What is the AI Kill Switch Act proposed in the US and how will it work? Two members of the United States Congress have introduced bipartisan legislation that would require developers of the country's most powerful artificial intelligence systems to build in a "kill switch", allowing advanced AI models to be slowed, suspended or shut down if they pose a catastrophic risk. The proposed AI Kill Switch Act, introduced on Thursday by Democratic Representative Ted Lieu and Republican Representative Nathaniel Moran, would give the US government authority to order companies developing advanced AI systems to intervene if their models escape human control or threaten human life, critical infrastructure or the economy. The disclosure has intensified debate over whether increasingly powerful AI systems require stronger safeguards. "Congress must act quickly to ensure humans remain able to say stop, no matter how powerful these systems become," said Brendan Steinhauser, head of the Alliance for Secure AI.

How are companies, governments responding to the OpenAI hack? ChatGPT owner OpenAI has admitted an "unprecedented cyber incident" - two of its most capable artificial intelligence models hacked into another AI company on their own - stirring debates over the need for stronger technology guardrails. The company said its AI systems broke out of a testing environment and hacked startup Hugging Face. The startup had disclosed on July 16 that its servers were hacked by an unknown but sophisticated agent acting on its own. Here's the latest on how companies, some governments and lawmakers have responded to the first such publicly disclosed cyberattack: What has Hugging Face said?

Open AI says its AI model "went rogue": What do we know? OpenAI has revealed that one of its artificial intelligence models independently stole login credentials and hacked into another technology company's system, in what is widely seen as one of the first known incidents of AI systems acting autonomously. "We had a significant security incident during evaluation of our models," CEO Sam Altman posted on X on Tuesday. They have grown so powerful in a short span of time that alarming phenomena such as deepfakes and sophisticated cyberscams are becoming the norm. Earlier this year, a number of software engineers quit their jobs at top companies such as Anthropic and AI in protest against how the technologies are being built.

The UN's independent scientific panel for AI has published its first report. Artificial intelligence development has been progressing at such a rapid pace that current governance systems are unable to keep up, the UN's Independent International Scientific Panel on Artificial Intelligence says in its preliminary report . The panel, consisting of members from around the world, will provide the information needed to stage the UN Global Dialogue on AI Governance. It will take place in Geneva, where member states will discuss how to manage the technology, and is scheduled to begin on July 6. In its report, the panel discusses how quickly AI capabilities have evolved over the past few years.

Agriculture is ready for AI, but its data isn't Data accuracy, structure, and governance are foundational components required for agricultural AI. Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%. However, what AI vendors usually won't tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide-we've seen it first hand.

'There's this deep mystery of what, actually, is this thing?': the philosopher inside Google DeepMind AI Since 2017, Iason Gabriel has worked at the tech giant, trying to anticipate - and think through - the impact of AI. But as commercial and geopolitical pressures escalate, can ethicists make any difference? In 2017, a 33-year-old political philosopher named Iason Gabriel was told by a friend that he ought to apply for a job at DeepMind, the London-based subsidiary of Google where much of its AI research was concentrated. The suggestion was not an obvious one. Gabriel was a cheerful but intense junior academic with a passion for Vipassana meditation and what his brother calls "enthusiastic" rock climbing. At the University of Oxford, where he was a fellow at St John's College, Gabriel taught courses on political theory and wrote papers on the moral contortions of "yuppie ethics" and the ethical blind spots of effective altruism. When he wasn't there, he did crisis work for the United Nations Development Programme in Sudan and Lebanon. DeepMind, meanwhile, was the world's leading AI research lab. In part, this was because it had the financial and computational backing of Google, which had bought the company in 2014 for $650m. In part, it was because DeepMind had recently shown it could put those resources to stunning use. In Seoul, in 2016, a DeepMind system called AlphaGo defeated Lee Sedol, a South Korean Go champion, in a five-game match. The victory was significant not least because of Go's legendary complexity; the game has more possible configurations than there are atoms in the universe. Thanks to the fuss around AlphaGo, Gabriel was aware of DeepMind.

How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-duration, pass/fail coding benchmarks, ALE-Bench encourages iterative solution refinement over long time horizons. Our software framework supports interactive agent architectures that leverage test-run feedback and visualizations. Our evaluation of frontier LLMs revealed that while they demonstrate high performance on specific problems, a notable gap remains compared to humans in terms of consistency across problems and long-horizon problem-solving capabilities. This highlights the need for this benchmark to foster future AI advancements.

Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the sociotechnical impacts of AI on combat systems, military decision-making, and the norms of warfare have been understudied. We focus on a specific subset of lethal autonomous weapon systems (LAWS) that use AI for targeting or battlefield decisions. We refer to this subset as AI-powered lethal autonomous weapon systems (AI-LAWS) and argue that they introduce novel risks--including unanticipated escalation, poor reliability in unfamiliar environments, and erosion of human oversight--all of which threaten both military effectiveness and the openness of AI research. These risks cannot be addressed by high-level policy alone; effective regulation must be grounded in the technical behavior of AI models. We argue that AI researchers must be involved throughout the regulatory lifecycle. Thus, we propose a clear, behavior-based definition of AILAWS--systems that introduce unique risks through their use of modern AI--as a foundation for technically grounded regulation, given that existing frameworks do not distinguish them from conventional LAWS. Using this definition, we propose several technically-informed policy directions and invite greater participation from the AI research community in military AI policy discussions.

Why do AI models struggle with online hate speech detection? Hate speech that once circulated in person now travels farther and faster via anonymous online accounts behind a screen. As the United Nations marks the International Day for Countering Hate Speech on June 18, UN Secretary-General Antonio Guterres has warned that social platforms are amplifying the threat. With artificial intelligence (AI) increasingly tasked with detecting and removing hate speech online, Al Jazeera looks at where these systems fall short compared with human judgement. How is hate speech defined?

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