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posted by martyb on Sunday December 15 2019, @01:32PM   Printer-friendly
from the I'll-think-about-it dept.

A sobering message about the future at AI's biggest party

Blaise Aguera y Arcas praised the revolutionary technique known as deep learning that has seen teams like his get phones to recognize faces and voices. He also lamented the limitations of that technology, which involves designing software called artificial neural networks that can get better at a specific task by experience or seeing labeled examples of correct answers.

"We're kind of like the dog who caught the car," Aguera y Arcas said. Deep learning has rapidly knocked down some longstanding challenges in AI—but doesn't immediately seem well suited to many that remain. Problems that involve reasoning or social intelligence, such as weighing up a potential hire in the way a human would, are still out of reach, he said. "All of the models that we have learned how to train are about passing a test or winning a game with a score [but] so many things that intelligences do aren't covered by that rubric at all," he said.


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  • (Score: 2) by JoeMerchant on Monday December 16 2019, @02:11AM

    by JoeMerchant (3937) on Monday December 16 2019, @02:11AM (#932639)

    HR does terrible things on the front end, not only matching (mostly meaningless) resume items to overly restrictive job descriptions, but also juicing the field to skew "irrelevant" parameters like race and sex toward more desirable ratios. What this ends up doing is freezing out lots of qualified candidates while we are required to interview more people of desirable counter-discrimination profiles.

    For positions of any importance, we will typically put at least 4 candidates through the process with 5-6 face to face interviews each, then get together and compare notes. Out of the pool of 4, there are almost always at least two who get the universal head-shake no for various reasons. About half the time, we're not happy with any of them and go back to HR for more.

    What's sad is when the weak ones are let through for various reasons. We hired a very personable engineer, enthusiastic, active in the community, fun to be around, but even in the interview they were clearly weak in technical execution abilities and if they haven't gotten those chops by the end of a Master's degree, they are unlikely to learn on the job. Anyway... fast forward two years and we've transferred them off to another division where they can hopefully contribute as a smaller player in a bigger team, we're just not big enough to take up that kind of slack.

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