between Artificial Intelligence and cryptocurrency, then you must know that the crypto market uses Artificial Intelligence in various ways. Due to the use of AI, the crypto market can operate 24X7, and for that, the traders or investors can keep a watch on the price all the time. It creates a large amount of data for AI to examine in order to estimate future prices using back-data discoveries such as analyzing the market price. As a result, crypto AI can assist in more accurate price predictions. Because it eliminates the danger of human error while calculating, they are also faster predictions.

Nonetheless, if you want to start your crypto journey, we recommend Bitcoin Era Software. This crypto exchange platform uses advanced AI technology to target the most possible profitable crypto coins.

Moreover, if you want to know the role of artificial intelligence in the crypto market, then our blog post will be helpful for you. In the below section, we have discussed how artificial intelligence is used in cryptocurrency trading. Along with that, we have also highlighted how AI is benefiting the crypto market.

The role of artificial intelligence in crypto trading

Artificial intelligence helps the crypto market create patterns in different dimensions that can determine the pricing and volume of the crypto market. Along with that, AI can also help analyze the publics sentiment ( the market demand) or the blockchain-related data, such as speed of mining, movement of the crypto coins, the volume of transactions made by the public, and others. The AI can help you to analyze all the key aspects that are required for making profitable trading in the crypto market.

Benefits of using Artificial Intelligence

Now when you know what role AI plays in crypto trading, lets discuss the benefits of using AI. The following are some of the benefits that you get from the use of AI in the crypto market:

In a nutshell

Artificial intelligence has a significant role in the crypto market, and it can be beneficial for both the market and the public. Thus, if the crypto market starts using more advanced algorithms, then the crypto market can get more growth in the digital financial market.

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How Artificial Intelligence is used in The Crypto Market - Deadline News

The proportion of beverage companies hiring for Artificial Intelligence-related positions rose significantly last month, according to recent research, with 41.2% recruiting for at least one position in the area.

The figure, which features in GlobalDatas latest analysis, represents an increase on the 31.2% of companies recruiting for AI positions in beverages in January 2021. The rate in December last year was also lower, at 36.4%.

Of all advertised vacancies in the global beverage industry, 1.3% were linked to AI in January, a decline on the 1.6% proportion in the same month of 2021.

The analysis shows that beverage companies are currently hiring for Artificial Intelligence jobs at a lower rate compared to the average for all companies within GlobalData's job analytics database. The average among all companies stood at 2.9% for the month.

Artificial Intelligence is one of the topics that GlobalData has identified as a key disruptive force facing industry in the coming years. Companies that are investing in these areas now are considered to be better prepared for the future business landscape.

GlobalData's job analytics database tracks the daily hiring patterns of companies worldwide, drawing in vacancies as they're posted and tagging them with additional layers of data, from the seniority of each position to whether a job is linked to wider industry trends.

The themes set to shape 2022 for alcohol brand owners Click here for a Just Drinks focus

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Artificial Intelligence hiring levels on the rise in beverages - data - just-drinks.com