'Dangerous' AI Models Are Coming No Matter What The US government crackdown on Anthropic's Claude Fable 5 and Mythos 5 hides a glaring truth: AI models with advanced hacking capabilities will soon be the norm. Late last week, Anthropic took its new Claude Fable 5 and Mythos 5 AI models offline following a United States government export-control directive barring "any foreign national" from using the services. The company has been in talks with the White House since Friday but has yet to secure an agreement that would allow it to reinstate the offerings. Since Mythos debuted in April, Anthropic has claimed--and warned--that the model has advanced capabilities for not only finding software vulnerabilities to help defenders patch them, but also figuring out ways to exploit them that could be used by bad actors. Anthropic itself noted this double edged sword in its launch of Mythos 5 and Claude Fable 5. "A great deal of advanced usage of AI models is dual use: the same queries that are beneficial in the hands of cybersecurity professionals and biology researchers could be dangerous if available to malicious actors," the company wrote in a blog post last week.

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Japan and Canada can work more closely together to accelerate the real-world adoption of artificial intelligence, an expert at a Toronto-based, cutting-edge research institute says. "AI will be the technology that will power the future," Cameron Schuler, chief commercialization officer and vice president of industry innovation at the Vector Institute, said in a recent interview. "There are lots of opportunities for Japan and Canada to collaborate," he also said, naming manufacturing, financial services, life sciences and other industries as promising areas of cooperation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Americans Are Trading Billions of Dollars on Polymarket's Banned Offshore Platform It's the first estimate of how many Americans are sneaking onto Polymarket's banned crypto-based platform. Approximately 30 percent of the trading volume on Polymarket comes from the United States, according to a new study--an eye-popping number, considering that none of those people are legally allowed to use the crypto -based platform. The study, conducted by Rutgers University statistician Harry Crane, estimated that people in the US funneled between $10.6 to $26.7 billion through Polymarket. To track the platform's activity, Crane looked at what appeared to be US-based trades on offshore prediction market platforms from May 2025 to the end of April 2026. He found that many of the highest-volume markets on Polymarket were US-centric, including those covering US elections and sporting events.

I am attempting to solve a mathematical conundrum that has stumped many of humanity's greatest thinkers. I have zero mathematical training, apart from a distant undergraduate physics degree, which should put my odds of success at slim to none. But I also have a trick up my sleeve - a kind of mathematical genie that can conjure arcane secrets seemingly out of thin air. I make a short request concerning an esoteric conjecture in number theory, then cross my fingers. Perhaps "genie" is a bit too strong - I'm simply using GPT 5.5 Pro, the latest iteration of OpenAI's flagship model. But for mathematicians, modern AI models appear to have a spark of magic.

Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem, to address high-dimensional treatment spaces. Specifically, under standard assumptions like no unobserved confounding, we show that causal error decomposes into a series of moment-balancing errors of increasing order, and design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto lower-dimensional treatment attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments, the last of which used a semi-synthetic dataset of Amazon Reviews. Our experiments demonstrate the benefit of higher-order balance error optimization and competitive performance of projected causal estimates with attribute-specific estimators.

Paul Anka says Toronto's crime rate has spiked amid the arrival over 400,000 new immigrants, telling Bill Maher that Canada was homogenous until recently.

Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appropriate estimator from the many specialized survival approaches often requires substantial methodological and domain expertise. We introduce SurvivalPFN, a prior-data fitted network that amortizes Bayesian inference for censored observations through in-context learning. SurvivalPFN is pretrained on a diverse family of synthetic, identifiable, and right-censored data-generating processes, enabling it to amortize survival analysis in a single forward pass during inference. As a result, the model adapts to the effective complexity of each dataset without task-specific training or hyperparameter tuning, avoids restrictive parametric assumptions, and produces calibrated survival distributions. In a large-scale benchmark spanning 61 datasets, 21 methods, and 5 evaluation metrics, SurvivalPFN achieves strong predictive performance and often improves upon established survival models. These results suggest that SurvivalPFN offers a principled and practical foundation model for survival analysis, with potential applications in high-impact domains such as healthcare, finance, and engineering (https://github.com/rgklab/SurvivalPFN).

'If OpenAI lost its footing as the AI industry frontrunner, another barely distinguishable competitor - Musk's xAI or other - would simply replace it.' 'If OpenAI lost its footing as the AI industry frontrunner, another barely distinguishable competitor - Musk's xAI or other - would simply replace it.' If it wasn't already clear, Elon Musk and Sam Altman hate each other. While the two men were once cofounders of OpenAI, they're now locked in a vicious feud, playing out in all its theatrics in front of a judge and jury in a California courtroom. Musk is suing, alleging that Altman and OpenAI president Greg Brockman tricked him into forming and funding the organization as a non-profit before they subsequently restructured it to have a for-profit entity.

Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These "fast weights" can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proved very helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns.