Deep Learning caused a renaissance in AI research, but it’s running out of steam
Machine learning is disrupting countless industries, but transportation is among the sectors where it can have the most serious, most immediate impact. One of the companies making great strides in the adoption of machine learning is Uber – and its use of AI is not limited to just autonomous vehicles.
“AI, broadly defined, powers a lot of the core services that Uber delivers,” Zoubin Ghahramani, chief scientist at Uber and professor of information engineering at the University of Cambridge, told AI Business.
He explained that the company has applied algorithms to everything from location tracking to passenger safety: “For example, one of the things that was launched by our CEO recently was a hands-free pickup experience for drivers. We really don’t want drivers to have to interact with the screen on their mobile phone more than they need to, so conversational or speech interfaces is something that powers that interaction. There are many, many examples like that.”
Ghahramani presented a session at the AI Summit in San Francisco dedicated to probabilistic machine learning – the advanced approach to ML that can suggest the correct result even when the necessary data is not readily available.
“Having been an academic for about 20 years, I’ve seen many of the trends in machine learning and AI come and go, it’s a very fad-ish field of research,” he said. “Of course, the big one that everybody talks about is Deep Learning.
“It turns out – and this is something I talked about in the keynote – there’s nothing really magical about Deep Learning systems, they’re almost like ‘brute force’ systems that extract patterns from massive amounts of data.
“But we’re reaching the point where we have milked this technology quite a lot, and we need to look out for what’s next.”