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Inside the world’s biggest and most important AI conference

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Look into the sea of computer scientists shaping the future of artificial intelligence and the first thing you’ll notice is all the backpacks. Expensive and branded, the backpacks mark the mostly male 20-and-30-somethings as members of the tribes of Google, Nvidia, Facebook, and Microsoft.

These researchers gathered last week at the largest and most influential AI conference, Neural Information Processing Systems. This is the conference’s 40th year, and its most-attended, at 7,229 registrations (only 17.1% were women). The people at the conference are the kind who need to carry computers with them all the time—pecking out presentation slides minutes before jumping on stage, training their algorithms up to the last-minute to eke out optimal results, checking the latest academic mudslinging on Twitter.

But the researchers who weren’t wearing backpacks were the talk of this year’s conference. They’re the old guard, including some who have attended the conference for more than 10 years. After years in academic obscurity, they’ve largely been recruited into senior positions at the companies that hand out pricey backpacks. In between presenting the latest work from the AI labs they lead, the typically-reserved corporate department heads traded barbs and showed up at talks to debate each others.

DeepMind CEO Demis Hassabis, 41, whose team dropped new research showing that its world champion Go-playing AI had also mastered chess and shogi, stepped up to a microphone after a talk to rebut comments from NYU professor and ex-Uber AI guru Gary Marcus, 47. Marcus was critical of DeepMind’s AlphaZero, saying the company had done some “hacks” to get the results it presented. Facebook’s AI head Yann LeCun, 57, wrote to his 60,000 Facebook followers to dispute senior Googler Ali Rahimi’s call for more rigor in the field.

The fights between AI’s most visible figures dominated the conference’s narrative. Rahimi’s call for engineers to understand more about the techniques they’re using was echoed in cries of “Make Machine Learning Rigorous Again,” while others argued that AI’s skyrocketing accuracy via open-sourced code and borrowed techniques is proof you don’t need to understand every bit of your algorithm.

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Article Credit: Quartz

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