At a convening for the Schmidt Sciences AI2050 program, I saw how academic researchers are facing up to the challenges of the AI era. Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI. The fellows list is a who's who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I'd interviewed previously or whose research I admired. It's a weird time for university AI researchers, who make up most of the AI2050 group. In the past four years, AI research has reoriented around large language models, and its cutting edge has moved from academic institutions to private companies.

Did we learn nothing from sci-fi? A study published in Science (via The New York Times) details how researchers used an AI model to design new viruses capable of infecting and reproducing inside bacteria. The good news: they took appropriate precautions, and their work could lead to breakthroughs in gene therapy and biotechnology. The bad news: the next guys might not be so generous. Scientists at Palo Alto's Arc Institute and Stanford University created the viruses with the genome language models Evo 1 and Evo 2. They work somewhat like large language models (LLMs) such as ChatGPT or Claude.

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Nvidia's Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company's growing ambition to power every layer of AI infrastructure. Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD's annual product event in San Francisco on Thursday. During a lengthy technical workshop last week at the company's headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system's increased power and efficiency capabilities. The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents. While GPUs are still the main hardware that companies use to train and run their AI models, the industry's shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks.

Apple regains top spot as world's most valuable company Apple has surpassed chipmaker Nvidia as the world's most valuable company as artificial intelligence-driven market pressures weigh on investors. Apple is now worth $4.88 trillion compared with Nvidia's $4.86 trillion, following a 3.5 percent decline in Nvidia's market value. The milestone marks the first time the Cupertino, California-based iPhone maker has held the top spot in more than a year. Last month, Apple unveiled a revamped version of its assistant, Siri AI, which enables the personal assistant to better understand the personal context of users' questions, access real-time information from the Web, and perform more complex tasks on behalf of users. "Market sentiment has shifted from rewarding model makers, then to semis, and now on to those companies that can turn compute into experiences and outcomes the customer will pay for, thus driving corporate earnings," Michael Monaghan, founder of Founder ETFs, told Al Jazeera. "Apple investors first questioned Apple's lower AI spend, but now have treated Apple's lower AI capital expenditure as an advantage, with the bull case being that Apple benefits from consumer AI without spending at cloud-infrastructure scale."

Reliant on Nvidia hardware for AI training and inferencing since its outset, Turing now handles roughly 10% of its AI training needs with Advanced Micro Devices graphics processing units. Self-driving tech developer Turing has added AMD Ventures to its list of backers and begun adopting Advanced Micro Devices' AI accelerators in its systems. The five-year-old Japanese startup is adding to its capabilities as it builds toward a commercial launch. Reliant on Nvidia hardware for AI training and inferencing since its outset, Turing now handles roughly 10% of its AI training needs with AMD graphics processing units, company executives said in an interview. AMD, headquartered a stone's throw away from Nvidia in Santa Clara, California, presented a good chance to diversify supply and achieve lower costs, the executives said. "We've made notable progress with the technology.

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We introduce BO4Mob, a new benchmark framework for high-dimensional Bayesian Optimization (BO), driven by the challenge of origin-destination (OD) travel demand estimation in large urban road networks. Estimating OD travel demand from limited traffic sensor data is a difficult inverse optimization problem, particularly in real-world, large-scale transportation networks. This problem involves optimizing over high-dimensional continuous spaces where each objective evaluation is computationally expensive, stochastic, and non-differentiable. BO4Mob comprises five scenarios based on real-world San Jose, CA road networks, with input dimensions scaling up to 10,100. These scenarios utilize high-resolution, open-source traffic simulations that incorporate realistic nonlinear and stochastic dynamics. We demonstrate the benchmark's utility by evaluating five optimization methods: three state-of-the-art BO algorithms and two non-BO baselines. This benchmark is designed to support both the development of scalable optimization algorithms and their application for the design of data-driven urban mobility models, including high-resolution digital twins of metropolitan road networks.

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Anthropic's Code with Claude showed off coding's future--whether you like it or not As tools like Claude Code get better, more and more developers are happy to hand off coding tasks to them. The way software gets built has changed for good. The vibes were strong at Code with Claude, Anthropic's two-day event for software developers in London that kicked off on May 19, the same day as Google's I/O in Palo Alto. "Who here has shipped a pull request in the last week that was completely written by Claude?" Jeremy Hadfield, an engineer at Anthropic, asked from the main stage. Almost half the people in the packed room--many sitting with laptops on their knees, coding or prompting as they watched the talks--raised their hands. Pull requests are fixes or updates to existing software that are submitted for review before they go live.