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Palantir CTO Shyam Sankar says AI competition with China requires making American workers 50 times more productive to bring manufacturing jobs back to the U.S.

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Meta's chief technology officer has called it "the great honor of my life" to be enlisted in a new US army corps that defence chiefs set up to better integrate military and tech industry expertise, including senior figures from top tech firms that also include Palantir and OpenAI. Andrew Bosworth, a long-term lieutenant to Mark Zuckerberg known widely as "Boz", is one of several senior Silicon Valley executives commissioned to the rank of lieutenant colonel in the corps, called Detachment 201, which the US army says will "fuse cutting-edge tech expertise with military innovation". Bosworth, who joined Facebook in 2006, was sworn into the army reserves earlier this month alongside Shyam Sankar, the chief technology officer of Palantir, a technology firm with extensive defence contracts, Kevin Weil, chief product officer of OpenAI, and Bob McGrew, an adviser at Thinking Machines Lab, a 10bn AI company. They wore military fatigues at the swearing-in ceremony but will not be full-time soldiers. The recruitment is a sign of the increasing importance of technology in modern warfare and growing commercial and research links between some of the largest tech firms and the military.

When I read a tweet about four noted Silicon Valley executives being inducted into a special detachment of the United States Army Reserve, including Meta CTO Andrew "Boz" Bosworth, I questioned its veracity. It's very hard to discern truth from satire in 2025, in part because of social media sites owned by Bosworth's company. But it indeed was true. Boz is now Lieutenant Colonel Bosworth. The other newly commissioned officers include Kevin Weil, OpenAI's head of product; Bob McGrew, a former OpenAI head of research now advising Mira Murati's company Thinking Machines Lab; and Shyam Sankar, the CTO of Palantir.

Tech experts Louis Rosenberg, Neil Sahota, and Jake Denton discuss the dangers of AI taking jobs and influencing the way people think. Halting the development of artificial intelligence in America would only give more power to China to develop its own AI technology that favors its communist political system and increase the chances that China's AI system becomes the global standard, technology experts warned senators this week. A subcommittee of the Senate Armed Services Committee heard testimony from AI experts on Wednesday, nearly a month after Elon Musk, Steve Wozniak and dozens of other tech luminaries called for a "pause" in AI development until its "profound risks to society and humanity" are better understood. But at the subcommittee hearing, experts warned of the dangers of such a pause, especially the risk that China might continue to develop AI and dominate the field while the U.S. delays. Sen. Mike Rounds, R-S.D., said he opposes the idea of a development pause, and asked if the U.S. should "expect that other competitors around the world would consider taking a break."

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task.

Last year, Intel partnered with Lady Gaga on the Super Bowl Halftime Show to showcase its latest aerial technology called "Shooting Star." Intel did a reprise performance of its Shooting Star technology for Singapore's 52nd birthday this past week. Instead of fireworks, the tech-savvy country celebrated its National Day Parade with a swarm of 300 LED drones animating the night sky with shapes, logos, and even a map of the country. Intel's global drone chief, Anil Nanduri, explained, "There's considerably more operational complexity in handling a 300 drone fleet, compared with 100 drones in a show. You may be able to juggle three, but if you juggle nine, you may have to throw them higher and faster to get more time."

Shyam Sankar: Some say computers could attain artificial intelligence superior to humans A more realistic approach is to envision computers aided by human intelligence, he says Computers can spot patterns from the past but can't anticipate as people can, he says Sankar: Human thought, aided by computer power, can make sense of "big data" Computers can spot patterns from the past but can't anticipate as people can, he says Sankar: Human thought, aided by computer power, can make sense of "big data" In 1997, Garry Kasparov was defeated by IBM's Deep Blue supercomputer. It seemed like a watershed moment, recalling the rise of the machines long prophesied in science fiction. Yet in 2005, a freestyle chess tournament featured teams of humans partnering with computers in various combinations. Shockingly, two amateurs using three fairly weak laptops emerged victorious, beating grand masters and supercomputers in turn. This contrast is fittingly emblematic of two great visionaries of computer science, Marvin Minsky and J.C.R. Licklider.