Uber has appointed Cambridge University academic Zoubin Ghahramani as its chief scientist.
Ghahramani will be based out of Uber’s head office in San Francisco and oversee Uber’s AI Labs, the company’s machine learning and AI research unit.
The taxi-hailing firm, which has been in the spotlight this month for all the wrong reasons, announced the appointment on its website on Tuesday, a week after Gary Marcus, head of Uber AI Labs, left the company.
Ghahramani is the latest Oxbridge AI expert to be poached by a rich tech giant — a growing trend that is raising some concerns in the industry.
Jeff Holden, chief product officer at Uber, wrote in a blog post on Uber’s website that Ghahramani is a “natural” for the chief scientist role. “Zoubin is among the most influential AI/ML researchers in the world,” he said.
Ghahramani, who has published over 250 academic papers, is a professor of information engineering at the University of Cambridge, where he oversees a team of roughly 30 researchers.
Explaining why he decided to join Uber, which is developing its own self-driving cars, Ghahramani wrote:
“Uber is one of the world’s fastest growing and innovative technology companies, and its mission to ‘make transportation as reliable as running water, everywhere, for everyone’ will have a tremendous positive impact on society. I’m a great believer in the potential for technology to improve lives around the world, and I’m really excited about helping Uber in this mission. I have realised what a fantastic place Uber is for machine learning and AI researchers: there are a huge number of opportunities for both near-term high-impact research and longer term challenges to work on; the resources both in terms of data and computation are plentiful; and there are many talented and brilliant colleagues to work with.
“Artificial intelligence and machine learning are absolutely central to Uber’s mission. Uber’s opportunities are unique among major technology companies, because they center around the real physical world, which is complex and difficult to predict. We have to navigate around the real world, develop perception and action systems for our self-driving cars, and understand, predict, and make more efficient the experience for our riders and drivers. At a larger scale, we are trying to model and optimise entire cities, and reimagine the future of transportation through, for example, urban VTOL aviation. The probabilistic ML approaches I work on are clearly useful for this, but we have assembled research talent across a much wider range of ML and AI approaches including deep learning, reinforcement learning, and optimization, as well as problem domains such as language and robotics. We are continuing to recruit across all these areas and more, for both talented researchers and engineers.”
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