AI has caused a renaissance of tech industry R&D, says Meta's chief AI scientist

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The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI scientist for Meta.

"The type of techniques that we've been working on have had a much bigger commercial impact, much more wide-ranging," than was the case in prior eras of artificial intelligence, said LeCun during a small meeting of press and executives via Zoom this month. 

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"And the result of this is it has attracted a lot of research funding and in fact, caused a renewal of industry research."

As recently as twenty years ago, said LeCun, Microsoft Research was the only industry entity that "had any kind of stature in information technology." But then, said LeCun, the 2010s saw "Google Research really coming to the fore, and FAIR [Facebook AI Research], which I created, and a couple of other labs starting up, and basically reviving the idea that industry could do fundamental research."

That resurgence of corporate R&D is happening, said LeCun, "because the prospect of what may happen in the future, and what happens in the present, thanks to those technologies, is great."

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AI also has "enormous potential for progress in materials science," said LeCun. "And we're going to need this because we need to solve climate change, so, we need to be able to have high-capacity batteries that don't cost a fortune, and don't require you to use exotic materials that we can only find in one place."

LeCun cited one such materials project, Open Catalyst, founded by colleagues at FAIR, which works with Carnegie Mellon University to apply AI to develop "new catalysts for use in renewable energy storage to help in addressing climate change."

"The idea there is, if we could cover a small desert with photovoltaic panels and then store the energy that is used by those panels, for example, in the form of hydrogen or methane," explained LeCun. The current approaches to store hydrogen or methane products, he said, are "either scalable, or efficient, but not both." 

"Could we discover, perhaps using the help of AI, a new catalyst that would make that process more efficient or scalable by not requiring some exotic new material? It may not work, but it's worth a try."

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Despite those many promising commercial and applied applications, LeCun suggested that the narrowness of industrial uses falls short of AI's grander objective, the quest for animal- or human-level intelligence.

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