Trump says FBI is investigating Jensen Huang’s affair with alleged Chinese spy

Trump says FBI is investigating Jensen Huang’s affair with alleged Chinese spy

Live•Nov 10, 08:30 AM•2 min read•
Carlos Mendez
Carlos Mendez

President Donald Trump says the FBI is investigating Nvidia chief executive Jensen Huang’s affair with Alice Huang, alleged to be a Chinese spy, first reported by Bbc. Nvidia has commissioned an independent investigation and says it has no reason to believe the company’s secrets or national security were compromised.

Trump said he spoke with Huang on Monday after reports that the US believed Huang had been targeted in a Chinese espionage campaign. Huang told Trump he had no idea Alice Huang was a spy, and Trump said he believed him.

Trump gave no details about the FBI inquiry or when it began; the FBI declined to comment when contacted by the BBC.

China’s foreign ministry rejected the allegations as entirely groundless and speculative, saying they were intended to smear and malign China. Huang said he would cooperate with Nvidia’s investigation.

Alice Huang, also known as Huang Le, was managing director of Singaporean company Megaspeed, which had dealings with Nvidia. The New York Times reported that US intelligence officials believe she was a spy for Beijing; it said Secretary of State Marco Rubio sent a memo to top Washington officials last spring warning of Jensen Huang’s close relationship with a Chinese spy.

The revelations knocked about $250bn (£190bn) off Nvidia’s value during trading on Monday as its share price fell by about 4%.

Nvidia is a major focus in the AI rivalry between the US and China, and Washington officials treat its exports to Beijing as a national-security matter. Huang has long opposed restrictions on sales to China, arguing that global AI rollout would be better powered by US technology.

The H200, Nvidia’s second most advanced semiconductor, had previously been restricted over concerns that it would give China’s technology industry an edge over the US. In January, the Trump administration said it would allow Nvidia to sell H200 chips to Chinese customers under certain conditions.

Several congressional committees have requested Huang’s appearance at a joint closed-door briefing next week, seeking information on Nvidia’s security protocols against foreign adversary espionage and his interactions with Alice Huang. The request puts company safeguards and Huang’s relationship on the same agenda; it is unclear whether Huang will appear.

Boston Dynamics tests Atlas control with a wheeled setup

Boston Dynamics has fitted wheels to its Atlas humanoid for a set of controls engineering experiments, first reported by Therobotreport. The experiment tests how the combined control approach supports dynamic stability as Atlas moves across unstructured, real-world terrain, shifting between rolling on flat ground and legged motion over obstacles or irregular surfaces.

Boston Dynamics describes a wheeled Atlas as a research testbed, not a product. Controls engineer Andy Wu attached wheels to Atlas’ legs to push actuation to its limits; the experiment also examines adaptability in conditions that are difficult to model or simulate.

Wu said a custom adapter mounts an off-the-shelf scooter rim onto Atlas’ existing ankle actuator and is the only new part in the build. On the software side, each wheel is modeled as an ankle that can spin continuously, replacing the feet in Atlas’ baseline robot model.

The system learned all of the behaviors in simulation and executed them in real life, with surprising accuracy, Wu said. The video also shows Atlas performing pirouettes and aggressive actuation maneuvers, alongside rolling and legged movement.

The whole-body reinforcement-learning policy processes real-time proprioceptive feedback and velocity commands. Separately, the experiment outputs dynamic pose and force adjustments to mitigate unmodeled disturbances and non-ideal contact states.

For Wu and the controls engineering team, the main takeaway is control synergy: classical motor control provides baseline stability, while whole-body reinforcement learning supports reactive adaptation. The combination yields greater real-world agility than either method alone.

Wu’s next question is how the policy will respond to failed actuators. That matters for safety and for understanding how the system will respond to a system failure.