Only after she spoke with a therapist did she understand the problem: she was burnt out. She ended up taking medical leave because of stress. Mitchell, who now works as an AI researcher and chief ethics scientist at the AI startup Hugging Face, is far from alone in her experience. Burnout is becoming increasingly common in responsible-AI teams, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI consultant at Boston Consulting Group. Companies are under increasing pressure from regulators and activists to ensure that their AI products are developed in a way that mitigates any potential harms before they are released.

There's little question artificial intelligence and machine learning are playing an increased role in making business decisions. A 2022 survey of senior data and technology executives by NewVantage Partners found that 92% of large companies reported achieving returns on their data and AI investments -- an increase from 48% in 2017. But as these technologies enter the mainstream, new issues arise: How will they change the nature of workflow and workplace connection? Will they be ethically harnessed? Here's what to consider as AI and machine learning become omnipresent, according to MIT Sloan researchers, visiting scholars, and industry experts.

Sarah Guo, founder of venture capital firm Conviction, organized a buzzy salon at a posh bar in San Francisco last week that drew an animated crowd of engineers, entrepreneurs, and financiers. Guo's event was just one of several held last week in San Francisco by investors and technologists excited by the commercial potential of what has been dubbed "generative AI." Her guests included AI engineers from large tech companies, fellow investors, and entrepreneurs building businesses powered by recent advances in algorithms that generate text or images. One of the guests of honor was Clement Delangue, CEO of Hugging Face, a company that hosts a number of open source generative AI projects, including one that recently sparked a frenzy of AI memes. He answered questions from engineers thinking about jumping onto the bandwagon with generative AI startups of their own. "It's just the hottest area from a fundraising perspective right now," Guo says.

The company's previous demo had involved marching a human out in a robot-like body suit, so when Optimus walked slowly around the stage, it was met with delight from the cheering crowd. Despite the show's futuristic framing, robotics experts were mostly underwhelmed by the reveal. Optimus' clunky attempts at something like a dance seemed less advanced than other humanoid robots, such as Honda's Asimo, which played soccer with former President Barack Obama back in 2014. Tesla engineers boasted that Optimus' hand had as many as 11 degrees of freedom (that's to say, all the ways in which robotic parts can bend). In comparison, a robotic hand designed by a Japanese engineer back in 1963 had 27. What is it about Optimus that makes us feel threatened?

In this #4 post of the Symposium "Hitchhikers Guide to Law & Tech", Nathalie Smuha and Anna Morandini continue asking fundamental questions on the interaction between regulation and technology. Can the European AI Act mitigate the ethical and legal concerns raised by this hyped technology? Which trail is the EU blazing to secure "Trustworthy Artificial Intelligence" in Europe, as distinct from the laissez-faire approach in the US and the state-centric approach in China? In this post, both authors unpack the proposed AI regulation and evaluate its merits and pitfalls. After explaining the build-up towards the proposal, they set out the scope of the Act and its four categories of risks as part of a "risk-based approach" to regulate AI.

Across the AI ethics and global policy landscape, there is consensus that there should be human accountability for AI technologies1. These machines are used for high-stakes decision-making in complex domains -- for example, in healthcare, criminal justice and transport -- where they can cause or occasion serious harm. Some use deep machine learning models, which can make their outputs difficult to understand or contest. At the same time, when the datasets on which these models are trained reflect bias against specific demographic groups, the bias becomes encoded and causes disparate impacts2,3,4. Meanwhile, an increasing number of machines that embody AI, and specifically machine learning, such as highly automated vehicles, can execute decision-making functions and take actions independently of direct, real-time human control, in unpredictable conditions that call for adaptive performance.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Turrka Kuusisto, CEO of Posti, talks about how the group reinvented and transformed from a traditional postal company to a next-generation and efficient delivery and fulfillment company using the cloud.

A pair of researchers have found a more efficient way to multiply grids of numbers, beating a record set just a week ago by the artificial intelligence firm DeepMind. The company revealed on 5 October that its AI software had beaten a record that had stood for more than 50 years for the matrix multiplication problem – a common operation in all sorts of software where grids of numbers are multiplied by each other. DeepMind's paper revealed a new method for multiplying two …

Branded "the world's first ultrarealistic humanoid robot artist," Ai-Da is widely known for creating portraits and poems, using a robotic arm, cameras in her eyes and AI algorithms. She told the house -- undoubtedly to her creator's pride -- that the unique features allow her to create "visually appealing images."

It may not be unusual for burgeoning areas of science, especially those related to rapid technological changes in society, to take off quickly, but even by these standards the rise of artificial intelligence (AI) has been impressive. Together with robotics, AI is representing an increasingly significant portion of research volume at various levels, as these charts show. The number of AI and robotics papers published in the 82 high-quality science journals in the Nature Index (Count) has been rising year-on-year -- so rapidly that it resembles an exponential growth curve. A similar increase is also happening more generally in journals and proceedings not included in the Nature Index, as is shown by data from the Dimensions database of research publications. Five countries -- the United States, China, the United Kingdom, Germany and France -- had the highest AI and robotics Share in the Nature Index from 2015 to 2021, with the United States leading the pack.