5 ms·

No, life is not like Go. You don't choose one of 64 squares to play your next stone on in life. The state space is infinite. This is why AI will continue beatin

No, life is not like Go. You don't choose one of 64 squares to play your next stone on in life. The state space is infinite. This is why AI will continue beating humans at games but be totally inept at, say, tech support.

JoeAltmaier 11y ago
Indeed not. Most folks can consider only a few moves, and hardly anybody plots more than 1 move ahead. Truly, the human state-space is finite (and quite tiny). Most folks sit in their hometown and vegetate. Instead of starting a business/catching a bus to the city/joining the circus/hiking into the woods to become a hermit/running for office/and on and on.
- astazangasta 11y ago
  This is just misanthropy.
  - JoeAltmaier 11y ago
    The statistics favor misanthropy?
    - mrec 11y ago
      I'm guessing astazangasta is objecting less to the statistics of how many people stay in their hometown, and more to the pejorative framing of that as "sitting" and "vegetating". Which strikes me as a fair point. I'm sure there are lots of positives to staying near family and friends, and if you have stronger community ties you may well be more active in that community.
deleted 11y ago
[deleted]
kazagistar 11y ago
Real life is also has a huge amount of hidden information; mostly hidden information actually. And a ton of the AI uses we need require the ability to handle this effectively.
tgb 11y ago
I don't think that's your real objection: consider a chat-bot, perhaps doing tech support, that can only output one letter at a time (forming words over multiple outputs). It chooses from one of 26 or so options at each step. State space is finite. You still think that GO is fundamentally unlike this case, but not for the reasons that you've presented.
- daveguy 11y ago
  A chatbot is definitely not choosing each letter separately. It is not even choosing each word separately. It is likely choosing a phrase structure and populating with a fixed set of words. LSTM net may be going word for word, but letter for letter would give poor results. At the very least you need n-grams.
  - tgb 11y ago
    You can write a chatbot which DOES choose each letter separately and it's isomorphic to the one which does not. This was part of my complaint about the branching factor discussion from an earlier article: it's not really well-defined. Your comment is like saying that AlphaGO doesn't choose each move separately, it considers a sequence of moves. Of course it does, but it still can output each move one at a time.
    - daveguy 11y ago
      No, my comment is like saying the typical minimum selection and output chunk of a chatbot is greater than one character at a time. The rules of Go dictate a single position selection per move. Chatbots do not have that restriction. Yes you can write a chatbot that chooses a character at a time, but no chatbot using a per character chunk has been considered remotely successful since before Eliza. I agree that the branching factor is not well defined for a chatbot. Although it definitely is not 26.
      - tgb 11y ago
        I'm saying that you can write it that way AND have no loss of functionality or infact any apparent change from the perspective of the user. Take a chat bot and have it output characters one at a time. That's all I'm claiming, nothing about how the chatbot decides what to output. But that suffices to show that a process which is choosing from a very constrained set of options is still in a different class of success than AlphaGo. The options chosen at each step are more constrained than the choice of moves in Go, so the argument "the difference between solving go and solving tech support is the number of options available at any time" is insufficient. One might be able to define a better notion than branching factor that takes into account how many steps are needed as well as how much it branches in each step and such a thing is probably much larger for tech support than it is for go. But such an argument would be hard to state well, I think.
lossolo 11y ago
I've said that you CAN'T brute force life like Go, so i don't know to what you are objecting.. One more thing. You are wrong in case of tech support, i've trained a lot of networks using ML, but not only neural networks, i've used markov chains, mcts etc. What you have wrote is ONLY a speculation, nothing more. Tech support for most of the products have finite states because they have finite use cases and finite code base. You know how paypal support looks like? In 99% of cases it's copy paste. What do you think support people know about the product ? They get couple of weeks of training and that's all they know about tech support of that product. It's only matter of time when you will get AI product tech support.
inopinatus 11y ago
Given that many human-operated tech support departments are inept, given their demonstrable inability to deal with issues and situations for which they lack a prior decision structure and/or script, your last point actually (albeit inadvertently) suggests that AlphaGo has a shot at a CSR job.
- lossolo 11y ago
  Exactly, because it has a shot. Writing that it doesn't have a chance is just effect of lack of knowledge in ML field. You can see some work that was already done in this research paper, before AlphaGo success. http://arxiv.org/pdf/1506.05869v1.pdf http://arxiv.org/pdf/1506.05869v1.pdf
monkmartinez 11y ago
I disagree. If you really think about each decision you make on a day to day basis, your state space is actually quite limited. In most cases, I don't have to sort through 64 different states to make most decisions. Even decisions that are "difficult." For example, My Air conditioner was not working correctly last week. These were my considerations: 1) I can investigate 2) I can call someone 3) I can investigate and try to repair 4) I can ignore it 5) I can destroy it 6) ??? Apply to college or go to trade school... Move to Silicon Valley or stay put... Decide to have children with my wife... All of these would start off as large state spaces, but would inevitably shrink as each state is considered. At least, that is how I solve my decisions. Lots of lists that get worked down... The real dilemma in decision making the determination of the outcome you desire, or better yet... the probability of attaining the desired outcome. If I am not mistaken, I read in _Thinking Fast and Slow_ that humans are pretty terrible at prediction, probability and remembering exact details. Furthermore, humans brains would meltdown if we sorted through 64 state spaces for every decision. We mostly live by rules of thumb and easy thinking... I think AlphaGo could make me a better human.
- astazangasta 11y ago
  The fact that humans pare down their choices through decision-making and convention is not the same as saying the choice does not exist. Your considerations to "My air conditioner was not working correctly last week" could also include: run through the streets naked, kill your wife, fill your bathtub with jello, buy a fan, etc. You eliminate most of these options out of hand because you know they won't be useful, as a result of an entire lifetime's worth of training; an AI has to be brought to this point. At any point on the AI's decision tree, a priori, is "kill all humans", just as it is on yours - it has to be taught that most of the time this choice doesn't make sense.
  - monkmartinez 11y ago
    You are right... I had to learn a lot to eliminate the obviously non-productive routes in my decision matrix. My contention is that those non-productive routes are fewer than the routes AlphaGo encounters within the game of Go. If there were an AlphaHVAC, AlphaMATH, AlphaPlumbing, AlphaStucco, AlphaEMS, AlphaLogic, AlphaMorals etc., etc. we could have modal expert systems we point at whatever we need to know and do. Emergency field medicine is a lot like remodeling a bathroom with mold. Once you discover the problem, you follow algorithms to fix the issues. It was the same with my HVAC system. In emergency field medicine, they even call them algorithms. Then we can combine the expert systems under some kind of unifying command structure... have them "talk" to each other, learn from one another. AlphaGo has shown that complex, difficult, and downright ugly problems can be modeled and yield results better than the lifetime training of a human in the game of Go (at least for now). IMO, Deepmind will mark the transition to the age of Transhumanism.
2bitencryption 11y ago
The state space of Go is essentially infinite as well. Computers could improve by several orders of magnitude and still not have a chance in hell of computing more states than there are atoms in the universe. For the purposes of computation, that is infinite. So Go is a fitting representation of the complex and "incomputable" problems of daily life.
DonaldFisk 11y ago
Very small Go board you have there. Normally there are 19x19 = 361 points to put the stones. Completely general AI is a long way off, but tech support and other specialisms are feasible using expert systems, which were each limited to a single area of expertise. These were limited by their knowledge bases but they did work - and they had one advantage over deep learning - we knew how they worked and they were able to explain their reasoning - the main problem was getting them adopted and deployed.

JoeAltmaier 11y ago

Indeed not. Most folks can consider only a few moves, and hardly anybody plots more than 1 move ahead. Truly, the human state-space is finite (and quite tiny). Most folks sit in their hometown and vegetate. Instead of starting a business/catching a bus to the city/joining the circus/hiking into the woods to become a hermit/running for office/and on and on.

astazangasta 11y ago
This is just misanthropy.
- JoeAltmaier 11y ago
  The statistics favor misanthropy?
  - mrec 11y ago
    I'm guessing astazangasta is objecting less to the statistics of how many people stay in their hometown, and more to the pejorative framing of that as "sitting" and "vegetating". Which strikes me as a fair point. I'm sure there are lots of positives to staying near family and friends, and if you have stronger community ties you may well be more active in that community.

deleted 11y ago

[deleted]

kazagistar 11y ago

Real life is also has a huge amount of hidden information; mostly hidden information actually. And a ton of the AI uses we need require the ability to handle this effectively.

tgb 11y ago

I don't think that's your real objection: consider a chat-bot, perhaps doing tech support, that can only output one letter at a time (forming words over multiple outputs). It chooses from one of 26 or so options at each step. State space is finite. You still think that GO is fundamentally unlike this case, but not for the reasons that you've presented.

daveguy 11y ago
A chatbot is definitely not choosing each letter separately. It is not even choosing each word separately. It is likely choosing a phrase structure and populating with a fixed set of words. LSTM net may be going word for word, but letter for letter would give poor results. At the very least you need n-grams.
- tgb 11y ago
  You can write a chatbot which DOES choose each letter separately and it's isomorphic to the one which does not. This was part of my complaint about the branching factor discussion from an earlier article: it's not really well-defined. Your comment is like saying that AlphaGO doesn't choose each move separately, it considers a sequence of moves. Of course it does, but it still can output each move one at a time.
  - daveguy 11y ago
    No, my comment is like saying the typical minimum selection and output chunk of a chatbot is greater than one character at a time. The rules of Go dictate a single position selection per move. Chatbots do not have that restriction. Yes you can write a chatbot that chooses a character at a time, but no chatbot using a per character chunk has been considered remotely successful since before Eliza. I agree that the branching factor is not well defined for a chatbot. Although it definitely is not 26.
    - tgb 11y ago
      I'm saying that you can write it that way AND have no loss of functionality or infact any apparent change from the perspective of the user. Take a chat bot and have it output characters one at a time. That's all I'm claiming, nothing about how the chatbot decides what to output. But that suffices to show that a process which is choosing from a very constrained set of options is still in a different class of success than AlphaGo. The options chosen at each step are more constrained than the choice of moves in Go, so the argument "the difference between solving go and solving tech support is the number of options available at any time" is insufficient. One might be able to define a better notion than branching factor that takes into account how many steps are needed as well as how much it branches in each step and such a thing is probably much larger for tech support than it is for go. But such an argument would be hard to state well, I think.

lossolo 11y ago

I've said that you CAN'T brute force life like Go, so i don't know to what you are objecting.. One more thing. You are wrong in case of tech support, i've trained a lot of networks using ML, but not only neural networks, i've used markov chains, mcts etc. What you have wrote is ONLY a speculation, nothing more. Tech support for most of the products have finite states because they have finite use cases and finite code base. You know how paypal support looks like? In 99% of cases it's copy paste. What do you think support people know about the product ? They get couple of weeks of training and that's all they know about tech support of that product. It's only matter of time when you will get AI product tech support.

inopinatus 11y ago

Given that many human-operated tech support departments are inept, given their demonstrable inability to deal with issues and situations for which they lack a prior decision structure and/or script, your last point actually (albeit inadvertently) suggests that AlphaGo has a shot at a CSR job.

lossolo 11y ago
Exactly, because it has a shot. Writing that it doesn't have a chance is just effect of lack of knowledge in ML field. You can see some work that was already done in this research paper, before AlphaGo success. http://arxiv.org/pdf/1506.05869v1.pdf http://arxiv.org/pdf/1506.05869v1.pdf

monkmartinez 11y ago

I disagree. If you really think about each decision you make on a day to day basis, your state space is actually quite limited. In most cases, I don't have to sort through 64 different states to make most decisions. Even decisions that are "difficult." For example, My Air conditioner was not working correctly last week. These were my considerations: 1) I can investigate 2) I can call someone 3) I can investigate and try to repair 4) I can ignore it 5) I can destroy it 6) ??? Apply to college or go to trade school... Move to Silicon Valley or stay put... Decide to have children with my wife... All of these would start off as large state spaces, but would inevitably shrink as each state is considered. At least, that is how I solve my decisions. Lots of lists that get worked down... The real dilemma in decision making the determination of the outcome you desire, or better yet... the probability of attaining the desired outcome. If I am not mistaken, I read in _Thinking Fast and Slow_ that humans are pretty terrible at prediction, probability and remembering exact details. Furthermore, humans brains would meltdown if we sorted through 64 state spaces for every decision. We mostly live by rules of thumb and easy thinking... I think AlphaGo could make me a better human.

astazangasta 11y ago
The fact that humans pare down their choices through decision-making and convention is not the same as saying the choice does not exist. Your considerations to "My air conditioner was not working correctly last week" could also include: run through the streets naked, kill your wife, fill your bathtub with jello, buy a fan, etc. You eliminate most of these options out of hand because you know they won't be useful, as a result of an entire lifetime's worth of training; an AI has to be brought to this point. At any point on the AI's decision tree, a priori, is "kill all humans", just as it is on yours - it has to be taught that most of the time this choice doesn't make sense.
- monkmartinez 11y ago
  You are right... I had to learn a lot to eliminate the obviously non-productive routes in my decision matrix. My contention is that those non-productive routes are fewer than the routes AlphaGo encounters within the game of Go. If there were an AlphaHVAC, AlphaMATH, AlphaPlumbing, AlphaStucco, AlphaEMS, AlphaLogic, AlphaMorals etc., etc. we could have modal expert systems we point at whatever we need to know and do. Emergency field medicine is a lot like remodeling a bathroom with mold. Once you discover the problem, you follow algorithms to fix the issues. It was the same with my HVAC system. In emergency field medicine, they even call them algorithms. Then we can combine the expert systems under some kind of unifying command structure... have them "talk" to each other, learn from one another. AlphaGo has shown that complex, difficult, and downright ugly problems can be modeled and yield results better than the lifetime training of a human in the game of Go (at least for now). IMO, Deepmind will mark the transition to the age of Transhumanism.

2bitencryption 11y ago

The state space of Go is essentially infinite as well. Computers could improve by several orders of magnitude and still not have a chance in hell of computing more states than there are atoms in the universe. For the purposes of computation, that is infinite. So Go is a fitting representation of the complex and "incomputable" problems of daily life.

DonaldFisk 11y ago

Very small Go board you have there. Normally there are 19x19 = 361 points to put the stones. Completely general AI is a long way off, but tech support and other specialisms are feasible using expert systems, which were each limited to a single area of expertise. These were limited by their knowledge bases but they did work - and they had one advantage over deep learning - we knew how they worked and they were able to explain their reasoning - the main problem was getting them adopted and deployed.