Some useful hints here about what is being contemplated. And, of course, big companies like MS, Google, Amazon, Apple, IBM .... have the access to huge amounts of data to work with, and exposure to rich problem types too. So expect new things from them. Note the statement that we are nowhere near 'AGI' (Artificial General Intelligence) yet, have been asked that several times lately.
Microsoft Just Built a World-Class Supercomputer Exclusively for OpenAI By Jason Dorrier in SingularityHub
Last year, Microsoft announced a billion-dollar investment in OpenAI, an organization whose mission is to create artificial general intelligence and make it safe for humanity. No Terminator-like dystopias here. No deranged machines making humans into paperclips. Just computers with general intelligence helping us solve our biggest problems.
A year on, we have the first results of that partnership. At this year’s Microsoft Build 2020, a developer conference showcasing Microsoft’s latest and greatest, the company said they’d completed a supercomputer exclusively for OpenAI’s machine learning research. But this is no run-of-the-mill supercomputer. It’s a beast of a machine. The company said it has 285,000 CPU cores, 10,000 GPUs, and 400 gigabits per second of network connectivity for each GPU server.
Stacked against the fastest supercomputers on the planet, Microsoft says it’d rank fifth.
The company didn’t release performance data, and the computer hasn’t been publicly benchmarked and included on the widely-followed Top500 list of supercomputers. But even absent official rankings, it’s likely safe to say its a world-class machine.
“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said OpenAI CEO Sam Altman. “And then Microsoft was able to build it.”
What will OpenAI do with this dream-machine? The company is building ever bigger narrow AI algorithms—we’re nowhere near AGI yet—and they need a lot of computing power to do it. ... "
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