Disclaimers: I cannot see the future. These are just my opinions. I really appreciate the work and money that SamA, Elon, and others have put into the OpenAI project. The Universe work in particular might help encourage young people, many of whom love video games, to study AI.
But I feel that contrarians, such as myself, have an ethical commitment to young people to voice our doubts and criticisms, so that they can avoid making a long journey down a career/research path that leads to a dead end. That being said, I think this project leads in a very unpromising direction. Here are some reasons:
1. Games aren't a good testbed for studying intelligence. In a game the main challenge is to map an input percept to an output action (am I drifting off the side of the road? Okay swerve right). The real challenge of intelligence is to find hidden abstractions and patterns in large quantities of mostly undifferentiated data (language, vision, and science all share this goal).
2. This platform is not going to help "democratize" AI. To succeed in one of these domains, contestants will need to use VAST amounts of computing power to simulate many games and to train their DL and/or RL algos. DeepMind and others will sufficient CPU/GPU power will almost certainly dominate in all of these settings.
3. Deep Learning, as it is practiced, isn't intellectually deep. With a few exceptions, there is nothing comparable to the great discoveries of physics, not even anything comparable to the big ideas of previous AI work (A*, belief propagation, VC theory, MaxEnt, boosting, etc). Progress in DL mostly comes from architecture hacking: tweak the network setup, run the training algo, and see if we get a better result. The apparent success of DL doesn't depend on any special scientific insight, but on the fact that DL algos can run on the GPU. That, combined with the fact that, except for the GPU, Moore's Law broke down roughly 10 years ago, means that relative to everything else, DL looks amazingly successful - because all other approaches to AI are frozen in time in terms of computing power.
1. Games are great, especially the closer they get to the real 3d worlds with all the basic visual transforms in play. You can generate training data cheaper that helps you bootstrap the AI.
2. Your argument, which boils down to large organizations can accomplish more than individuals is in general true. But then that isn't saying anything new. Still, I'd prefer the car company give its blueprints than not. My factory (my cpu) can then at least build the car, albeit at a smaller scale. And soon my factory will be bigger/cheaper. Yeah I know, I'd like to be wealthy like Google too.
3. This is your own value judgement. The insight that we simplify AI architecture to some addition and multiplication is a big idea - in my opinion. Transistors are getting cheaper and I believe they will continue to do so. DNN are better suited to take advantage of this new computing power. Turns out, all those great discoveries were just some multiplications and adds the whole time ;)
What you mention in point #1 isn't the main issue with games, but with the "reactive" approach in AI. Reactive AI systems are necessarily going to be more limited than say, predictive AI systems (a new emerging class of AI models). [1]
Agreed, though, that the focus on games is a long-standing source of problems holding back AI. My colleague has referred to this as "ludic AI" in a recent blog post where he expands on your statement about the real challenge of intelligence. [2]
"Deep Learning, as it is practiced, isn't intellectually deep. With a few exceptions, there is nothing comparable to the great discoveries of physics ... Progress in DL mostly comes from architecture hacking: tweak the network setup, run the training algo, and see if we get a better result."
To be fair isn't this what physicists do all day at CERN too? Smash some particles together, analyse the numbers, try to find patterns, tweak a few things and try again?
I take the point to be that there aren't "deeper" fundamental principles at play in these models. Tremendous progress has comes from simply tweaking of the numbers of layers, or how the feed forward to each other (skipping layers, etc), or by throwing more computer power or data at the same basic algorithm.
Where might we look for deeper principles? One idea is to consider what brains do and how they might be doing it. (I'm not saying we need to go down the rabbit hole of biological detail -- on the contrary I'm suggesting we look at known or even hypothesized principles of brain operation and import them into AI.)
Two ideas we have used in our work: prediction (over time), recurrent feedback (most brain regions have more feedback than feedforward inputs)
As a physicist judging from the outside, I share some your feeling. Are there general laws governing "learning"? Theorems? Are there "deeper" things to learn as humans? The thing is people in the field don't need heavy intuition or math. In some ways that's good (if you just want a result to utilize) and in others, it's bad (if you are a curious person).
In a sense, I would say yes there are learning laws, but it's still early in codifying them.
Along one axis, you could compare: supervised, semi-supervised, self-supervised and unsupervised learning. Along another axis, consider that there are versions of each method that take into account temporal/dynamic data, versus others that require randomly shuffled static data.
In the current problems of visual perception, I think the field would benefit greatly a shift to focus on multiscale interaction/dynamics rather than on (static) statistics as it is currently (for more on this, see my colleague's blog: [1]).
>In the current problems of visual perception, I think the field would benefit greatly a shift to focus on multiscale interaction/dynamics rather than on (static) statistics as it is currently (for more on this, see my colleague's blog: [1]).
Your friend's blog has a lot of good insights that I've seen in the theoretical neuroscience and computational cognitive science literature as well. Where do you guys work?
I work at LeEco US out of San Diego, and my colleagues work at other ML/AI companies also in San Diego. We originally met and collaborated at Brain Corporation.
Are you thinking of laws along the lines of biological evolution or physical laws of motion, where very simple ideas can produce extremely complex emergent behavior?
If so I think a layman law of ML is already understood.
To draw an analogy, some programmers might use trial-and-error to produce programs by fiddling with a few lines of code, seeing if it pass more unit tests, repeat.
If you believe that a human brain can be represented by machine code, then given infinite time, that trial-and-error programmer can write down the "source code" of the brain.
Then machine learning is just a "turing complete programming language"(i.e., a neural network architecture) with "source code" (in the form of matrix weights) where "passing more unit tests" is done by numerically following a gradient to update the "source code".
Everything else is just finding a better "programming language" that can make this run very fast on our current machines.
Based on my understanding, it all really boils down to probability, statistics and a few important theories like Vapnik-Chervonenkis which provides the mathematical foundation for what "learning" is, can we even learn from the given data and how well can we learn (VC dimension, etc).
But I would love it if someone can point me to or explain / derive from core first principles the concept of "learning".
Part of me says that you are correct on all of these points. However I think you went too far on 3.
Experimentation does not always follow theory in science. I would argue that many of the great discoveries of physics in the 20th century followed directly from startling results of experiments conducted at the end of the 19th century (the photoelectric effect for example). I agree that there seems to be an awful lot of experimenting going on and that hardware and large datasets have helped tremendously but there are also many researchers poking and prodding at deep learning theory [1, 2].
So from a glass-half-full perspective we have rapid (sometimes iterative) experimentation coupled with yawning gaps in theory to explain surprising results. In other words, the opposite of previous AI booms and a big reason to be optimistic despite all the hype.
You're forgetting about the mother nature's miracle work. Wait till you see some unknown genious from some god forgotten village far away from a civilization in an undeveloped country come up with a new break-through idea that will kick asses of all Sillicon Valleys in the world.
1. Games are great for studying intelligent agent behavior and intelligence itself. An intelligent agent will find hidden abstractions and patterns in a large dimensionality. Language understanding, vision, decision science, solving long-temporal credit assignment etc. all are present. Russell & Norvig (2003) considered goal-directed behavior as the essence of intelligence. Also see possibly the first papers on AI and their focus on parlor games: https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_B... & http://www.loebner.net/Prizef/TuringArticle.html
2. This platform will provide a test bed for AI algo's and help "democratize" AI. One does not need to set up their own platform. One can compare approaches. One can learn from other implementations running on a common ground. Being resource constrained forces one to be more creative and this paves the way for more energy-friendly methods. Sure, a high school student will not dominate the power houses like DeepMind et al. But the high school student can get up and running in a week or few days.
3. https://arxiv.org/abs/1608.08225 Physics and Deep Learning are well entwined. Deep Learning certainly is a big idea, up there with VC theory and boosting. It exists for decades now, I agree the more recent incarnation was made possible with more computing power and better bigger datasets, and relies less on new tricks. Yet, still tricks are being invented in the recent years that have majorly contributed to better generalization. Dropout being one. DL, and especially the relevant Deep Reinforcement Learning, is not just GPU's, but a lot of new (and budding) theory. One can run Random Forests (and other approaches) on CUDA too. The Neural Turing Machine heralded a whole new, intellectually deep and stimulating, field in Deep Learning, and we haven't seen the best of it yet. There are also fields, like vision, where the other approaches are significantly underperforming relative to DL. Try to train a SVM or RF on ImageNet. Also, one is not required to use DL for your agent. Experiment with the classic approaches and see which is better (https://arxiv.org/abs/1603.04119).
I actually shared some of your concern, not for AI research, but for game development. I thought it was very hard to actually get a good job in that field. Then the mobile game market started booming, and indy developers could make a living. AI has got the backing of all the major players in industry. Instead of pipe dreams and philosophical meanderings, we have actually business-value adding working models now. It is not going anywhere soon.
If all else fails, you remain a good coder or data analyst with a lot of automation skills.
But I feel that contrarians, such as myself, have an ethical commitment to young people to voice our doubts and criticisms, so that they can avoid making a long journey down a career/research path that leads to a dead end. That being said, I think this project leads in a very unpromising direction. Here are some reasons:
1. Games aren't a good testbed for studying intelligence. In a game the main challenge is to map an input percept to an output action (am I drifting off the side of the road? Okay swerve right). The real challenge of intelligence is to find hidden abstractions and patterns in large quantities of mostly undifferentiated data (language, vision, and science all share this goal).
2. This platform is not going to help "democratize" AI. To succeed in one of these domains, contestants will need to use VAST amounts of computing power to simulate many games and to train their DL and/or RL algos. DeepMind and others will sufficient CPU/GPU power will almost certainly dominate in all of these settings.
3. Deep Learning, as it is practiced, isn't intellectually deep. With a few exceptions, there is nothing comparable to the great discoveries of physics, not even anything comparable to the big ideas of previous AI work (A*, belief propagation, VC theory, MaxEnt, boosting, etc). Progress in DL mostly comes from architecture hacking: tweak the network setup, run the training algo, and see if we get a better result. The apparent success of DL doesn't depend on any special scientific insight, but on the fact that DL algos can run on the GPU. That, combined with the fact that, except for the GPU, Moore's Law broke down roughly 10 years ago, means that relative to everything else, DL looks amazingly successful - because all other approaches to AI are frozen in time in terms of computing power.