In April 2021, the European Commission published its draft regulation on AI, the first of its kind, with an aim to develop a bespoke regulatory framework on AI technology. The proposed regulation, if adopted in its current form, looks to introduce: a strict regime and mandatory requirements for "high-risk" AI, such as AI systems used to evaluate creditworthiness or establish credit scores; limited requirements for specific types of AI, such as chatbots; and a ban on certain uses of AI, such as AI systems that deploy subliminal techniques beyond a person's consciousness. The regulation will apply to users and providers of AI based in the EU. It will also regulate providers and users of AI systems that are established in a third country, where AI systems are located in the EU or to the extent the output produced by systems in a third country are used in the EU. The proposed regulation was open for consultation last year.

Risk management continues to dominate business conversations this year and with good reason.While risk is inherent in business, recent events revealed that many companies lacked the means to adequately manage third-party risk. Despite the digital transformation efforts companies have undertaken, many companies' obligations related to contractual, regulatory, financial reporting, and environmental requirements are still often managed manually. This creates informational silos that make it difficult for organizations to form a complete picture of potential risk and compliance exposures throughout their supply chains. As a result, companies lack the visibility to proactively manage these obligations and end up taking a reactive approach to identifying and addressing third-party risk. As industry and government regulation increases and pressure mounts to deliver quality goods at a competitive price, companies recognize the need to approach their risk management initiatives differently.

In 1950, Alan Turing proposed an imitation game as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions indistinguishable from a human. Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers, and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like. In fact, many of the most powerful systems are very different from humans. So an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created. Furthermore, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policymakers.

NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, a suite of tasks that require reasoning over long texts. We examine existing long-text datasets, and handpick ones where the text is naturally long, while prioritizing tasks that involve synthesizing information across the input. SCROLLS contains summarization, question answering, and natural language inference tasks, covering multiple domains, including literature, science, business, and entertainment. Initial baselines, including Longformer Encoder-Decoder, indicate that there is ample room for improvement on SCROLLS. We make all datasets available in a unified text-to-text format and host a live leaderboard to facilitate research on model architecture and pretraining methods.

A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. When deploying these systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in uncertain, complex, and unpredictable real-world contexts. We are also identifying a number of urgent systems challenges for trustworthy embodied systems, including robust and human-centric AI, cognitive architectures, uncertainty quantification, trustworthy self-integration, and continual analysis and assurance.

This article is part of a series on AI for Boards of Directors. As AI grows in impact in the business world, the US Government is finally wading in to influence the future of regulation. Businesses have significant challenges effectively operating in a regulatory free-for-all world. Companies actually want some regulation. As mentioned in "Why Are Technology Companies Quitting Facial Recognition?", the providers of AI solutions want federal regulations because, in the absence of leadership at the federal level, states and municipalities will create those regulations.

Anna* was 10 when she built her first video game on Roblox, a digital platform where young people can make, share and play games together. She used Roblox much like a child from a previous generation might have used cardboard boxes, marker pens and stuffed toys to build a castle or a spaceship and fill it with characters and story. There was one alluring difference: Roblox hosted Anna's tiny world online, enabling children she had never met and who maybe lived thousands of miles away from her home in Utah to visit and play. Using Roblox's in-built tools – child-friendly versions of professional software – Anna began to learn the rudiments of music composition, computer programming and 3D modelling. When she wasn't at school Anna was rarely off her computer. As she became more proficient, Anna's work caught the attention of some experienced users on Roblox, game-makers in their 20s who messaged her with a proposition to collaborate on a more ambitious project. Flattered by their interest, Anna became the fifth member of the nascent team, contributing art, design and programming to the game.

Algorithmic bias never comes from nowhere, of course; it begins with biased data. Human data is naturally skewed, with conscious and unconscious biases leaving their fingerprints all over datasets. The trick comes in spotting – and removing – any biases from your data and apps before it's too late. Despite the fact innovation often outpaces legislation, are organisations getting twitchy about the legal risks of AI? "The legal risk is simple but serious," Simon Carroll, dispute resolution partner at legal firm, BP Collins, tells IT Pro. "Biased algorithmic decisions could breach the Equality Act 2010, which aims to protect from unlawful discrimination by automated systems as well as by people."

Welcome to the supermarkets of the future. They may look and feel like the supermarkets we are all used to – and stock the same bread, butter and bananas – but these shops are now fitted out with more than £1m of the latest technology that their bosses promise will put an end to our biggest frustration (queueing) and our most persistent crime (shoplifting). Jill French, a legal secretary in her 30s, wearing a sharp navy suit and matching beret, has just left a Tesco Express on London's Holborn Viaduct empty-handed. It's coming up to 6.30pm on a Thursday and, like dozens of others, French has popped in for a few essentials on her way home. "I just went in to grab pasta, milk and some broccoli," she says.

However, it's only really in the last year that we have really seen digital ethics hit the mainstream, with organisations in the private and public sector focusing their attention and, increasingly, resources, on these matters. So, what's caused this shift? Many organisations underwent an overnight transformation during the pandemic to survive and at the heart of this was the accelerated adoption of more advanced technologies such as automation and artificial intelligence (AI). Organisations are now taking a more serious look at what being data-driven means for them, developing data strategies that could shift entire business models. Without integrating digital ethics into this acceleration, the ethical risks proliferate.