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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 FatPhil on Sunday December 15 2019, @03:42PM

    by FatPhil (863) <pc-soylentNO@SPAMasdf.fi> on Sunday December 15 2019, @03:42PM (#932382) Homepage
    The AI cold fron was 1994-2014. 5 orders of magnitude of CPU (DSP*) tech advances, and an itsy-bitsy single order of magnitude of bogorithmic advances only cohered into real world advancements in what could be done in the last 5 years. Now that watershed is passed, it's full steam ahead - don't expect any downtime from now on.

    [* Your "GPU"s are just the DSPs of yore, massive muladd machines]
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