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They learned how to train deep nets ten years ago (and are getting better fast as new techniques are invented). As a result, neural nets started taking over. B

• edblarney 10y ago
  This should not be downvoted, he is essentially correct. 'AI' has always been around, but the 'big leap' we've seen recently is entirely due to researchers finally able to make Neural Nets actually work :). 'Deep Learning' really refers to a specific kind of Neural Network. The first application was image recognition. It's also used a lot now in voice recognition - and they're trying to jam it into everything possible. I think it's basically fair to say that at least in 2016 AI pretty much boils down to Deep Learning / Neural Nets to move something more classical along in terms of capability. What 'AI' means changes over time, but I think it's safe to say the renewal and hype is based on the 'big eureka' in Neural Nets and their relevant applications.
  • _delirium 10y ago
    Even that I don't think involved much of a "eureka" moment, but mostly incremental improvements due to the environment changing. A lot of neural-net techniques from the '80s and '90s that didn't work that well at the time, now work well because we have sufficient data and computation power (especially via GPUs) to make them work. There have been improvements as well of course, but they're evolutionary improvements, while the big breakthrough was big data + big compute.