US researchers have announced the creation of new viruses using artificial intelligence, a scientific first that raises hopes for advances in medical treatments while also stoking anxiety about the dangers of the frontier technology. In findings published in the journal Science, researchers at Stanford University and the Broad Institute of MIT and Harvard said they used a naturally occurring phage - a type of virus that infects bacteria - as a template to generate thousands of genomes with AI. In tests, a mixture of the synthetic viruses proved more effective at killing E coli bacteria than the naturally occurring viruses, according to the findings. "Our approach expands what synthetic genomics can achieve alongside methods such as directed evolution and rational engineering, lays out a path for generating adaptive and resilient phage therapies against rapidly evolving pathogens, and establishes a foundation for the generative design of larger, more complex genomes," the researchers wrote in Science. Brian Hie and Samuel King, two of the paper's authors, did not immediately respond to requests for comment.

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A central goal of cognitive science is to provide a computationally explicit account of both the structure of the mind and its development: what are the primitive representational building blocks of cognition, what are the rules via which those primitives combine, and where do these primitives and rules come from in the first place? A long-standing debate concerns the adequacy of artificial neural networks as computational models that can answer these questions, in particular in domains related to abstract cognitive function, such as language and logic. This paper argues that recent advances in neural networks -- specifically, the advent of large language models (LLMs) -- represent an important shift in this debate. We test a variety of LLMs on an existing experimental paradigm used for studying the induction of rules formulated over logical concepts. Across four experiments, we find converging empirical evidence that LLMs provide at least as good a fit to human behavior as models that implement a Bayesian probablistic language of thought (pLoT), which have been the best computational models of human behavior on the same task. Moreover, we show that the LLMs make qualitatively different predictions about the nature of the rules that are inferred and deployed in order to complete the task, indicating that the LLM is unlikely to be a mere implementation of the pLoT solution. Based on these results, we argue that LLMs may instantiate a novel theoretical account of the primitive representations and computations necessary to explain human logical concepts, with which future work in cognitive science should engage.

The Cannes film festival isn't typically associated with video games, but this year it's playing host to an unusual collaboration. Lili is a co-production between the New York-based game studio iNK Stories (creator of 1979 Revolution: Black Friday, about a photojournalist in Iran) and the Royal Shakespeare Company, and it's been turning heads with its eye-catching translocation of Macbeth to modern-day Iran. "It's been such an incredible coup to have it as the first video game experience at Cannes," says iNK Stories co-founder Vassiliki Khonsari. "People have gone in saying, I'm not familiar playing games, so I may just try it out for five minutes. The Cannes festival's Immersive Competition began in 2024, although the lineup doesn't usually feature traditional video games. "VR films and projection mapping is the thrust of it," says iNK Stories' other co-founder, Vassiliki's husband Navid Khonsari. But Lili weaves live-action footage with video game mechanics in a similar way to a game such as Telling Lies or Immortality. Its lead, Zar Amir Ebrahimi, won best actress at Cannes three years ago. Lili focuses on the story of Lady Macbeth, here cast as the ambitious wife of an upwardly mobile officer in the Basij (a paramilitary volunteer militia within the Islamic Revolutionary Guard in Iran). As in the play, she plots a murder to secure her husband's rise. "I think that the narrative of Lady Macbeth is that she's manipulative, and that's exactly what got us interested," says Navid. "The social limitations based on her gender forced her to try to attain whatever leadership role she can," he continues. "If she was a man, she would have been one of the greatest kings that country would have ever experienced, but because she was a woman she had to work within the structure that was there for her.

The bouncy, midi melody of Nintendo's Wii theme descends into a drill beat. A Game Boy Colour opens up into a lip gloss case. ASAP Rocky goes "full Minecraft" in a pixelated hoodie, and a panting man bobs up and down with his arm stuck in a bush. This is not a glitch. Both online and IRL, pop culture is embracing the aesthetics of retro gaming.

During an address Thursday, President Joe Biden claimed he taught "political theory" at the University of Pennsylvania. An Altoona, Pennsylvania man has been arrested after allegedly having a sexual relationship with a teenage girl he met on the popular dating app, Tinder, according to reports. An NBC station out of Johnstown-Altoona, Pennsylvania reported that state police spoke with the 14-year-old girl on Sept. 19. During the conversation, the girl reportedly told police she met Steven Ellis, 32, on Tinder after creating a profile that made her appear older. In court documents, police said they learned the teenager and Ellis sent each other explicit messages and photos.

AI company Hugging Face has released a new open source JavaScript library that allows frontend and web developers to add machine learning capabilities to webpages and apps. Traditionally, Python notebooks are the toolkit for data scientists, but for most web and frontend developers, it's JavaScript. Until now, adding those functions meant a Python app on the backend that did the work, said Jeff Boudier, head of product and growth at the startup. Using JavaScript, the browser can request machine learning models to serve predictions and obtain answers for a visitor. "We provide some low code/no code tools, but if you want to dig in a little bit, you still have to whip out some Python notebooks, etc. And that's the traditional toolkit of data scientists," Boudier told The New Stack.

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do. But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own. When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter "a" must be added to end of a word to make the masculine form feminine in Serbo-Croatian.

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do. But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own. When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter "a" must be added to end of a word to make the masculine form feminine in Serbo-Croatian. This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do. But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own. When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter "a" must be added to end of a word to make the masculine form feminine in Serbo-Croatian.