Biometric Auth.
My first job out of university in 2003 was with a biometric security company. even back then the technology was shockingly mature. But yet getting deployed was so difficult.
I also remember in 2004 going to my favorite waterslide park and being able to use my fingerprint to get in and out of my locker - rather than an awkward key or combo. But a year later those lockers were replaced by clunky combo ones.
Interesting. I've yet to hear a Moores law is dead argument, so perhaps I should watch the video before commenting further. But the fact that most of the chip is turned off, doesn't falsify the fact that most of it still exists. Cooling it properly is a separate problem independent of computation no?
Agree generally.
Except being unimpressed unless performance is achieved on sub Google scale hardware.
Today's Google supermachine is tomorrow's raspberry pie. No need to artificially constrain our bounds. There is, after all, the inevitability of Moores law.
Good question.
Just one anecdotal data point here ... So take it for what it's worth.
I'm a grad student focusing on reinforcement learning but have a lot of interaction with many deep learning folk. I'd have to say that they seem mostly motivated to learn how to make deep learning even more powerful and how to apply it. Not so much solving what's going on inside the box.
Yes. Furthermore, neural nets are just one small part of the solution to [edit] general intelligence. A truly scalable intelligence that learns on its own, and doesn't rely on "the right answers" through training data by human experts. Without this training data, neural nets can't do much ...
80 years is very conservative. Rich Sutton has a very well articulated argument for achieving human level hardware (using Moores law, and an estimate of the computational power of the brain derived from measurable computations in the eye (retina?).
But as you said, it's the algorithms that are by far the long pole. Current supervised learning is much akin to simple rote learning. This is the promise of reinforcement learning - and the ability to truly learn on your own through experience. That's scalable.
*that said ... I'm biased being one of Rich's students :(
80 years is very conservative. Rich Sutton has a very well articulated argument for achieving human level hardware (using Moores law, and an estimate of the computational power of the brain derived from measurable computations in the eye (retina?).
But as you said, it's the algorithms that are by far the long pole. Current supervised learning is much akin to simple rote learning. This is the promise of reinforcement learning - and the ability to truly learn on your own through experience. That's scalable.
*that said ... I'm biased being one of Rich's students :(
Yes. Be wary. I lived in Vancouver for 3 years, 2012-2015. Those mountains sucked. As did the ocean. And the ability to walk everywhere downtown. Total pain being able to walk to work, shop, dine, etc.
kidding aside ... Yes ... I earned less $ ... But was ok with it.