OpenAI CEO Sam Altman is remaining tight-lipped about the company’s secretive Q* project — even after admitting that his company is something of a leaky ship.

Even among those who followed along with OpenAI’s November massacre that saw Altman temporarily ousted, it’s easy to overlook the Q* (pronounced “queue-star”) of it all, particularly because nobody outside the company really knows what the heck it is.

The speculation-laden project was linked to the chaos at the firm in the aftermath of that failed coup, and at the end of 2023, OpenAI refused to answer any questions about it — a posture Altman continued in a new interview with tech podcaster Lex Fridman.

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating…

Jensen Huang believes coding languages are a thing of the past.

After setting a new world speed record for humanoid robots earlier this month, China’s Unitree is now claiming another. Its latest H1 bipedal takes the title for first to perform a standing backflip without the use of hydraulics.

Yes, humanoids like Boston Dynamics’ Atlas have been nailing backflips for a few years now but they make use of heavy, potentially leaky hydraulics to launch into the air, somersault backwards and then land on both feet.

Impressively, Unitree’s H1 relies on in-house M107 electric joint motors only, each of which boasts 360 Nm (265.5 lb.ft) of peak torque and can also be found on the company’s B2 quadruped. Each leg has three degrees of freedom at the hip plus one at the knee and another at the ankle, and all cabling is routed internally for snag-free clean lines.

Sakana AI, a Tokyo-based artificial intelligence startup founded by two prominent former Google researchers, released AI models on Wednesday it said were built using a novel method inspired by evolution, akin to breeding and natural selection.

Indoor positioning is transforming with applications demanding precise location tracking. Traditional methods, including fingerprinting and sensor-based techniques, though widely used, face significant drawbacks, such as the need for extensive training data, poor scalability, and reliance on additional sensor information. Recent advancements have sought to leverage deep learning, yet issues such as low scalability and high computational costs remain unaddressed.