Jensen Huang Says Nvidia's New Vera Rubin Chips Are in'Full Production' The chip giant says Vera Rubin will sharply cut the cost of training and running AI models, strengthening the appeal of its integrated computing platform. Nvidia CEO Jensen Huang says that the company's next-generation AI superchip platform, Vera Rubin, is on schedule to begin arriving to customers later this year. "Today, I can tell you that Vera Rubin is in full production," Huang said during a press event on Monday at the annual CES technology trade show in Las Vegas. Rubin will cut the cost of running AI models to about one-tenth of Nvidia's current leading chip system, Blackwell, the company told analysts and journalists during a call on Sunday. Nvidia also said Rubin can train certain large models using roughly one-fourth as many chips as Blackwell requires.

The world's top chipmaker wants open source AI to succeed--perhaps because closed models increasingly run on its rivals' silicon. Nvidia CEO Jensen Huang arrives for a meeting with lawmakers in Washington, DC. Nvidia has made a fortune supplying chips to companies working on artificial intelligence, but today the chipmaker took a step toward becoming a more serious model maker itself by releasing a series of cutting-edge open models, along with data and tools to help engineers use them. The move, which comes at a moment when AI companies like OpenAI, Google, and Anthropic are developing increasingly capable chips of their own, could be a hedge against these firms veering away from Nvidia's technology over time. Open models are already a crucial part of the AI ecosystem with many researchers and startups using them to experiment, prototype, and build.

The humble inventions that power our modern world wouldn't have been possible without decades of support for early-stage research. In December 1947, three physicists at Bell Telephone Laboratories--John Bardeen, William Shockley, and Walter Brattain--built a compact electronic device using thin gold wires and a piece of germanium, a material known as a semiconductor. Their invention, later named the transistor (for which they were awarded the Nobel Prize in 1956), could amplify and switch electrical signals, marking a dramatic departure from the bulky and fragile vacuum tubes that had powered electronics until then. They were asking fundamental questions about how electrons behave in semiconductors, experimenting with surface states and electron mobility in germanium crystals. Over months of trial and refinement, they combined theoretical insights from quantum mechanics with hands-on experimentation in solid-state physics--work many might have dismissed as too basic, academic, or unprofitable. Their efforts culminated in a moment that now marks the dawn of the information age.

Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the'Decoding Broken Hearts' initiative on'Special Report.' This week's Senate hearing on U.S. competitiveness in artificial intelligence made it clear that we are not just in an AI race with China and the rest of the world. We are in a race to build the foundation of the 21st century global economy while strengthening our national security. That foundation is made of silicon, steel and megawatts. America's ability to lead in AI hinges on a simple but urgent question – can we build the computing infrastructure fast enough to unleash AI's full potential and drive a competitive advantage? The emerging AI cloud computing infrastructure is not like the general-purpose cloud that still powers most of the digital world.

Breaking: A Big Tech company is ramping up its AI development. The idea is to lower its gargantuan infrastructure costs and reduce its dependence on NVIDIA (a company that apparently brings out Mark Zuckerberg's "adult language" side). If all goes well, Meta hopes to use it for training by 2026. Meta has reportedly kicked off a small-scale deployment of the dedicated accelerator chip, which is designed to specialize in AI tasks (and is, therefore, more power-efficient than general-purpose NVIDIA GPUs). The deployment began after the company completed its first "tape-out," the phase in silicon development where a complete design is sent for a manufacturing test run.

Amazon is doubling its investment in Anthropic. The e-commerce giant will provide Anthropic with an additional 4 billion in funding on top of the 4 billion it committed last year. Although Amazon remains a minority investor, Anthropic has agreed to make Amazon Web Services (AWS) its "primary cloud and training partner." Before today's announcement, The Information had reported that Amazon wanted to make any additional funding contingent on a commitment from Anthropic to use the company's in-house AI chips instead of silicon from NVIDIA. It appears Amazon got its way, with both companies noting in separate press releases that Anthropic will use AWS Trainium and Inferentia chips to train future foundation models.

This paper introduces distribution-based prediction, a novel approach to using Large Language Models (LLMs) as predictive tools by interpreting output token probabilities as distributions representing the models' learned representation of the world. This distribution-based nature offers an alternative perspective for analyzing algorithmic fidelity, complementing the approach used in silicon sampling. We demonstrate the use of distribution-based prediction in the context of recent United States presidential election, showing that this method can be used to determine task specific bias, prompt noise, and algorithmic fidelity. This approach has significant implications for assessing the reliability and increasing transparency of LLM-based predictions across various domains.

As Qualcomm-powered Windows on Arm PCs begin appearing here at Computex, ushering in a generation of AI-infused Copilot laptops, it seemed appropriate to interview a major player in the push. Instead, I mean Arm, the semiconductor design company that licenses CPUs to companies like Qualcomm, Apple, and Samsung. Arm dominates in smartphones and tablets, and now, true PC contention finally seems possible. I sat down with chief executive Rene Haas in Taipei, touching upon everything from NPUs, to how Arm solved its Windows app gap, to why Intel, AMD, and Qualcomm don't matter to the success of Windows on Arm PCs. And he has nothing but praise for Apple's M-series Macs, which he says "woke up the industry on the art of the possible" with Arm laptops. "I think Apple silicon has really proven that you could build a first-class laptop and have no compromises," Haas said. This interview has been slightly edited for length and clarity.

Large language models exhibit societal biases associated with demographic information, including race, gender, and others. Endowing such language models with personalities based on demographic data can enable generating opinions that align with those of humans. Building on this idea, we propose "random silicon sampling," a method to emulate the opinions of the human population sub-group. Our study analyzed 1) a language model that generates the survey responses that correspond with a human group based solely on its demographic distribution and 2) the applicability of our methodology across various demographic subgroups and thematic questions. Through random silicon sampling and using only group-level demographic information, we discovered that language models can generate response distributions that are remarkably similar to the actual U.S. public opinion polls. Moreover, we found that the replicability of language models varies depending on the demographic group and topic of the question, and this can be attributed to inherent societal biases in the models. Our findings demonstrate the feasibility of mirroring a group's opinion using only demographic distribution and elucidate the effect of social biases in language models on such simulations.

Even the cleverest, most cunning artificial intelligence algorithm will presumably have to obey the laws of silicon. Its capabilities will be constrained by the hardware that it's running on. Some researchers are exploring ways to exploit that connection to limit the potential of AI systems to cause harm. The idea is to encode rules governing the training and deployment of advanced algorithms directly into the computer chips needed to run them. In theory--the sphere where much debate about dangerously powerful AI currently resides--this might provide a powerful new way to prevent rogue nations or irresponsible companies from secretly developing dangerous AI.