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Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To t

Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis have improved by extraordinary leaps and bounds in recent years. If we used the logic that past failures inform a confident belief that future success in a challenge will inevitably fail, then we should've bet heavily against Alpha Go defeating one of the most accomplished human Go champions in the world. Self driving cars seem like a sci-fi fantasy until they become a mundane reality. There's an irrational arrogance to human beings in general, and Wall Street types in particular, regarding the specialness / non-reproducibility of their intelligence. It's not unlike the belief that people had that organic molecules were somehow special, "vital," and not synthesizable from base elements. Certainly, there's a long way to go to replicate the capabilities of a human brain, but I don't think we should exaggerate or fetishisize the human power to estimate and mitigate risk. We've seen many spectacular failures of that in recent years.

rimantas 10y ago
Would Alpha go beat the same human on 17x17 or 21x21? Or even 13x13? Let's not get started about self-driving cars…
- loup-vaillant 10y ago
  Maybe not, because it has not been trained for those dimensions. Then again neither have humans (except maybe on 13x13). That could be interesting, but I'm not sure what we could learn from this.
- educhana 10y ago
  Absolutely, both Fan Hui and Lee Sedol. https://deepmind.com/research/alphago/ https://deepmind.com/research/alphago/
  - pyrale 10y ago
    The link you provide does give no data on games played on 1717 or 2121. They were played on the standard 19*19 board.
vegabook 10y ago
The difference is that chess, go, etc are all essentially rules based. Finance has very few rules that do not break over time. Just look at QE. Arguably the financial market represents the collective intelligence of a huge amount of very clever people. Machines are only just starting to challenge a single human at a rules-based activity. We're very far from beating a brutally darwinian, impressively adaptive, human hive-mind whose main skill is figuring out when rules are about to get broken.
- cm2187 10y ago
  Interest rates cannot go negative was a commonly accepted rule at the beginning of my career (and interest rate option traders where using models that did not allow for negative interest rates).
- snakeworld 10y ago
  QE?
  - timthorn 10y ago
    Quantitative Easing
  - melling 10y ago
    Here's my first result from Google: https://en.m.wikipedia.org/wiki/Quantitative_easing https://en.m.wikipedia.org/wiki/Quantitative_easing Google is getting really good.
- dougabug 10y ago
  What are the rules for image / video captioning? For natural sounding speech synthesis? For realistic image generation? For semantic segmentation? For determining perceptual visual similarity between images? Frankly, rules based AI is basically a bust compared to learning from data approaches. The hive was pretty amazing at destabilizing the entire global economy within a brief span following fundamental financial de-regulation. People flatter themselves in ways which satisfies their egos and self-interest. Of course they want to believe in, and have everyone else credit, their magical superpowers. No government (backstop) wires necessary.
dilemma 10y ago
But the idea that you can predict the future is absurd. And even if you could, acting on that knowledge would change the future.
- yjftsjthsd-h 10y ago
  But the traders are trying to predict the future already! If it's impossible, then the machines are a shoe in, and if it is possible I'd still give the machines great odds.
- peller 10y ago
  The nuances of the game are different from strictly trying to predict the future. Casinos don't try to predict the future of every spin of the wheel or roll of the dice. Every individual game is random. But over a large enough sample size (number of games played), the odds are not random. The same principle is at play in trading the markets, except the "odds" are determined by the traders' skill - their "edge." Which is roughly some combination of access to the right people, access to better information (where better might but not necessarily mean quicker), and experience (gut feelings, hunches, what-have-you). So the real question is, can machine learning algorithms develop an "edge", and if so, can they stay solvent long enough to do so.
  - dilemma 10y ago
    No. In casinos the games are designed so that in the long run the house always wins. They don't need to care about odds. The stock market isn't a game; it isn't designed and it continuously evolves.
    - peller 10y ago
      > In casinos the games are designed so that in the long run the house always wins. That's exactly what I meant by odds; they always win because the odds of the games are in their favor. > The stock market isn't a game It's a game in the same sense that life itself is a game. > it isn't designed The mechanisms that fit all the pieces together are very much designed by humans. True the behavior of the market isn't designed; it's an emergent property of the complex system we call "the markets." > it continuously evolves Yes, but change is also the constant. What underlies that change is supply and demand. Of course you're free to disagree. But if you'd like to learn more about the not-100%-accurate analogy I described in abbreviated form, the source is here: http://www.goodreads.com/book/show/253516.Trading_in_the_Zone http://www.goodreads.com/book/show/253516.Trading_in_the_Zon... Edit: Good reading on "life as a game": http://www.goodreads.com/book/show/189989.Finite_and_Infinite_Games http://www.goodreads.com/book/show/189989.Finite_and_Infinit...
Cybiote 10y ago
There are varying levels of sophistication from which data can be learned from. Pigeons can learn non-trivial word concepts and statistics but they do so at a rate that is much slower than a human infant. Thus far, machines have not done well in scenarios of low stationarity. Those are scenarios where the past is not so good a predictor of the future or where the data manifold is rapidly changing. This occurs for example, when the act of predicting changes what is being predicted. The related notion of Antipredictable Sequences is the most interesting consequence of the oft misconstrued notion of the No free lunch theorem. Humans are far from the ideal machine for this scenario but they vastly outmatch current algorithms. This is because humans are much better in the kind of generalization that is based off theory building. While not perfect, it does well enough at generalizing from observations (think Newton deriving the laws of gravitation from observations and Brahe's data) to scenarios that are not so close to the data. This means that for now and in complex environments, simpler models that are adaptable to change online are better. Additionally, their inspectability means they are more easily modified to better match any very changed dynamics. So your examples of: `speech recognition, face recognition, object classification and detection, speech` Are of the type where a learner, such as a deep network, whose generalization ability mostly hails from interpolating to fill in missing structure based on data (implying poor out of sample performance) does well when you can give lots of examples which provide good coverage. Language translation is sort of inbetween (the space is not as smooth but is still fairly stationary at the scale of years) and so we also find that the advantages of deep learning have not been as pronounced and dramatic as has been the case for image and speech. The digital machine advantage is two. Their memory capacity is outmatched. Second, and related to the first, energy needs are not so pressing a concern.
- dougabug 10y ago
  No one is suggesting that AI is a solved problem. I simply cited a handful of areas such as computer vision where progress in ML has been remarkably rapid after many years where the problems seemed daunting and skeptics had written off the prospects of the field. Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking. How often do most human beings come to exhibiting the level of insight and reasoning power required to construct the calculus and discover the laws of classical mechanics? Inventing supernatural explanations for natural phenomena, burning sacrifices / witches / books (on Evolution or other heresies) seem more par for the course than Newtonian level revelations. Having said that, learning the laws of classical physics from observational data strikes me as a pretty natural task for the right kinds of machine architectures, since they represent essentially geometric symmetries which hold over a vast range of scales, space and time.
coding123 10y ago
I bet Google "could" write some algorithms that can predict if a stock will go up or down seconds before change based on live search data. All it takes is for an article or something to come out, then watch people search for "Will IBM stock drop" - and perform live sentiment analysis across all such live queries involving the stock name. In a sense, it's all about what information you have access to.
- bhewes 10y ago
  So the rumor is Goldman Sachs got out of the financial crash first because their systems saw the market move before anyone else. I don't know if that is apocrypha, but it is more to the point Google would not be predicting the market, but instead see market movements before anyone else.
  - lordnacho 10y ago
    I highly doubt that it is as technical as you seem to make it. GS is very well connected, and they are constantly out there asking people for their opinions. Ten years ago they probably had some sort of database where people could share experiences, and that is most likely what led to them guessing the financial crash. But it's not like they had some machine that one day told them to short mortgages.
    - bhewes 10y ago
      I checked you bio are developing your own HFT strats? I ask because I am connected with a couple startup HFs. Ping me on Symphony {Brian Hewes} if your up for chatting about it.
WalterBright 10y ago
The trouble with stock market systems is that any successful one is defeated by its own success - as its logic gets factored into everyone else's strategy. This self-defeating problem is not present in the other AI applications you mentioned.
- xapata 10y ago
  Any strategy a human can think of, they can also code as a program. The difficulty is in expressing one's strategy clearly.
- dougabug 10y ago
  Again, I'm not trying to provide an exhaustive survey of AI or ML applications. Merely point out that the fallacy of using past failures of AI and ML to conclude that they will continue to fail at tasks against which they have thus far made limited headway. Bear in mind that in the 90's only a handful of supercomputers had teraflops of computing power, where now you can get an 11 TFLOPS Titan X Ultimate for $1200. Compute power continues to grow exponentially, yet it has only recently reached a level where certain kinds of approaches are truly practical. As Heinlein said, "When it's time to go railroading, people go railroading." It's interesting that you should talk about antagonistic systems, since Actor-Critic Models, dueling architectures, Generative Adversarial Networks (GANs) are an extremely hot area of AI/ML research at the moment.
hendzen 10y ago
A question - with the answer left as an exercise for the reader: What do speech recognition, language translation, face recognition, object classification/detection have in common that is not true about predicting the future price of a security?
deleted 10y ago
[deleted]

rimantas 10y ago

Would Alpha go beat the same human on 17x17 or 21x21? Or even 13x13? Let's not get started about self-driving cars…

loup-vaillant 10y ago
Maybe not, because it has not been trained for those dimensions. Then again neither have humans (except maybe on 13x13). That could be interesting, but I'm not sure what we could learn from this.
educhana 10y ago
Absolutely, both Fan Hui and Lee Sedol. https://deepmind.com/research/alphago/ https://deepmind.com/research/alphago/
- pyrale 10y ago
  The link you provide does give no data on games played on 1717 or 2121. They were played on the standard 19*19 board.

vegabook 10y ago

The difference is that chess, go, etc are all essentially rules based. Finance has very few rules that do not break over time. Just look at QE. Arguably the financial market represents the collective intelligence of a huge amount of very clever people. Machines are only just starting to challenge a single human at a rules-based activity. We're very far from beating a brutally darwinian, impressively adaptive, human hive-mind whose main skill is figuring out when rules are about to get broken.

cm2187 10y ago
Interest rates cannot go negative was a commonly accepted rule at the beginning of my career (and interest rate option traders where using models that did not allow for negative interest rates).
snakeworld 10y ago
QE?
- timthorn 10y ago
  Quantitative Easing
- melling 10y ago
  Here's my first result from Google: https://en.m.wikipedia.org/wiki/Quantitative_easing https://en.m.wikipedia.org/wiki/Quantitative_easing Google is getting really good.
dougabug 10y ago
What are the rules for image / video captioning? For natural sounding speech synthesis? For realistic image generation? For semantic segmentation? For determining perceptual visual similarity between images? Frankly, rules based AI is basically a bust compared to learning from data approaches. The hive was pretty amazing at destabilizing the entire global economy within a brief span following fundamental financial de-regulation. People flatter themselves in ways which satisfies their egos and self-interest. Of course they want to believe in, and have everyone else credit, their magical superpowers. No government (backstop) wires necessary.

dilemma 10y ago

But the idea that you can predict the future is absurd. And even if you could, acting on that knowledge would change the future.

yjftsjthsd-h 10y ago
But the traders are trying to predict the future already! If it's impossible, then the machines are a shoe in, and if it is possible I'd still give the machines great odds.
peller 10y ago
The nuances of the game are different from strictly trying to predict the future. Casinos don't try to predict the future of every spin of the wheel or roll of the dice. Every individual game is random. But over a large enough sample size (number of games played), the odds are not random. The same principle is at play in trading the markets, except the "odds" are determined by the traders' skill - their "edge." Which is roughly some combination of access to the right people, access to better information (where better might but not necessarily mean quicker), and experience (gut feelings, hunches, what-have-you). So the real question is, can machine learning algorithms develop an "edge", and if so, can they stay solvent long enough to do so.
- dilemma 10y ago
  No. In casinos the games are designed so that in the long run the house always wins. They don't need to care about odds. The stock market isn't a game; it isn't designed and it continuously evolves.
  - peller 10y ago
    > In casinos the games are designed so that in the long run the house always wins. That's exactly what I meant by odds; they always win because the odds of the games are in their favor. > The stock market isn't a game It's a game in the same sense that life itself is a game. > it isn't designed The mechanisms that fit all the pieces together are very much designed by humans. True the behavior of the market isn't designed; it's an emergent property of the complex system we call "the markets." > it continuously evolves Yes, but change is also the constant. What underlies that change is supply and demand. Of course you're free to disagree. But if you'd like to learn more about the not-100%-accurate analogy I described in abbreviated form, the source is here: http://www.goodreads.com/book/show/253516.Trading_in_the_Zone http://www.goodreads.com/book/show/253516.Trading_in_the_Zon... Edit: Good reading on "life as a game": http://www.goodreads.com/book/show/189989.Finite_and_Infinite_Games http://www.goodreads.com/book/show/189989.Finite_and_Infinit...

Cybiote 10y ago

There are varying levels of sophistication from which data can be learned from. Pigeons can learn non-trivial word concepts and statistics but they do so at a rate that is much slower than a human infant. Thus far, machines have not done well in scenarios of low stationarity. Those are scenarios where the past is not so good a predictor of the future or where the data manifold is rapidly changing. This occurs for example, when the act of predicting changes what is being predicted. The related notion of Antipredictable Sequences is the most interesting consequence of the oft misconstrued notion of the No free lunch theorem. Humans are far from the ideal machine for this scenario but they vastly outmatch current algorithms. This is because humans are much better in the kind of generalization that is based off theory building. While not perfect, it does well enough at generalizing from observations (think Newton deriving the laws of gravitation from observations and Brahe's data) to scenarios that are not so close to the data. This means that for now and in complex environments, simpler models that are adaptable to change online are better. Additionally, their inspectability means they are more easily modified to better match any very changed dynamics. So your examples of: `speech recognition, face recognition, object classification and detection, speech` Are of the type where a learner, such as a deep network, whose generalization ability mostly hails from interpolating to fill in missing structure based on data (implying poor out of sample performance) does well when you can give lots of examples which provide good coverage. Language translation is sort of inbetween (the space is not as smooth but is still fairly stationary at the scale of years) and so we also find that the advantages of deep learning have not been as pronounced and dramatic as has been the case for image and speech. The digital machine advantage is two. Their memory capacity is outmatched. Second, and related to the first, energy needs are not so pressing a concern.

dougabug 10y ago
No one is suggesting that AI is a solved problem. I simply cited a handful of areas such as computer vision where progress in ML has been remarkably rapid after many years where the problems seemed daunting and skeptics had written off the prospects of the field. Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking. How often do most human beings come to exhibiting the level of insight and reasoning power required to construct the calculus and discover the laws of classical mechanics? Inventing supernatural explanations for natural phenomena, burning sacrifices / witches / books (on Evolution or other heresies) seem more par for the course than Newtonian level revelations. Having said that, learning the laws of classical physics from observational data strikes me as a pretty natural task for the right kinds of machine architectures, since they represent essentially geometric symmetries which hold over a vast range of scales, space and time.

coding123 10y ago

I bet Google "could" write some algorithms that can predict if a stock will go up or down seconds before change based on live search data. All it takes is for an article or something to come out, then watch people search for "Will IBM stock drop" - and perform live sentiment analysis across all such live queries involving the stock name. In a sense, it's all about what information you have access to.

bhewes 10y ago
So the rumor is Goldman Sachs got out of the financial crash first because their systems saw the market move before anyone else. I don't know if that is apocrypha, but it is more to the point Google would not be predicting the market, but instead see market movements before anyone else.
- lordnacho 10y ago
  I highly doubt that it is as technical as you seem to make it. GS is very well connected, and they are constantly out there asking people for their opinions. Ten years ago they probably had some sort of database where people could share experiences, and that is most likely what led to them guessing the financial crash. But it's not like they had some machine that one day told them to short mortgages.
  - bhewes 10y ago
    I checked you bio are developing your own HFT strats? I ask because I am connected with a couple startup HFs. Ping me on Symphony {Brian Hewes} if your up for chatting about it.

WalterBright 10y ago

The trouble with stock market systems is that any successful one is defeated by its own success - as its logic gets factored into everyone else's strategy. This self-defeating problem is not present in the other AI applications you mentioned.

xapata 10y ago
Any strategy a human can think of, they can also code as a program. The difficulty is in expressing one's strategy clearly.
dougabug 10y ago
Again, I'm not trying to provide an exhaustive survey of AI or ML applications. Merely point out that the fallacy of using past failures of AI and ML to conclude that they will continue to fail at tasks against which they have thus far made limited headway. Bear in mind that in the 90's only a handful of supercomputers had teraflops of computing power, where now you can get an 11 TFLOPS Titan X Ultimate for $1200. Compute power continues to grow exponentially, yet it has only recently reached a level where certain kinds of approaches are truly practical. As Heinlein said, "When it's time to go railroading, people go railroading." It's interesting that you should talk about antagonistic systems, since Actor-Critic Models, dueling architectures, Generative Adversarial Networks (GANs) are an extremely hot area of AI/ML research at the moment.

hendzen 10y ago

A question - with the answer left as an exercise for the reader: What do speech recognition, language translation, face recognition, object classification/detection have in common that is not true about predicting the future price of a security?

deleted 10y ago

[deleted]