The science and engineering of making chips dedicated to processing Artificial Intelligence is as vibrant as ever, judging from a well-attended chip conference taking place this week at Stanford University called Hot Chips.

The Hot Chips show, currently in its 36th year, draws 1,500 attendees, just over half of whom participate via the online live feed and the rest at Stanford's Memorial Auditorium. For decades, the show has been a hotbed for discussion of the most cutting-edge chips from Intel, AMD, IBM, and many other vendors, with companies often using the show to unveil new products. 

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This year's conference received over a hundred submissions for presentation from all over the world. In the end, 24 talks were accepted, about as many as would fit in a two-day conference format. Two tutorial sessions took place on Sunday, with a keynote on Monday and Tuesday. There are also thirteen poster sessions. 

The tech talks onstage and the poster presentations are highly technical and oriented toward engineers. The audience tends to spread out laptops and multiple screens as if spending the sessions in their personal offices. 

Monday morning's session, featuring presentations from Qualcomm about its Oryon processor for the Data Center and Intel's Lunar Lake processor, drew a packed crowd and elicited plenty of audience questions. 

In recent years, a big focus has been on chips designed to run neural network forms of AI better. This year's conference included a keynote by OpenAI's Trevor Cai, the company's head of hardware, about "Predictable scaling and infrastructure." 

OpenAI infrastructure engineer Trevor Cai on the predictable scaling benefits of increasing computing power that have been OpenAI's focus since the beginning.

Cai, who has spent his time putting together OpenAI's compute infrastructure, said ChatGPT is the result of the company "spending years and billions of dollars predicting the next word better." That led to successive abilities such as "zero-shot learning."

"How did we know it would work?" Cai asked rhetorically. Because there are "scaling laws" that show ability can predictably increase as a "power law" of the compute used. Every time computing is doubled, the accuracy gets close to an "irreducible" entropy, he explained.