I think that the idea that learning is what was missing from the prior generation of AI is the most important insight of this generation. There are many things that we don't know how to implement from first principals but that can be implemented by a system that can learn. The problem now is that the substrates for learning are extremely low level, practically the raw inputs to the retina or pure symbols. In order to go beyond the admittedly impressive parlor tricks you can play with these kinds of inputs we need much higher level representations that can be the substrates for learning. We are still missing that ever illusive 'common sense' knowledge about the world that evolution baked into nervous systems millions of years ago, and it is not at all clear to me whether learning algorithms are going to be the tool that allows machines to build an actionable internal model of the world, evolution didn't do it by learning, it did it by billions of years of trial and error and the search space is unimaginably larger than that of something like go.
Is AI Riding a One-Trick Pony?
61–70 of 219 posts
Re: Is AI Riding a One-Trick Pony?
#62> To continue reading this article, please exit incognito mode or log in.
Eat shit and fucking die.
Re: Is AI Riding a One-Trick Pony?
#63The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1
In defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.
Re: Is AI Riding a One-Trick Pony?
#64The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Hinton was able to survive 30 years in the academic wilderness. Most academics can't.
Thus they work on "safe" projects.
Re: Is AI Riding a One-Trick Pony?
#65The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1
We know the brain and associated sensor behaviours are too large for us to fully simulate in a reasonable way on anything resembling current hardware (We also can't fully model it but as we approached the size of hardware to do so we'd probably solve many of the problems of doing so). So which hacks and shortcuts do you want to apply to reduce the dimensionality to something runnable? Step 1) will take far too long so AI research looks for things it can do well in the category of 2) without being a full simulation. Deep Learning has been unreasonably effective here.
Re: Is AI Riding a One-Trick Pony?
#66[flagged]
Re: Is AI Riding a One-Trick Pony?
#67The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Last I checked bridges came before Newtonian Mechanics and it seems strange to argue this wasn't a good thing. Admittedly paper writing wasn't the main mechanism of transmitting knowledge but it's fairly common for human engineering to come before the full theoretical foundations as opposed to after.
Re: Is AI Riding a One-Trick Pony?
#68Earlier quoted context omitted.
The unrestricted Turing test has been around since the 1950s as a test that hasn't changed. No one is moving the goalposts, I think it's rather the opposite. Every ten years computers learn a new trick or two and people rush to claim that this time, it's intelligent.
The Turing test is all a smoke and mirrors game. Q&A interactions say nothing about underlying self-directed initiative. Acting intelligent doesn't make it so just as a thespian doesn't become a real Hamlet by playing the role.
It does if the questions are very carefully considered. Designing good questions is not easy.
Re: Is AI Riding a One-Trick Pony?
#69The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Humanity used fire for a long time before combustion was understood. Even today Anesthesia is not well understood at biological/physiological level that has not stopped its safe use and innovation through Clinical Trials. Maybe competitions and empiricism are the best approaches to building intelligent systems. Why get caught up in Physics/Math envy?
Re: Is AI Riding a One-Trick Pony?
#70[deleted]