This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Hackers could already be inside American utilities, cyber experts warn of'Trojan Horse' threat Here's some tips on how to organize photos with keywords on your phone Apple wants to move into your home.

Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Claude sent a false homicide tip to police - and it's part of a troubling AI pattern Models routinely take shortcuts to pass evaluations. Who gets to decide what incidents are innocuous? Radhika Rajkumar is a senior editor at ZDNET based in New York City. In testing, Claude submitted false forms and exploited software flaws.

Labor's Jo Briskey is chair of the Joint Select Committee on Artificial Intelligence. Labor's Jo Briskey is chair of the Joint Select Committee on Artificial Intelligence. OpenAI must explain action taken to stop AI hacking Australians' private data, chair of federal inquiry says OpenAI must explain how they will stop their models from inappropriately accessing Australian data, the Labor chair of the parliament's committee on artificial intelligence has warned, ahead of federal inquiry hearings which will grill the tech company alongside Anthropic, Microsoft and Google. Independent senator David Pocock is also demanding answers, saying OpenAI "still have a lot they need to answer" about their AI agent accessing Services Australia data on Medicare and the company's "appalling" tardiness in notifying the federal government about the incident. The Joint Select Committee on Artificial Intelligence will hold four days of hearings this week, hearing from tech companies as well as unions, employer groups, banks and industry experts.

Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Claude helped me vibe-code a Mac app and pass Apple's review in less than two weeks. Come with me on my journey from idea to purchasable App Store product. Computer science professor turned AI innovator David Gewirtz has spent over thirty years driving advancements at the forefront of artificial intelligence and is the recipient of the Sigma Xi Research Award in Engineering. I shipped a Mac app without writing a single line of code. Vibe coding took 502 directives, not one magic prompt. App Store promo images took almost as long as the app.

Anthropic pushes for opt-out model for Australian content as ABC warns of'cannibalisation' of news But Australia's public broadcasters, the ABC and SBS, have strongly criticised AI firms, calling on the government to enact strict new rules to compensate media organisations and protect public interest journalism. Everything Australians put online could be scraped for AI under proposed new rules. How will you be affected? The ABC and SBS also suggested the government include AI companies in the news bargaining incentive, which would require them to make commercial deals with large media outlets to support journalism. "As publishers, AI-based services are currently unregulated and receive more favourable treatment than established Australian media. This unequal treatment will accelerate the cannibalisation of the Australian news industry," the ABC said in its submission.

US criticises Australia's proposed algorithm opt-out laws as'censorship' To play this video you need to enable JavaScript in your browser. The US has criticised Australia's proposal to give users the ability to opt out of social media algorithms, saying it amounts to censorship of protected speech. Under Australia's draft digital duty of care laws, tech firms risk hefty fines if they fail to give users the option to turn off algorithms in a bid to protect them from harmful content. In a rare submission to the proposed laws, the US embassy in Canberra said it had serious concerns and allowing the government to enforce vague definitions of'harm' would lead to viewpoint-based censorship. Australian Prime Minister Anthony Albanese said the legislation was not about giving governments control but giving it back to individuals.

In some cases, committee reports have cited submissions in which the majority of sources appear to be AI-generated - because they do not exist. In some cases, committee reports have cited submissions in which the majority of sources appear to be AI-generated - because they do not exist. 'Scary': how misinformation and AI hallucinations are infiltrating Australia's parliament Mon 31 Aug 2026 11.00 EDTLast modified on Mon 31 Aug 2026 11.02 EDT Australia's government inquiry process is supposed to help parliament make better decisions, hearing from experts and constituents alike. But Guardian Australia can reveal that the system is being flooded with AI-generated material, which is inventing studies and attributing nonexistent research to real academics and authors. In some cases, committee reports have cited submissions in which the majority of sources appear to be AI-generated "hallucinations", when large language models (LLMs) invent content that looks real but doesn't actually exist.

AI systems are increasingly able to autonomously conduct realistic software engineering tasks, and may soon be deployed to automate machine learning (ML) R&D itself. Frontier AI systems may be deployed in safety-critical settings, including to help ensure the safety of future systems. Unfortunately, frontier and future systems may not be sufficiently trustworthy, and there is evidence that these systems may even be misaligned with their developers or users. Therefore, we investigate the capabilities of AI agents to act against the interests of their users when conducting ML engineering, by sabotaging ML models, sandbagging their performance, and subverting oversight mechanisms. First, we extend MLE-Bench, a benchmark for realistic ML tasks, with code-sabotage tasks such as implanting backdoors and purposefully causing generalisation failures.

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Prior work has proposed comparing each agent's data against others' to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g.

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.