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Artificial intelligence pioneer says we need to start over

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Re: Artificial intelligence pioneer says we need to start over

#121
post #55
post #45

Machine learning may be nearing its ceiling. The history of AI goes in cycles. Someone has a good idea which solves some problems, followed by "strong AI Real Soon Now" enthusiasm, followed by that idea hitting its ceiling. AI has been through search, backtracking, the General Problem Solver, hill-climbing, and expert systems. Each was overhyped at the time, and each hit its ceiling. The big difference this time is t…

>The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn't the case with the previous rounds. My company (fortune 500) depends on expert systems, some even date back to the 80's AI boom. Doing what we do, at the scale we do it, would simply not be possible without them. Lots of companies make a lot of money with expert systems, but they…

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Re: Artificial intelligence pioneer says we need to start over

#122

Earlier quoted context omitted.

Yet it's the only strategy that we're absolutely certain produces intelligent life.

Are you absolutely certain we qualify as intelligent life? edit: This comment isn't a joke, I mean this seriously. Modeling a system after ourselves may not be the best idea if we're not the best possible system.

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Re: Artificial intelligence pioneer says we need to start over

#123

Earlier quoted context omitted.

Yet it's the only strategy that we're absolutely certain produces intelligent life.

Are you absolutely certain we qualify as intelligent life? edit: This comment isn't a joke, I mean this seriously. Modeling a system after ourselves may not be the best idea if we're not the best possible system.

You can define 'intelligent life' however you want. No matter how high you set the bar though, an algorithm that reproduces the intelligence of the average human and can be run in parallel at speeds unhinged by biological constraints would provide immense power, wouldn't it?

Re: Artificial intelligence pioneer says we need to start over

#124

Brief description of the issue for non-experts: Supervised learning You can judge the output of your network against ground truth. You say that's a cat? Nope, it's a dog! And then slightly adjust your network so it's less likely to give that wrong answer in the future. How exactly you adjust the network is what backpropagation describes (in combination with something called a learning rate). Unsupervised learning You…

Well your teachers, parents, society tell you what is right and wrong so we really aren't learning unsupervised.

But who teaches the previous generations and how? Chicken or egg first?

I think the origin is communication and memory. When a being (X) signals to another being (Y) the origins of its pain (signal of increased death probability) and X and Y can remember this cause, they can begin to teach.

Re: Artificial intelligence pioneer says we need to start over

#125
post #84
post #45

Machine learning may be nearing its ceiling. The history of AI goes in cycles. Someone has a good idea which solves some problems, followed by "strong AI Real Soon Now" enthusiasm, followed by that idea hitting its ceiling. AI has been through search, backtracking, the General Problem Solver, hill-climbing, and expert systems. Each was overhyped at the time, and each hit its ceiling. The big difference this time is t…

I feel that Numenta will break the ceiling that we're coming to now. I have quite a few upcoming personal projects that I plan to use there neural network(HTM) structure to enhance, the key being enhance. The mindset that I feel is lacking in the ML/AI community is that we should be creating systems that can be augmented and improved using AI, the product itself should stand alone without using any AI at all. This is…

From Yann LeCunn. ---

Numenta, Vicarious, NuPic, HTM, CLA, etc.

Jeff Hawkins has the right intuition and the right philosophy. Some of us have had similar ideas for several decades. Certainly, we all agree that AI systems of the future will be hierarchical (it’s the very idea of deep learning) and will use temporal prediction.

But the difficulty is to instantiate these concepts and reduce them to practice. Another difficulty is grounding them on sound mathematical principles (is this algorithm minimizing an objective function?).

I think Jeff Hawkins, Dileep George and others greatly underestimated the difficulty of reducing these conceptual ideas to practice.

As far as I can tell, HTM has not been demonstrated to get anywhere close to state of the art on any serious task.

I still think [Vicarious] is mostly hype. There is no public information about the underlying technology. The principals don’t have a particularly good track record of success. And the only demo is way behind what you can do with “plain vanilla” convolutional nets (see this Google blog post and this ICLR 2014 paper and this video of the ICLR talk).

HTM, NuPIC, and Numenta received a lot more publicity than they deserved because of the Internet millionaire / Silocon Valley celebrity status of Jeff Hawkins.

But I haven’t seen any result that would give substance to the hype.

Don’t get fooled by people who claim to have a solution to Artificial General Intelligence, who claim to have AI systems that work “just like the human brain”, or who claim to have figured out how the brain works (well, except if it’s Geoff Hinton making the claim). Ask them what error rate they get on MNIST or ImageNet.

---- Reference:

http://fastml.com/yann-lecuns-answers-from-the-reddit-ama/

Re: Artificial intelligence pioneer says we need to start over

#126
post #55
post #45

Machine learning may be nearing its ceiling. The history of AI goes in cycles. Someone has a good idea which solves some problems, followed by "strong AI Real Soon Now" enthusiasm, followed by that idea hitting its ceiling. AI has been through search, backtracking, the General Problem Solver, hill-climbing, and expert systems. Each was overhyped at the time, and each hit its ceiling. The big difference this time is t…

>The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn't the case with the previous rounds. My company (fortune 500) depends on expert systems, some even date back to the 80's AI boom. Doing what we do, at the scale we do it, would simply not be possible without them. Lots of companies make a lot of money with expert systems, but they…

What's a good resource for learning about expert systems? I'm curious about this "dead end" but most of what I've found online has been about the ai winter and not the technical details of expert systems.

Re: Artificial intelligence pioneer says we need to start over

#127

Brief description of the issue for non-experts: Supervised learning You can judge the output of your network against ground truth. You say that's a cat? Nope, it's a dog! And then slightly adjust your network so it's less likely to give that wrong answer in the future. How exactly you adjust the network is what backpropagation describes (in combination with something called a learning rate). Unsupervised learning You…

Having just raised a human from newborn to walking, talking kid, I have to say that her life so far has been chock full of oracles guiding her learning.

Long before language, babies spend hours every day just interacting with the physical environment, which is "true": the toy is here, not there. The body weighs a certain amount. If you move the head this way, you'll roll over. If you move it that way, no rolling. Etc.

And parents provide a different kind of guidance. We react differently to different sounds, actions, movements of child. Even if these are not "truth" philosophically, they represent for the child emotional truth.

I'm just a guy who reads things about AI on the Internet, but I have to say it sometimes seems to me like AI folks have funny ideas of what "intelligence" is, and how it develops. The description of "unsupervised" learning above doesn't comport with any human experience I recognize. IME, humans receive constant immediate feedback on everything we do in life.

Re: Artificial intelligence pioneer says we need to start over

#128

Back in the nineteen-nineties I'd built a neural-net creator using a method other than back-propogation that was able to create nets with about a hundred neurons that played tic-tac-toe well or perfectly (depending on the net created that day.) This was on a 12 mhz 286 using what I called static point multiplication (based on shift instructions, not multiplication instructions.) So I went to Toronto to see Professor…

My method was evolutionary, put too simply - something not completely undiscussed even at the time and it's undoubtedly happening in one way or another now. I also designed a method for parallel processing dedicated to neural nets that seemed much more efficient than just "more processors", I've no idea whether NVIDIA et al have caught up with design that or passed it by now.

Re: Artificial intelligence pioneer says we need to start over

#129
What we lack is an AI equivalent of a theory of aerodynamics. At the moment we're just brute forcing random solution when we have perfectly good systems we should be trying to reverse engineer: biological brains. This will likely require genetic & evutionary algorithms, a developmental genotype-phenotype mapping, and competitive ecological systems for open ended evolution. Right now we have back propogration which is effectively reinforcement learning--just one piece of the puzzle. I actually applied to Hinton's lab in 2009 for a postdoct position to address these very issues. He said they didn't have any money and were not taking new postdocs.

Re: Artificial intelligence pioneer says we need to start over

#130
post #55

Earlier quoted context omitted.

>The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn't the case with the previous rounds. My company (fortune 500) depends on expert systems, some even date back to the 80's AI boom. Doing what we do, at the scale we do it, would simply not be possible without them. Lots of companies make a lot of money with expert systems, but they…

What's a good resource for learning about expert systems? I'm curious about this "dead end" but most of what I've found online has been about the ai winter and not the technical details of expert systems.

The Wikipedia article isn't a bad start. https://en.wikipedia.org/wiki/Expert_system

I have previously built an expert system - a system of codified expert human knowledge built into a system of executable, interrelated rules. Rule engines are the execution engines of expert knowledge in an expert system. The RETE algorithm is another good place to start.

https://en.m.wikipedia.org/wiki/Business_rules_engine https://en.m.wikipedia.org/wiki/Inference_engine https://en.m.wikipedia.org/wiki/Rete_algorithm

When I was first learning, I downloaded and played with CLIPS, a pretty cool old expert system tool built by NASA. https://en.m.wikipedia.org/wiki/CLIPS

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