Artificial intelligence is defined as systems that do not operate according to a designed algorithm but are able to learn from new data. The fact that European policymakers have turned their eyes to the challenges of applying AI technologies is an important step forward, according to Jokūbas Drazdas, director of UAB Acrux Cyber Service, a Lithuanian IT company specialising in AI and cyber security. Europe is lagging far behind the US and China in the development and deployment of AI. In 2020, only 7% of European companies were using AI systems. The US and China are currently trying to accelerate the use of AI in the public and private sectors.

On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC defines AI as "any method for programming computers or products to enable them to carry out tasks or behaviors that would require intelligence if performed by humans" and machine learning as "an iterative process of applying models or algorithms to data sets to learn and detect patterns and/or perform tasks, such as prediction or decision making that can approximate some aspects of intelligence."3 To inform the ongoing discussion on how to regulate AI, machine learning, and related technologies, the CPSC provides the following list of considerations: Do AI and machine learning affect consumer product safety? Do AI and machine learning affect consumer product safety? UL 4600 Standard for Safety for the Evaluation of Autonomous Products covers "fully autonomous systems that move such as self-driving cars along with applications in mining, agriculture, maintenance, and other vehicles including lightweight unmanned aerial vehicles."5

On May 12, 2022, the U.S. Equal Employment Opportunity Commission (EEOC) issued a "Technical Assistance" (TA) document addressing compliance with …

Ryan N. Phelan is a registered patent attorney who counsels and works with clients in all areas of intellectual property (IP), with a focus on patents. Clients enjoy Ryan's business-focused approach to IP. With a MBA from Northwestern's Kellogg School of Management, Ryan works with clients to achieve their business objectives, including developing and protecting their innovations and businesses with IP.

C3.ai (NYSE:AI) is a leading software company, which provides Artificial Intelligence services to enterprises. The company is poised to ride the wave of growth forecasted for AI. The global Artificial Intelligence (AI) market is forecasted to grow at a meteoric 20.1% CAGR from $387 billion in 2022 to over $1.3 trillion by 2029. C3.ai serves an envious list of large reputable customers from The US Air Force and the Department of Defence, to large energy companies such as Shell & Engie. They have been growing revenues at a 40% CAGR over the past couple of years, while the stock price has declined massively.

The pandemic accelerated the advancement of artificial intelligence (AI) in remote patient care. Physicians are increasingly using digital patient monitoring to better track health data, identify abnormalities, and provide patient-specific treatment -- all without the need for in-person interaction. Additionally, emergency departments are employing remote monitoring solutions to allow some patients to leave the hospital sooner. These transformative technologies are leading to better outcomes for patients and reduced healthcare costs. AI use cases continue to grow in healthcare, as constant learning and training of algorithms results in smarter technology as well as improved patient experiences. Most AI applications in healthcare use "augmented intelligence," which curates the algorithms' output to provide clinicians with direction on "where to look" when they get the analysis.

In the second of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in France. What is France's strategy for Artificial Intelligence? The French president, Emmanuel Macron, announced in March 2018 his ambition for France to become a global leader of the artificial intelligence (AI) ecosystem. The first phase of the National Programme included an initial investment of €1.5 billion into the creation of a network of interdisciplinary institutes dedicated to artificial intelligence (the "3IA" institutes) and the financing of multiple AI projects overseen by Bpifrance. The second phase will provide for €2 billion of private and public funding to attract and train new talent.

US AI guidelines are everything the EU's AI Act is not: voluntary, non-prescriptive and focused on changing the culture of tech companies. As the EU's Artificial Intelligence (AI) Act fights its way through multiple rounds of revisions at the hands of MEPs, in the US a little-known organisation is quietly working up its own guidelines to help channel the development of such a promising and yet perilous technology. In March, the Maryland-based National Institute of Standards and Technology (NIST) released a first draft of its AI Risk Management Framework, which sets out a very different vision from the EU. The work is being led by Elham Tabassi, a computer vision researcher who joined the organisation just over 20 years ago. Then, "We built [AI] systems just because we could," she said.

The novelty cheque has long been a mainstay of the political "photo op" but a Guardian Australia analysis of photos posted during the 2022 and 2019 election campaigns suggests giant cheques are on the way out, while hi-vis workwear and photos of dogs are on the rise. During our work building the automated systems behind the pork-o-meter, which tracks election campaign pork barrelling as it occurs, the Guardian's data team found ourselves asking an important question. Could we teach a robot to spot photos of novelty cheques? We were already using machine learning to flag text from politicians' Facebook posts as likely grant announcements and election promises, but having another model in place to find big cheques and certificates in photos might pick up things we'd missed in the text.

Speed has always been a critical success factor in winning wars on the battlefield. You need to move troops faster, reach targets more quickly, and strike with speed and precision. However, what is often not talked about is how the speed with which decisions are made plays a role in claiming victory. Alexander the Great's success on the battlefield is often credited to the rapid decision-making capabilities of his armies. Enabled by trust and a decentralized command structure, his troops were able to beat their enemies by "out-decisioning" them.