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Featured Limits of Machine Learning?

  1. Jul 27, 2017 #1
    From what I understand, machine learning is incredibly good at making predictions from data in a very automated/algorithmic way.

    But for any inference that is going to deal with ideas of causality, it's primarily a subject matter concern, which relies on mostly on judgment calls and intuition.

    So basically, a machine learning algorithm would need human level intelligence and intuition to be able to do proper causal analysis?

    Here's an example where there might be issues.

    Say, an ML algorithm finds that low socioeconomic status is associated with diabetes with a significant p value. We clearly know that diabetes is a biological phenomena and that any possible(this is a big if) causal connection between a non biological variable such as low SES and diabetes must logically have intermediate steps between the two variables within the causal chain. It is these unknown intermediate steps that probably should be investigated in follow up studies. We logically know(or intuit from prior knowledge+domain knowledge) that low SES could lead to higher stress or unhealthy diet, which are biological. So a significant pval for SES indicates that maybe we should collect data on those missing variables, and then redo the analysis with those in the model.

    But there's no way a learning algorithm can make any of those connections because those deductions are mostly intuition and logic, which are not statistical. Not to mention, how would ML look at confounders?
     
    Last edited: Jul 28, 2017
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  3. Jul 28, 2017 #2

    Stephen Tashi

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    Why do you think human level intelligence and intuition is capable of doing a proper causal analysis?

    Human level intelligence hasn't reached a consensus about the definition of causality yet. If a "proper casual analysis" is concept known only to particular person's intuition then I agree that it takes a human being to know such a thing.
     
  4. Jul 28, 2017 #3
    Humans can't do causal analysis perfectly, that's true. But we do have a better idea of what causality is, even if it's not perfectly defined. Also, humans narrow things down much better through deductive reasoning. In the example I gave, the algorithm wouldn't be able to narrow down what those latent variables are, simply because they might have been considered in the first place, and hence are not in the data set. A human analyst would think, "Ah ha! since SES is associated with diabetes, maybe low SES causes something( e.g stress) that leads to diabetes, so hindsight shows maybe we should collect data from that". So the results leads to new insights and avenues of investigation that was never thought of before. Essentially it takes detective work to do causal inference.

    But if there already is data on every possible thing about diabetics(DNA, all biochemicals etc), and advanced learning algorithms that stably run models on millions of variables, then it is conceivable that an ML algorithm can get the answer blindly(or at least with subhuman intelligence) in one go without logical deduction. I'm not sure if this is mathematically possible, but if it is, then they beat humans at causal analysis.
     
  5. Jul 28, 2017 #4

    Dale

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    So far, I am not convinced that machines are particularly good at learning. For example, an average human teenager can learn to drive a car with about 60 hours of experience. The machines that are learning the same task have millions of hours and are far from average. Similarly, even a below average human toddler can learn to speak any language with far less data than is available to computers attempting the same task.
     
  6. Jul 28, 2017 #5
    True. One of the biggest hinderances to AI is pattern recognition, in which they can do only in very well controlled settings. The fact that they can't switch tasks well implies that machine's intuition about things are basically non existent. However, they are phenomenal at rapid calculation, which means they can do conceptually easy but extensive tasks.
     
  7. Jul 29, 2017 #6

    Merlin3189

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    I'm not sure whether your question / statement relates to AI we have now, or to what can eventually be achieved. I agree that what we have now is very limited, but I believe that someone can eventually build AI that will match the best human brains. I suspect that AI will be able exceed HI, simply because it can already beat us in some tasks, so just add those to the HI skills when it acquires them. (Though that is rather like us using computers, so maybe it still counts as just our equal.)

    My reason is simply that I am surrounded by machines doing all the things that AI can't do. For me the main goal of AI is not to replace these HI machines, but to understand how they work.

    As you say, the sort of thinking you esteem - intuition, logic(?), judgement, deduction, experience, guesswork, prejudice, (I'm extending your list a bit!) , etc - may be outside the reach of current AI. So how are these machines (the humans) doing it? What is it that they can do, in concrete definable terms, that we haven't yet put into AI? Either we say, that is unknowable and psychologists are wasting their time, or our understanding of psychology will grow and we will incorporate it into AI.

    If one believes in some magical ether in the human brain - gods, human spirit, animus, life, ... ? - then obviously only machines endowed with this stuff can do these ill-defined things. Otherwise, what is the reason, other than we don't know what they are, that we can't incorporate these skills into AI machines?

    This is a psychological perspective and I think most people in AI are more in the engineering camp. So I expect AI to continue to get better in specialised tasks, using algorithms not particularly related to HI. Progress in HI may (?) usefully help get us over some of the bumps, but will we be that keen on AI systems when they start to display the same faults as HI systems? If driverless cars did get as good as human driven ones, we'd still accept human error as, well, human, but computer error is another matter. How much better than HI will AI need to become? .
     
  8. Jul 29, 2017 #7
    Whether humans will be able to create these type of thinking will likely depend on the actual complexity of those tasks in comparison to current tasks executable by AI. For example, feeling an emotion might seem easier than computing a complicated integral to a human, but it's just the opposite. Computing an integral is just the adding up of many smaller parts, few concepts needed. But an "emotion" or gut feel intuition could have much more rich and complex mathematical algorithms with many interrelated concepts that we have not even thought of yet. It's possible that such ideas are so mathematically complex that even the smartest AI scientist/mathematician would never deduce the patterns, even though the patterns are happening in physical spacetime inside a biological machine. If all this is true, then I don't know if humans will ever figure this out because the upper limit of human brain capacity is evolutionarily limited by the size of the birth canal and we probably need a mind far greater than Einstein to really understand consciousness.

    For simple repetitive tasks or tasks requiring simple low level concepts, AI will likely surpass humans at all of these, given enough training data.
     
  9. Jul 29, 2017 #8

    Merlin3189

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    Yes, that is a worry. It may be like turbulence: we'll get some ideas about it, extract some general principles, but maybe never get on top of the detail.
    My own feeling about the brain is that it's basic elements are really quite simple, but like the molecules of a fluid, when you get enough of them involved, even simple deterministic properties can lead to fundamentally unpredictable behaviour.
     
  10. Jul 29, 2017 #9

    atyy

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    I agree that machines have a long way to go before reaching human level performance. But is it true that they have access to the same data as humans? For example, in addition to the 60 hours of experience a teenager needs to learn to drive, that teen already spent 16 years acquiring other sorts of data while growing up. Similarly, the toddler is able to crawl about and interact in the real world, which is a means of data acquisition the computers don't have.
     
  11. Jul 29, 2017 #10

    Dale

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    That is a good point, but I think that shows even more how amazing the human brain is at learning. It can take that general knowledge from walking and running and playing and use it to inform the ability to drive. I don't think data from walking would help a machine learn to drive.
     
  12. Aug 22, 2017 #11

    Demystifier

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    I am probably missing something trivial, but millions of hours means hundreds of years. But we do not have such machines for that long. So how do they learn?
     
  13. Aug 22, 2017 #12

    Demystifier

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    So perhaps we need an AI kindergarten, as proposed by my brother:
    https://www.linkedin.com/pulse/ai-kindergarten-what-does-take-build-truly-machine-danko-nikolic
     
  14. Aug 22, 2017 #13
    I don't know much about this topic, but this is partly related and also somewhat amusing (this is quite a recent video):



    The relevant part starts around 5 minutes or so. Though I think people mostly tend to think of programs versus humans only in the context of strategy games (talking about video games).

    For arcade games, for example, there are already easy TAS (tool assisted) runs for lots of games. But they are hardly any fun to watch at all (except to see the limits) --- as compared to human replays/videos. Because the fun part is in experience of hand-eye coordination, visual cognition, mechanical perfection etc. Judgement is just one part of playing.

    Actually something similar will apply to FPS and many other more action related genres.

    In strategy games judgement seems to play a bigger part (as compared to other factors) so it is more amusing to see a program playing very well. And also fog of war (in RTS or derivative genres) tends to add a large element of imperfect information (and it is fun to see how a program handles that).

    I think (just from a layman perspective) that's probably because of raw computation power they can replay the same scenarios over and over in a very short period of time.
     
  15. Aug 22, 2017 #14

    phyzguy

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    I think you're missing the point of how machine learning is being increasingly done today. Many (perhaps most) machine learning tools today are not algorithmic in nature. They use neural networks configured in ways similar to the human brain, and then train these networks with learning sets, just like a human is trained to recognize patterns. Even the (human) designer of the neural network doesn't know how the machine will respond to a given situation. Given this, I don't see why these artificial neural networks cannot match or eventually exceed human capability. Indeed, I think Google's facial recognition software is already exceeding human capability. Granted this is in a controlled environment, but given time and increasing complexity of the networks (and increasing input from the environment), I think you will see these machines able to do anything a human mind can do.
     
  16. Nov 1, 2017 #15
  17. Nov 2, 2017 #16
    Hi there,
    Here is Danko. Hello everyone. Writing an article is maybe a bit too much for me right now. But I would be glad to answer questions. Here is two comments to what has been said before:
    - Does AI have access to the same data as humans? In my opinion, at one important level the answer is NO. This is the knowledge we have stored in our genes. We should think about genes as a small but very extensively trained (millions if not billions of years) machine learning component that assists every toddler's learning. Without having in genes knowledge on what to learn and how to learn (we usually refer to those as instincts), a toddler could not do any of its intelligence magic. And this is the key problem: how to provide for an AI this millions-of-years-experience wisdom that we are born with? How do we provide an AI with the data that our ancestors used throughout evolution to get us the genes that we have? (not to mention the computational power needed to work AIs way through these data.)

    - Can today's artificial neural networks eventually match or exceed human capabilities? I have written an article explicitly dealing with that problem: According to my calculation, the answer is NO. A good news is that I also propose organisation of AI that possibly could do that. One can download the paper here:

    http://www.ijac.net/EN/article/downloadArticleFile.do?attachType=PDF&id=1958

    I know that the paper is technical and scientific and that people would prefer a digest. Maybe you can just take a look at the abstract.

    I hope that this is useful.
    Best,
    Danko
     
  18. Nov 2, 2017 #17

    Demystifier

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    As you can see above, I just did it. :smile:
     
  19. Nov 2, 2017 #18
    Today is like Christmas! This is almost as exciting as was meeting Roger Penrose in person last year :D

    Honoured to make your acquaintance. I'm at work currently so I cannot spend too much time reading the paper, but I will do so asap.

    In the meantime I was hoping that you could elaborate on the dynamical systems description of practopoiesis, specifically that thinking was akin to changing the parameters of this system, and how new thoughts occur during phase transitions i.e. during bifurcation of this system.

    Did you happen to have some specific equations and parameters in mind and how would these would be changed physically? And for humans/animals, should we be thinking about these as simple attractors detectable through analysis or more like some high dimensional attractors, perhaps akin to some Kuramoto type network model?
     
  20. Nov 2, 2017 #19
    I haven't made an interpretation based on dynamical systems. One could but I was never sure that this would be particularly insightful. Maybe it would, but one would have to try first. Instead, I focused on cybernetic/control-theory interpretation.

    A dynamical system would need to be described by stochastic differential equations.

    Still, intuitively, an interpretation of practopoietic hierarchy (traverses) based on dynamical systems would be quite simple to understand I think. There is nothing especially complicated about it -- at least not in principle. You just need to imagine two dynamical systems, one that operates fast (F, say updated every second) and one that operates slowly (S, say updated every five hours). Now we need the following conditions:
    - the value of at least one parameter of F is being decided/adjusted by S. But S cannot affect the dynamics of F in any other way.
    - In contrast, F cannot affect the parameters of S, but the accumulated results of the dynamics of F become a part of the dynamics of S. Thus, the dynamics of F affect the dynamics of S.

    This is all.

    The two are sort of asymmetrically coupled: In one direction they interact through parameters of a dynamical system, S -> F; in the other direction they interact through dynamics: F -> S. This results in practopoietic loop of causation: http://www.danko-nikolic.com/practopoietic-cycle-loop-of-causation/

    It may be difficult to make a mental click to understand what I am talking about. But once the click occurs, it is very easy to think about these systems. There is no immense complexity that often occurs with dynamical systems. This is precisely because the two operate at different speeds. So, whenever you think about the fast one, you can neglect the operations of the slow one, and when you think about the slow one, you can approximate the operations of the fast one with some simple function (mean + noise).

    Kuramoto types of networks are in my understanding not particularly relevant here.

    Their interaction is such that S induces bifurcations of F, but not vice versa. In contrast, the dynamics of F accumulated over time, makes a part of the dynamics of S.

    Critical is that S has knowledge on when and in which direction to change the parameters of F. And to discuss that further, we have to define certain "goals" or "target values" that S and F are trying to achieve. And this leads us to attractors. We can say that S has an attractor state, much like any regulator.

    As to particular equations, you can use any equations you want. This is completely unlimited for as long as they satisfy the conditions mentioned above.

    I hope this is understandable.
    Danko
     
  21. Nov 2, 2017 #20
    Is the distinction of algorithm and function of "some relevance" in this topic (which I don't know anything about) in general?

    Quite simple way to describe it is suppose we have a function that takes an array as input and outputs a sorted array. The function is unique but we distinguish between various "algorithms"/"methods" for achieving this.

    I am not sure that this distinction can be made fully mathematically rigorous (possibly in same way as "efficient computation" or "natural examples" etc. "surely" can't be made mathematically rigorous but perhaps could be defined in practically useful ways).

    Speaking quite generically, I am thinking along the lines that while stimulus and response are important parts of interaction with environment ---- possibly the representation of information internally is also of some importance (and this seems to be related with the function/algorithm distinction).
     
  22. Nov 3, 2017 #21

    Demystifier

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    Why stochastic? Why not deterministic?
     
  23. Nov 3, 2017 #22
    Because the interaction is between an organism and its environment. A real environment is unpredictable; you never get into an identical situation twice, and the environment never responds twice in the same way to your actions. Therefore, from the perspective of differential equations the interaction has a considerable stochastic component.
     
  24. Nov 3, 2017 #23

    Demystifier

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    OK, but from dynamical-systems perspective, unpredictable behavior of the environment can be a result of deterministic chaos. In the end, there may be no much apparent difference between stochastic and chaotic modeling of the environment, and the former may be simpler to implement in a computer simulation, but the latter seems more realistic from the fundamental physical point of view.

    Anyway, this all looks like a red herring, as I agree with you that dynamical-systems perspective is not very useful here.
     
    Last edited: Nov 3, 2017
  25. Nov 3, 2017 #24
    I just read the paper you linked earlier. The part about how slowly learned genetic policies enable networks themselves to gain knowledge about fast adaptation policies, causing the operation of adaptation policies to directly provide stimuli with their best interpretation, in other words an actual explanation for what 'understanding' may entail, simply blew me away.

    This actually answers many long-standing philosophical questions in the philosophy of mind, including what qualia may be.
    I am sure that I read somewhere a dynamical system description/metaphor of practopoiesis; this was namely what caused the click with my own (nowhere nearly as sufficiently developed) ideas about cognitive states as being represented as points in some state space.
    The existence of such attractors are precisely why I opt for a dynamical systems description. If, as in a regular cognitive setting, many different aspects of some perceived phenomenon are to be evaluated on the same time scales, i.e. different network policies are executed in parallel, this implies that these multiple outputs together form some attractor and that similar behavior may be evoked by activating all or many of these network policies as if just a few or even one of these network policies was activated.
    I disagree, the usefulness of the dynamical systems perspective all depends on what a theory is aiming to explain and at what level; the perspective enables the rapid creation of experimentally checkable hypotheses which may otherwise not be appearant to check at all to those thinking directly about some naturally occuring system or using statistics to do their hypothesis testing for them. This can happen completely outside of the context of the original theory, in this case AI.

    Here are some examples, 'the dynamics in the rewiring of networks into most conducive for abductive reasoning either will or will not exhibit small world characteristics'. Or, 'the equi-level synchronised activation of different network policies implies that synchronised chaos may exist across many cognitive states'. Or even, 'a sudden discontinuous increase in cognitive capacities is to be expected when comparing different species which have evolved genetic policies capable of creating small world neural networks compared with those without such policies'. Such 'insights' are far more easily generated than if one were to rely on logical deduction alone, and once envisioned they naturally raise tonnes more questions, all which definitely seem checkable in some way.

    Moreover, evidence can and often already has been gained from other both top-down and/or bottom-up researchers not looking for such patterns, meaning we can rapidly falsify models in this way. We can even use the perspective to tie together many different sciences in novel ways, e.g. as done here when analysing human languages as naturally evolved dynamical systems, directly also leading to new results in completely orthogonal directions, such as towards the subject we are actually discussing here.

    As you yourself say, many spontaneous behaviors in the environment and induced by the environment on some systems need not be strictly stochastic given deterministic chaos. The nice thing is that extremely complicated but typical behavior will tend to fall on an attractor. If the goal is identifying and characterizing such possibly immensely complicated attractors, I don't see how one would do that without phase space reconstruction and/or other tools inherent to a dynamical systems perspective.

    Lastly, on a more abstract level, it seems all complexity science subjects, such as cybernetics, chaos, (nonlinear) dynamical systems, network theory and so on, share an underlying mathematical backbone which is currently, as a field of mathematics, still a work in progress, perhaps one extremely relevant to physics. Many great mathematicians and physicists, both historical and contemporary (e.g. Benoit Mandelbrot, Floris Takens, John Baez, Stephen Strogatz) have made this point and I tend to agree with them on it.
     
  26. Nov 4, 2017 #25

    Pythagorean

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