6 ms·

Escaping the Local Minimum: Where AI Has Been and Where It Needs to Go

beezle 10y ago
Author needs to proofread before releasing.
- kennethfriedman 10y ago
  Haha, author here. I can assure you I did proofread it multiple times, but I'm sure I didn't catch every typo. Though I would love to hear thoughts on the content rather than the writing! Also, if anything is confusing, I'd be happy to clarify.
  - pboutros 10y ago
    That was an incredible piece. What an interesting way of looking at it. Matching it to Minsky's model really shows the limitations of current AI - but I'm going to read more about the validity of Minsky's model before setting any thoughts in stone. Thanks for sharing that.
    - kennethfriedman 10y ago
      Thanks! I'm glad you found the perspective interesting. It's definitely worth reading more about Minksy's models. I have a lot of references in-line, but here are a few more starting points. The near-final-draft version his book Emotion Machines is available for free on his MIT site[0] and WashPo gave it a very positive review[1]. It would be interesting to investigate some criticisms of the model, though I haven't found any that specifically target the 6 layer model. [0]: http://web.media.mit.edu/~minsky/Introduction.html http://web.media.mit.edu/~minsky/Introduction.html [1]: http://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html http://www.washingtonpost.com/wp-dyn/content/article/2006/12...
  - jcoffland 10y ago
    There are a lot of typos!
    - kennethfriedman 10y ago
      Thanks for reading! I'd be happy to correct specific problems if you'd be kind enough to email them to me (see the about section of my website for my email). If not, let's stick to substantive comments on the content rather than grammar or spelling.
      - jcoffland 10y ago
        Ok. To pick on one part. This argument is self-referencing and redundant: "However, it would be useful to have a neural network that is able to perform symbolic algebra. There are two clear reasons for this desire. First, this hypothetical system would demonstrate that neural networks can be used as a substrate for previously-achieved AI systems. Second, a neural network that could perform symbolic algebra would, by definition, be able to manipulate symbols." Then directly following the above, this sentence makes very little sense: "This would show that high level knowledge representations can be grounded in statistical models." What does it mean for "high level knowledge representations" to be "grounded in statistical models?" It sounds to me like your saying to implement symbolic algebra on NNs would prove that NNs can implement symbolic algebra. There is some good content in the article but I find the conclusions and even the premise to be overreaching. It would read better if it were condensed into a history of AI booms and busts with out making wild predictions.
        daveguy 10y ago
        The term "grounded" is to have meaning which references some external reality. He meant that a NN which can perform symbolic algebra would demonstrate that symbols are grounded in some meaning outside the identification/pattern matched sense. They have meaning outside of "an or symbol, an and symbol, a true symbol" with additional information in their relationships that a NN can understand well enough to use them in context.
  - beezle 10y ago
    Sorry if I offended but it was meant to be constructive. No doubt much effort went into the research but two mistakes in 2nd paragraph of the introduction do not reflect well on the presentation to readers like myself who are not in the field but are interested to know more.
Animats 10y ago
The big difference this time is that AI makes money. This matters. The first two AI booms never made it to profitability, or produced much usable technology. This time, there are applications. As a result, far more people are involved. This AI boom is at least three orders of magnitude bigger than the first two. I've had similar criticisms to the parent author for years, but I thought of it as a hubris problem. In each AI boom, there was a good idea, which promoter types then blew up into Strong AI Real Soon Now. The arrogance level of the first two AI booms was way out of line with the results achieved. This time, it's more about making money, and much of the stuff actually works. Machine learning may hit a wall too, but it's useful. The field isn't going to get trapped in a local minimum with neural nets because the field is too big now. When AI was 20 people each at Stanford, MIT, and CMU, that could happen. With 50,000 people taking machine learning courses, there are enough people for some to focus on optimizing existing technologies without taking away from new ideas. We're going to get automatic driving pretty soon. That's working now, with cars on the road from about a half dozen groups. Not much question about that. The author rehashes symbolic systems and natural language understanding as areas of recommended work. This may or may not be correct. Time will tell. He omits, though, the "common sense" problem. There's been work on common sense, but mostly as a symbolic or linguistic problem. Yet the systems that really need common sense are the ones that operate in the real, physical world. What happens next? What could go wrong? What if this is tried? That's what Google's self driving car project is trying to deal with. Unfortunately, Google doesn't say much about how they do this. That project, though, is really working on common sense. Incidentally, Danny Hillis did not found Symbolics. He founded Thinking Machines, which built the Connection Machine, a big SIMD (single instruction, multiple datastream) computer with 1024 dumb processors each executing the same instruction on different data.
- bogomipz 10y ago
  Can you elaborate on what is the "common sense problem"?
- kennethfriedman 10y ago
  Author here. Wow, fantastic feedback! Thank you for taking the time to read it and respond. I have a few responses. As an undergrad, I won't assume my background is strong enough for direct opinions, so apologies in advance as my responses are mostly pointers to other people. Professor Patrick Winston[0] at MIT would argue that the expert systems, the successes of the '80s also about making money. Obviously not as much present day, but that is due to the tech sector infiltrating more areas/markets simply because of the exponential growth processing power. It would be very interesting to compare AI's value-add to the world between 1980 and today when adjusted for the "inflation" of moore's law. It's true I didn't consider the number of people in the field currently compared to the past, and that's an interesting point. The instructor of the class that I wrote this paper for, Joscha Bach[1], would argue that real physical world results are not very significant, since the real world can be thought of as a simulation itself. The idea of "what happens next? What could go wrong?" with self driving cars is interesting. A question arises, should you be able to ask the car, "why did you just stop short?" and receive an answer? This is a question Gerald Sussman has been discussing recently[2]. If we do want systems that can explain their behavior, then they must speak in human-language and therefore must be symbolic at some level. In general, the idea of "Common Sense" seems too much of (what Minksy would describe as) a suitcase word[3] -- because the definition is so abstract, it's not worthy of debate without defining the term. Great call on Hillis, thanks! I completely mixed up Symbolics and Thinking Machines. Fixed! [0]: http://people.csail.mit.edu/phw/ http://people.csail.mit.edu/phw/ [1]: http://cognitive-ai.com/ http://cognitive-ai.com/ [2]: https://vimeo.com/151465912 https://vimeo.com/151465912 [3]: https://alexvermeer.com/unpacking-suitcase-words/ https://alexvermeer.com/unpacking-suitcase-words/
visarga 10y ago
What I knew was that local minima are not that problematic when the state space is highly dimensional. Only saddle points appear in high dimensional spaces. The 2D example is not realistic. Here is a quote from a paper I randomly sampled on arxiv: > For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). https://arxiv.org/abs/1605.07110v1 https://arxiv.org/abs/1605.07110v1 Also, if your critique is related to the perceived lacks of backpropagation, keep in mind than reinforcement learning is also a kind of backpropagation of a reward, but this time the reward is much sparser and low dimensional. Thus, they are somewhere in-between supervised and unsupervised learning, not quite enjoying the full supervision of backpropagating at every example, but still learning based on an external critic. The way forward is to implement reinforcement learning agents with memory and attention. These systems are neural turing machines, they can compute in a sequence of steps.
- kennethfriedman 10y ago
  Author here, thanks for the comment! While this is an interesting read (and NTMs are great), it is not particularly relevant to the model I describe or this paper. I think you are misunderstanding my generality of my paper: I am not discussing a particular method of deep learning. I am using the idea of gradient descent as an metaphor for the field of AI, itself. As described in the second paragraph of the "Gradient Descent" section, this analogy is not high dimensional. In fact, it is only three dimensional: distance the field is from General AI, time, and a hypothetical "method of attack".
  - phreeza 10y ago
    I think the reasoning still holds. If the dimensions are reasonably independent of each other, the probability of a local minimum occurring in any kind of high-dimensional surface falls rapidly as the number of dimensions increases. And I would argue that machine learning methods are a pretty high-dimensional search space, describing them in a single 'method of attack' dimension is probably not right.
nl 10y ago
Have you read Pedro Domingos's[1] "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World"? You should. It directly addresses the idea of blending different fields of AI. [1] http://homes.cs.washington.edu/~pedrod/ http://homes.cs.washington.edu/~pedrod/
- kennethfriedman 10y ago
  No, I haven't seen this before but it looks like a great read, thanks for the recommendation! I'll check it out
  - nl 10y ago
    (Also I think it's really good the way you have been engaged and open to new ideas and criticisms here. That isn't always easy, and very refreshing to see)
- deleted 10y ago
  [deleted]
PaulHoule 10y ago
Rule based systems have to get ergonomic; intelligent systems by definition are not stupid -- I.e. if the behavior of the system is unacceptable you need to be able to patch it quickly not add another 150 million training examples. The #1 threat to a.I. right now is kaggleism, that is, training data is more valuable than talent, algorithms and all that.
munawwar 10y ago
I'd say that speeding up learning times for image related "AI" technology are important as well. Think of it..my nephew (1.5 year old) sees a cat just two times and is able to identify cats, whereas these neural nets needs huge training sets + performant machines and gpus.
- sushirain 10y ago
  If we trained a net on many classes except for cats, then it would require a few examples of cat to recognize cats.
sushirain 10y ago
Local minimum? In the next 10 years many deep learning applications will materialize: speech recognition will reach human level in production, autonomous trucks on highways, autonomous cars for consumers, AR, personal robot cleaners. The following 10 years will not fail us, too.
- kennethfriedman 10y ago
  Author here. Thanks for commenting, interesting point! However, as you can see in the section "1960s", many similar comments were predicted 50 years ago. The point I address in this paper is that optimism is easy when things are going well. This is a great example. Lots of people are saying today that we will have X in 10 years. But in 2006, no one was saying we will have X in 20 years. I'm not saying you're wrong, but I'm saying it's worth questing why you are so optimistic, and whether it's because we aren't looking at the big picture.
  - sushirain 10y ago
    The difference from the 60's mis-predictions is that today we already have speech and visual object recognition systems working with 95% accuracy and we only need to get them to 99%. This gap will be closed by data collection and faster processors, no algorithmic leaps necessary.

Author needs to proofread before releasing.

kennethfriedman 10y ago
Haha, author here. I can assure you I did proofread it multiple times, but I'm sure I didn't catch every typo. Though I would love to hear thoughts on the content rather than the writing! Also, if anything is confusing, I'd be happy to clarify.
- pboutros 10y ago
  That was an incredible piece. What an interesting way of looking at it. Matching it to Minsky's model really shows the limitations of current AI - but I'm going to read more about the validity of Minsky's model before setting any thoughts in stone. Thanks for sharing that.
  - kennethfriedman 10y ago
    Thanks! I'm glad you found the perspective interesting. It's definitely worth reading more about Minksy's models. I have a lot of references in-line, but here are a few more starting points. The near-final-draft version his book Emotion Machines is available for free on his MIT site[0] and WashPo gave it a very positive review[1]. It would be interesting to investigate some criticisms of the model, though I haven't found any that specifically target the 6 layer model. [0]: http://web.media.mit.edu/~minsky/Introduction.html http://web.media.mit.edu/~minsky/Introduction.html [1]: http://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html http://www.washingtonpost.com/wp-dyn/content/article/2006/12...
- jcoffland 10y ago
  There are a lot of typos!
  - kennethfriedman 10y ago
    Thanks for reading! I'd be happy to correct specific problems if you'd be kind enough to email them to me (see the about section of my website for my email). If not, let's stick to substantive comments on the content rather than grammar or spelling.
    - jcoffland 10y ago
      Ok. To pick on one part. This argument is self-referencing and redundant: "However, it would be useful to have a neural network that is able to perform symbolic algebra. There are two clear reasons for this desire. First, this hypothetical system would demonstrate that neural networks can be used as a substrate for previously-achieved AI systems. Second, a neural network that could perform symbolic algebra would, by definition, be able to manipulate symbols." Then directly following the above, this sentence makes very little sense: "This would show that high level knowledge representations can be grounded in statistical models." What does it mean for "high level knowledge representations" to be "grounded in statistical models?" It sounds to me like your saying to implement symbolic algebra on NNs would prove that NNs can implement symbolic algebra. There is some good content in the article but I find the conclusions and even the premise to be overreaching. It would read better if it were condensed into a history of AI booms and busts with out making wild predictions.
      - daveguy 10y ago
        The term "grounded" is to have meaning which references some external reality. He meant that a NN which can perform symbolic algebra would demonstrate that symbols are grounded in some meaning outside the identification/pattern matched sense. They have meaning outside of "an or symbol, an and symbol, a true symbol" with additional information in their relationships that a NN can understand well enough to use them in context.
- beezle 10y ago
  Sorry if I offended but it was meant to be constructive. No doubt much effort went into the research but two mistakes in 2nd paragraph of the introduction do not reflect well on the presentation to readers like myself who are not in the field but are interested to know more.

The big difference this time is that AI makes money. This matters. The first two AI booms never made it to profitability, or produced much usable technology. This time, there are applications. As a result, far more people are involved. This AI boom is at least three orders of magnitude bigger than the first two. I've had similar criticisms to the parent author for years, but I thought of it as a hubris problem. In each AI boom, there was a good idea, which promoter types then blew up into Strong AI Real Soon Now. The arrogance level of the first two AI booms was way out of line with the results achieved. This time, it's more about making money, and much of the stuff actually works. Machine learning may hit a wall too, but it's useful. The field isn't going to get trapped in a local minimum with neural nets because the field is too big now. When AI was 20 people each at Stanford, MIT, and CMU, that could happen. With 50,000 people taking machine learning courses, there are enough people for some to focus on optimizing existing technologies without taking away from new ideas. We're going to get automatic driving pretty soon. That's working now, with cars on the road from about a half dozen groups. Not much question about that. The author rehashes symbolic systems and natural language understanding as areas of recommended work. This may or may not be correct. Time will tell. He omits, though, the "common sense" problem. There's been work on common sense, but mostly as a symbolic or linguistic problem. Yet the systems that really need common sense are the ones that operate in the real, physical world. What happens next? What could go wrong? What if this is tried? That's what Google's self driving car project is trying to deal with. Unfortunately, Google doesn't say much about how they do this. That project, though, is really working on common sense. Incidentally, Danny Hillis did not found Symbolics. He founded Thinking Machines, which built the Connection Machine, a big SIMD (single instruction, multiple datastream) computer with 1024 dumb processors each executing the same instruction on different data.

bogomipz 10y ago
Can you elaborate on what is the "common sense problem"?
kennethfriedman 10y ago
Author here. Wow, fantastic feedback! Thank you for taking the time to read it and respond. I have a few responses. As an undergrad, I won't assume my background is strong enough for direct opinions, so apologies in advance as my responses are mostly pointers to other people. Professor Patrick Winston[0] at MIT would argue that the expert systems, the successes of the '80s also about making money. Obviously not as much present day, but that is due to the tech sector infiltrating more areas/markets simply because of the exponential growth processing power. It would be very interesting to compare AI's value-add to the world between 1980 and today when adjusted for the "inflation" of moore's law. It's true I didn't consider the number of people in the field currently compared to the past, and that's an interesting point. The instructor of the class that I wrote this paper for, Joscha Bach[1], would argue that real physical world results are not very significant, since the real world can be thought of as a simulation itself. The idea of "what happens next? What could go wrong?" with self driving cars is interesting. A question arises, should you be able to ask the car, "why did you just stop short?" and receive an answer? This is a question Gerald Sussman has been discussing recently[2]. If we do want systems that can explain their behavior, then they must speak in human-language and therefore must be symbolic at some level. In general, the idea of "Common Sense" seems too much of (what Minksy would describe as) a suitcase word[3] -- because the definition is so abstract, it's not worthy of debate without defining the term. Great call on Hillis, thanks! I completely mixed up Symbolics and Thinking Machines. Fixed! [0]: http://people.csail.mit.edu/phw/ http://people.csail.mit.edu/phw/ [1]: http://cognitive-ai.com/ http://cognitive-ai.com/ [2]: https://vimeo.com/151465912 https://vimeo.com/151465912 [3]: https://alexvermeer.com/unpacking-suitcase-words/ https://alexvermeer.com/unpacking-suitcase-words/

visarga 10y ago

What I knew was that local minima are not that problematic when the state space is highly dimensional. Only saddle points appear in high dimensional spaces. The 2D example is not realistic. Here is a quote from a paper I randomly sampled on arxiv: > For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). https://arxiv.org/abs/1605.07110v1 https://arxiv.org/abs/1605.07110v1 Also, if your critique is related to the perceived lacks of backpropagation, keep in mind than reinforcement learning is also a kind of backpropagation of a reward, but this time the reward is much sparser and low dimensional. Thus, they are somewhere in-between supervised and unsupervised learning, not quite enjoying the full supervision of backpropagating at every example, but still learning based on an external critic. The way forward is to implement reinforcement learning agents with memory and attention. These systems are neural turing machines, they can compute in a sequence of steps.

kennethfriedman 10y ago
Author here, thanks for the comment! While this is an interesting read (and NTMs are great), it is not particularly relevant to the model I describe or this paper. I think you are misunderstanding my generality of my paper: I am not discussing a particular method of deep learning. I am using the idea of gradient descent as an metaphor for the field of AI, itself. As described in the second paragraph of the "Gradient Descent" section, this analogy is not high dimensional. In fact, it is only three dimensional: distance the field is from General AI, time, and a hypothetical "method of attack".
- phreeza 10y ago
  I think the reasoning still holds. If the dimensions are reasonably independent of each other, the probability of a local minimum occurring in any kind of high-dimensional surface falls rapidly as the number of dimensions increases. And I would argue that machine learning methods are a pretty high-dimensional search space, describing them in a single 'method of attack' dimension is probably not right.

nl 10y ago

Have you read Pedro Domingos's[1] "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World"? You should. It directly addresses the idea of blending different fields of AI. [1] http://homes.cs.washington.edu/~pedrod/ http://homes.cs.washington.edu/~pedrod/

kennethfriedman 10y ago
No, I haven't seen this before but it looks like a great read, thanks for the recommendation! I'll check it out
- nl 10y ago
  (Also I think it's really good the way you have been engaged and open to new ideas and criticisms here. That isn't always easy, and very refreshing to see)
deleted 10y ago
[deleted]

PaulHoule 10y ago

Rule based systems have to get ergonomic; intelligent systems by definition are not stupid -- I.e. if the behavior of the system is unacceptable you need to be able to patch it quickly not add another 150 million training examples. The #1 threat to a.I. right now is kaggleism, that is, training data is more valuable than talent, algorithms and all that.

munawwar 10y ago

I'd say that speeding up learning times for image related "AI" technology are important as well. Think of it..my nephew (1.5 year old) sees a cat just two times and is able to identify cats, whereas these neural nets needs huge training sets + performant machines and gpus.

sushirain 10y ago
If we trained a net on many classes except for cats, then it would require a few examples of cat to recognize cats.

sushirain 10y ago

Local minimum? In the next 10 years many deep learning applications will materialize: speech recognition will reach human level in production, autonomous trucks on highways, autonomous cars for consumers, AR, personal robot cleaners. The following 10 years will not fail us, too.

kennethfriedman 10y ago
Author here. Thanks for commenting, interesting point! However, as you can see in the section "1960s", many similar comments were predicted 50 years ago. The point I address in this paper is that optimism is easy when things are going well. This is a great example. Lots of people are saying today that we will have X in 10 years. But in 2006, no one was saying we will have X in 20 years. I'm not saying you're wrong, but I'm saying it's worth questing why you are so optimistic, and whether it's because we aren't looking at the big picture.
- sushirain 10y ago
  The difference from the 60's mis-predictions is that today we already have speech and visual object recognition systems working with 95% accuracy and we only need to get them to 99%. This gap will be closed by data collection and faster processors, no algorithmic leaps necessary.