The governor of the Bank of England has warned G20 finance ministers that artificial intelligence could cause a global economic downturn and pose a significant cyber security risk to financial systems. Andrew Bailey said any collapse of growth in the AI sector could lead to a future market correction that spreads worldwide. In an open letter to finance ministers in the US on Monday, he said companies around the world should prepare for security breaches involving simultaneous disruption across multiple firms. Earlier this month, a group of 100 firms, including Google, Microsoft, Anthropic and OpenAI, urged countries and groups to beef up their cyber defences before AI grows powerful enough to override them. Bailey told the G20 finance ministers that a combination of highly priced stock markets, increased borrowing by investors, and the growing concentration of money into a small number of major technology companies could amplify any future market correction.

The Bank governor recently told City bosses: 'No country can seal itself off from the cross-border nature of systems that are prevalent today.' The Bank governor recently told City bosses: 'No country can seal itself off from the cross-border nature of systems that are prevalent today.' Andrew Bailey warns G20 members about risk of cyber-disruption spreading'across jurisdictions' The Bank of England's governor, Andrew Bailey, has joined the throng of figures warning about the global risks posed by the most advanced artificial intelligence technology. In a two-page letter sent to international finance ministers and central bank governors as part of his role as chair of the international Financial Stability Board (FSB), Bailey said "frontier" AI models were "showing increasingly sophisticated autonomy and problem-solving abilities, as well as threat capabilities". He said the models risked destabilising the "highly interconnected" global financial system via cyber-disruption that "can spread across jurisdictions".

The widespread adoption of Artificial Intelligence (AI) is likely to displace people from jobs in a similar way seen during the Industrial Revolution, the governor of the Bank of England has said. Andrew Bailey said the UK needed to have the training, education, [and] skills in place so workers could shift into jobs that use AI. He told the BBC Radio 4's Today programme people looking for a job would find securing employment a lot easier if they had such skills. However, he warned that there was an issue with younger, inexperienced professionals finding it difficult to secure entry-level roles due to AI. We do have to think about, what is it doing to the pipeline of people?

The threat landscape is being shaped by two seismic forces. To future-proof their organizations, security leaders must take a proactive stance with a zero trust approach. AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate. The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

The radioactive'miracle water' that killed its believers In the 1920s, Radithor promised to cure everything from wrinkles to leukemia, but its unintended results were deadly. While 1920s soda shops offered a plethora of sweet treats, nearby pharmacies served their own tinctures--like Radithor, certified radioactive water. Breakthroughs, discoveries, and DIY tips sent every weekday. William Bailey promised to cure anything that ailed you. " Just a tiny bottle of apparently lifeless, colorless, and tasteless water " was, he advertised in a 1929 pamphlet for his product, Radithor, "the greatest therapeutic force known to mankind."

The company did not say how many jobs would be cut in Singapore or which roles would be affected. DBS currently has between 8,000 and 9,000 temporary and contract workers. The bank employs a total of around 41,000 people. Last year, Mr Gupta said DBS had been working on AI for over a decade. "We today deploy over 800 AI models across 350 use cases, and expect the measured economic impact of these to exceed S 1bn ( 745m; 592m) in 2025," he added.

This study employs explainable artificial intelligence (XAI) techniques to analyze the behavior of asphalt concrete with varying aggregate gradations, focusing on resilience modulus (MR) and dynamic stability (DS) as measured by wheel track tests. The research utilizes a deep learning model with a multi-layer perceptron architecture to predict MR and DS based on aggregate gradation parameters derived from Bailey's Method, including coarse aggregate ratio (CA), fine aggregate coarse ratio (FAc), and other mix design variables. The model's performance was validated using k-fold cross-validation, demonstrating superior accuracy compared to alternative machine learning approaches. SHAP (SHapley Additive exPlanations) values were applied to interpret the model's predictions, providing insights into the relative importance and impact of different gradation characteristics on asphalt concrete performance. Key findings include the identification of critical aggregate size thresholds, particularly the 0.6 mm sieve size, which significantly influences both MR and DS. The study revealed size-dependent performance of aggregates, with coarse aggregates primarily affecting rutting resistance and medium-fine aggregates influencing stiffness. The research also highlighted the importance of aggregate lithology in determining rutting resistance. To facilitate practical application, web-based interfaces were developed for predicting MR and DS, incorporating explainable features to enhance transparency and interpretation of results. This research contributes a data-driven approach to understanding the complex relationships between aggregate gradation and asphalt concrete performance, potentially informing more efficient and performance-oriented mix design processes in the future.

Gold Treasure Worth a Fortune Was Hidden in a Forest. For years, Jason Rohrer put out bizarre, beloved video games. Now, with Project Skydrop, he launches the real-world treasure hunt of his dreams. The muddy trail levels out and we stop to catch our breath. Which is good, because hiking with my eyes covered has been a pain in the ass. A voice says: "You can take your blindfold off now." I squint as I get my bearings. Then, after a bit more hiking and some bushwhacking, I finally see it. The thing no one is supposed to know the location of, at least for another few weeks. I have to fight a lizard-brain instinct to reach for it.

The new mobile OS can rewrite your text messages, summarize emails, and identify objects in photos. But one of the most fun features is Image Playground, which generates cartoon-like illustrations based on a text prompt. While Apple has shown examples of its output during its keynotes, demos, and product videos, we have yet to see a real-world example of an Image Playground character until now. Apple has shared with WIRED the first-ever example created by Image Playground that it's shown outside of its pre-recorded keynotes and marketing materials. Her name is Bailey, and she belongs to Craig Federighi, senior vice president of software engineering at Apple, who created the image for his wife in honor of Bailey's recent birthday.

Large Language Models (LLMs) are able to provide assistance on a wide range of information-seeking tasks. However, model outputs may be misleading, whether unintentionally or in cases of intentional deception. We investigate the ability of LLMs to be deceptive in the context of providing assistance on a reading comprehension task, using LLMs as proxies for human users. We compare outcomes of (1) when the model is prompted to provide truthful assistance, (2) when it is prompted to be subtly misleading, and (3) when it is prompted to argue for an incorrect answer. Our experiments show that GPT-4 can effectively mislead both GPT-3.5-Turbo and GPT-4, with deceptive assistants resulting in up to a 23% drop in accuracy on the task compared to when a truthful assistant is used. We also find that providing the user model with additional context from the passage partially mitigates the influence of the deceptive model. This work highlights the ability of LLMs to produce misleading information and the effects this may have in real-world situations.