Mirendil believes this process will automate a lot of scientific and AI research, helping scientists make progress in fields like medicine, biology, and materials science.
Neyshabur thinks AI can mimic how human scientists can learn more about new domains, accumulate knowledge and expertise, and gradually improve their performance. “You can have a self-improving AI where you can point a problem at it and it keeps getting better with time,” he said.
“How can we have an AI system that keeps doing research, keeps improving its own knowledge and performance when it comes to Alzheimer’s disease?” he continued. “This technology allows us to set goals that are ambitious for AI, and the AI would keep making progress.”
Training self-improving AI, however, requires enormous amounts of computing power. The lab’s co-founder Harsh Mehta said training is increasingly about matching the right workloads to the right hardware.
“These models are really good at working with different workloads and chips, and assigning the right workloads to the right chips,” Mehta said. ” provides multiple kinds of chips […] this flexibility allows us to ultimately mix and match workloads with the right kind of accelerators, and then lower the cost not just for us, but also for our customers using our systems.”
That flexibility is central to Google’s AI infrastructure pitch. Amin Vahdat, SVP and chief technologist of AI and infrastructure at Google, said in a statement that AI advancement isn’t just about chip-level performance anymore, “but how we orchestrate entire systems of intelligence and break through the physical constraints of scaling.”
Neyshabur said Mirendil’s software and systems layer help customers get more out of Google’s hardware, giving the cloud giant another potential leg up in the race against its competition. In return, Google gets a strategic partner building frontier recursive self-improving AI — technology that it can eventually shop around to enterprise customers.
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