Two of these images were generated using the artificial intelligence tool Grok, which is free to use and belongs to Elon Musk. I've never worn the rather fetching yellow ski suit, or the red and blue jacket - the middle photo is the original - but I don't know how I could prove that if I needed to, because of those pictures. Of course, Grok is under fire for undressing rather than redressing women. It made pictures of people in bikinis, or worse, when prompted by others. And shared the results in public on the social network X.

The UK will bring into force a law which will make it illegal to create non-consensual intimate images, following widespread concerns over Elon Musk's Grok AI chatbot. The Technology Secretary Liz Kendall said the government would also seek to make it illegal for companies to supply the tools designed to create such images. Speaking to the Commons, Kendall said AI-generated pictures of women and children in states of undress, created without a person's consent, were not harmless images but weapons of abuse. The BBC has approached X for comment. It previously said: Anyone using or prompting Grok to make illegal content will suffer the same consequences as if they upload illegal content..

Humans can reason about intuitive physics in fully or partially observed environments even after being exposed to a very limited set of observations. This sample-efficient intuitive physical reasoning is considered a core domain of human common sense knowledge. One hypothesis to explain this remarkable capacity, posits that humans quickly learn approximations to the laws of physics that govern the dynamics of the environment. In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy. In BSP, the environment is represented by a top-down generative model of entities, which are assumed to interact with each other under unknown force laws over their latent and observed properties. BSP models each of these entities as random variables, and uses Bayesian inference to estimate their unknown properties.

If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] See appendix B.

Humans can reason about intuitive physics in fully or partially observed environments even after being exposed to a very limited set of observations.

Humans can reason about intuitive physics in fully or partially observed environments even after being exposed to a very limited set of observations. This sample-efficient intuitive physical reasoning is considered a core domain of human common sense knowledge. One hypothesis to explain this remarkable capacity, posits that humans quickly learn approximations to the laws of physics that govern the dynamics of the environment. In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy. In BSP, the environment is represented by a top-down generative model of entities, which are assumed to interact with each other under unknown force laws over their latent and observed properties.

Humans can reason about intuitive physics in fully or partially observed environments even after being exposed to a very limited set of observations. This sample-efficient intuitive physical reasoning is considered a core domain of human common sense knowledge. One hypothesis to explain this remarkable capacity, posits that humans quickly learn approximations to the laws of physics that govern the dynamics of the environment. In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy. In BSP, the environment is represented by a top-down generative model of entities, which are assumed to interact with each other under unknown force laws over their latent and observed properties.

At age 19, I read an interview of physicist Lee Smolin. The idea that a foreseeable limit exists on our understanding of physics by the end of my life was profoundly unsettling. I felt frustrated that I might never witness solutions to the great mysteries of science, no matter how hard I work. But… perhaps one can find a way to tear down this limit. Artificial intelligence presents a new regime of scientific inquiry, where we can automate the research process itself.