He Did PR for Zuckerberg, Musk, and Google. Now He Says He'Only Told Half the Story' Thirty thousand feet in the air, Mark Zuckerberg turned to his speechwriter. The duo were flying in Zuckerberg's jet to the United Nations General Assembly in New York, where the Facebook boss was scheduled to address world leaders. Zuckerberg had a question for his companion. "Wait, what exactly is the UN?" Dex Hunter-Torricke had to hide his surprise. Zuckerberg was, by this point in 2015, the head of a company that was reshaping politics and societies around the world, with 1.5 billion users and counting.

Entertainment about or made with artificial intelligence has been missing the mark with viewers over the past year. Acclaimed director Darren Aronofsky is the executive producer of a new web series that matches human voice actors with video images generated in part by Google DeepMind. An insurrectionist robot unleashed by a mad inventor in Fritz Lang's . HAL 9000 sabotaging a manned mission to Jupiter in . Skynet, the self-aware global defense network that seeks to exterminate humanity throughout the franchise.

Do You Feel the AGI Yet? According to some predictions, 2026 is the year that an all-powerful AI will arrive. H undreds of billions of dollars have been poured into the AI industry in pursuit of a loosely defined goal: artificial general intelligence, a system powerful enough to perform at least as well as a human at any task that involves thinking. Will this be the year it finally arrives? Anthropic CEO Dario Amodei and xAI CEO Elon Musk think so.

Google's Project Genie lets you generate your own interactive worlds You'll need a Google AI Ultra subscription to try the showcase. Project Genie allows people outside of Google to try the company's Genie 3 world model. This past summer, Google DeepMind debuted Genie 3 . It's what's known as a world world, an AI system capable of generating images and reacting as the user moves through the environment the software is simulating. At the time, DeepMind positioned Genie 3 as a tool for training AI agents.

AI model from Google's DeepMind reads recipe for life in DNA An AI model developed by Google's DeepMind could transform our understanding of DNA - the complete recipe for building and running the human body - and its impact on disease and medicine discovery, according to researchers. Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as an incredible feat and a major milestone. We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life, says Natasha Latysheva, research engineer at DeepMind.

WIRED spoke with DeepMind's Pushmeet Kohli about the recent past--and promising future--of the Nobel Prize-winning research project that changed biology and chemistry forever. Amino acids "folded" to form a protein. Over the past few years, we've periodically reported on its successes; last year, it won the Nobel Prize in Chemistry . Until AlphaFold's debut in November 2020, DeepMind had been best known for teaching an artificial intelligence to beat human champions at the ancient game of Go Its work culminated in the compilation of a database that now contains over 200 million predicted structures, essentially the entire known protein universe, and is used by nearly 3.5 million researchers in 190 countries around the world The Nature article published in 2021 describing the algorithm has been cited 40,000 times to date. Last year, AlphaFold 3 arrived, extending the capabilities of artificial intelligence to DNA, RNA, and drugs.

Startups flush with cash are building AI-assisted laboratories to find materials far faster and more cheaply, but are still waiting for their ChatGPT moment. The microwave-size instrument at Lila Sciences in Cambridge, Massachusetts, doesn't look all that different from others that I've seen in state-of-the-art materials labs. Inside its vacuum chamber, the machine zaps a palette of different elements to create vaporized particles, which then fly through the chamber and land to create a thin film, using a technique called sputtering. What sets this instrument apart is that artificial intelligence is running the experiment; an AI agent, trained on vast amounts of scientific literature and data, has determined the recipe and is varying the combination of elements. Later, a person will walk the samples, each containing multiple potential catalysts, over to a different part of the lab for testing. Another AI agent will scan and interpret the data, using it to suggest another round of experiments to try to optimize the materials' performance. For now, a human scientist keeps a close eye on the experiments and will approve the next steps on the basis of the AI's suggestions and the test results. But the startup is convinced this AI-controlled machine is a peek into the future of materials discovery--one in which autonomous labs could make it far cheaper and faster to come up with novel and useful compounds. Flush with hundreds of millions of dollars in new funding, Lila Sciences is one of AI's latest unicorns.

We first address questions that might arise in response to the main text. What does it mean for Bob to *solve* one of these tasks? PO, or TO for Bob to learn to solve, when can Alice determine Bob has solved the task? A: Indeed, as discussed in our introduction, our goal is to examine the expressivity of Markov rewards in the context of finite MDPs. Instead, we suggest that for a given CMP, it is natural to be interested in Markov rewards, but acknowledge the importance of going beyond such functions.

Further, our best model's consensus statements are preferred

Upon its release earlier this month, OpenAI's Sora 2 model took the Internet by storm, thanks to its ability to generate realistic videos from just a text prompt. But Sora is about more than just capturing eyeballs with viral content. "On the surface, Sora, for example, does not look like it is AGI-relevant," OpenAI CEO Sam Altman said on a podcast earlier this month. "But I would bet that if we can build really great world models, that will be much more important to AGI than people think." Altman was speaking to a growing belief inside the AI industry at large: that if you can simulate the world with enough accuracy, you could drop AI agents into those simulations. There, they could learn more skills than they currently can from just text, photos, and videos--because they could interact with a simulated world. That form of training could be highly efficient, in part because simulated time can be accelerated, and because many simulations can be run in parallel.