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While I get what you're saying, isn't that a very "supervised ML" way of thinking? In a world where unsupervised learning was easier to apply, it might be poss

While I get what you're saying, isn't that a very "supervised ML" way of thinking? In a world where unsupervised learning was easier to apply, it might be possible to start with data, compress it to a low-dimensional manifold, and use that understanding as inspiration for better directions/questions to pose. So I guess the real problem is that humans expect anything branded as "intelligence" to have the ability to learn in an unsupervised manner, while breakthroughs are closer to the regime where the goals are supervised while the solutions are discovered. Of course, the amount of supervision probably lies on a continuum, between everything being completely specified (classical programming) and everything unsupervised ("true" "human" intelligence).

taeric 8y ago
Lots of data does nothing to help when the person you are talking to is not given a) knobs to turn, and b) a goal to achieve. To that end, just having loads of data doesn't necessarily help. You also have to have loads of control over some part of the system. And even then, you can't expect ML to help, all on its own. So, the worst is folks that think just because you have full trace logs of your system, it should be trivial to apply anomaly detection. At face value, yes. Yes you can. But also at face value, your trace logs aren't free and it isn't like folks are keen on letting their detection system cut off anomalous transactions without some sort of kill switch.
- ssivark 8y ago
  I think my point may not have gotten across clearly, so let me clarify. I agree with most of what you said, which IMHO comes under the spirit of supervised machine learning paradigm, which is the most advanced tool we have pragmatically available to use today. Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. Many people (including some experts, rightly or wrongly) believe that neural networks might be that model class, and (clever tweaks of) gradient descent might be an acceptable learner. All I'm saying is that the current hype about AI fails to separate the potential of the latter class from the currently available successful tools of the former class.
  - nerdponx 8y ago
    Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. In my experience, at least, the value in this kind of activity is relatively low. Sometimes you find something, usually you just find a low-rank version of nothing in particular. Unless some significant developments appear in this space (and they very well might), the best low-dimensional approximations will be the means, variances, and correlations computed by business analysts, which are tremendously valuable quantities to keep in mind at all times.
    - speedplane 8y ago
      > In my experience, at least, the value in this kind of activity is relatively low. Sometimes you find something, usually you just find a low-rank version of nothing in particular. Unless some significant developments appear in this space (and they very well might), the best low-dimensional approximations will be the means, variances, and correlations computed by business analysts, which are tremendously valuable quantities to keep in mind at all times. Definitely. Humans find and reject "low rank" correlations all the time. For example, North America and South America are both part of the "new world", but no sociologist would use that to make general predictions about the people on the continent. People both like steak and cookies, and would conceivably fall into a "low rank" "humans like this category", but they couldn't be more dissimilar in many respects. Conversely, some doctors have an encyclopedic knowledge of various ailments, and either memorize or infer from experience a particular ailment from a huge collection of possible ailments. If the number of possible ailments is large, their "understanding" is not low rank, it's the opposite. Low rank does not necessarily mean "understanding", it's just a local minimum in some function of a random variable.
      - ssivark 8y ago
        I appreciate the thought-provoking examples :-) > Humans find and reject "low rank" correlations all the time ... steak and cookies ... Sure, but the appropriateness of a low rank approximation depends on what you want to predict. Eg: for predicting basketball success, {height, wingspan} might be a very useful "low rank" description, while for predicting obesity the relevant low rank description might be the body mass index (BMI) or something like it. Wingspan = chest width + 2 * arm length and BMI = weight/height^2 could be composite features "discovered" by such a model+inference toolkit. > Conversely, some doctors have an encyclopedic knowledge of various ailments, and either memorize or infer from experience a particular ailment from a huge collection of possible ailments. If the number of possible ailments is large, their "understanding" is not low rank, it's the opposite. That's exactly why I would not call that understanding, especially if they have to memorize a book full of ailments and symptoms. "Understanding" would entail a (causal) model of underlying physiological problems and the symptoms they give rise to, with a recipe for inferring in reverse. > Low rank does not necessarily mean "understanding", it's just a local minimum in some function of a random variable. I don't understand that statement at all. PS: I don't have any particular affinity to neural networks over other ML models, and don't mean to escalate the hype :-)

taeric 8y ago

Lots of data does nothing to help when the person you are talking to is not given a) knobs to turn, and b) a goal to achieve. To that end, just having loads of data doesn't necessarily help. You also have to have loads of control over some part of the system. And even then, you can't expect ML to help, all on its own. So, the worst is folks that think just because you have full trace logs of your system, it should be trivial to apply anomaly detection. At face value, yes. Yes you can. But also at face value, your trace logs aren't free and it isn't like folks are keen on letting their detection system cut off anomalous transactions without some sort of kill switch.

ssivark 8y ago
I think my point may not have gotten across clearly, so let me clarify. I agree with most of what you said, which IMHO comes under the spirit of supervised machine learning paradigm, which is the most advanced tool we have pragmatically available to use today. Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. Many people (including some experts, rightly or wrongly) believe that neural networks might be that model class, and (clever tweaks of) gradient descent might be an acceptable learner. All I'm saying is that the current hype about AI fails to separate the potential of the latter class from the currently available successful tools of the former class.
- nerdponx 8y ago
  Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. In my experience, at least, the value in this kind of activity is relatively low. Sometimes you find something, usually you just find a low-rank version of nothing in particular. Unless some significant developments appear in this space (and they very well might), the best low-dimensional approximations will be the means, variances, and correlations computed by business analysts, which are tremendously valuable quantities to keep in mind at all times.
  - speedplane 8y ago
    > In my experience, at least, the value in this kind of activity is relatively low. Sometimes you find something, usually you just find a low-rank version of nothing in particular. Unless some significant developments appear in this space (and they very well might), the best low-dimensional approximations will be the means, variances, and correlations computed by business analysts, which are tremendously valuable quantities to keep in mind at all times. Definitely. Humans find and reject "low rank" correlations all the time. For example, North America and South America are both part of the "new world", but no sociologist would use that to make general predictions about the people on the continent. People both like steak and cookies, and would conceivably fall into a "low rank" "humans like this category", but they couldn't be more dissimilar in many respects. Conversely, some doctors have an encyclopedic knowledge of various ailments, and either memorize or infer from experience a particular ailment from a huge collection of possible ailments. If the number of possible ailments is large, their "understanding" is not low rank, it's the opposite. Low rank does not necessarily mean "understanding", it's just a local minimum in some function of a random variable.
    - ssivark 8y ago
      I appreciate the thought-provoking examples :-) > Humans find and reject "low rank" correlations all the time ... steak and cookies ... Sure, but the appropriateness of a low rank approximation depends on what you want to predict. Eg: for predicting basketball success, {height, wingspan} might be a very useful "low rank" description, while for predicting obesity the relevant low rank description might be the body mass index (BMI) or something like it. Wingspan = chest width + 2 * arm length and BMI = weight/height^2 could be composite features "discovered" by such a model+inference toolkit. > Conversely, some doctors have an encyclopedic knowledge of various ailments, and either memorize or infer from experience a particular ailment from a huge collection of possible ailments. If the number of possible ailments is large, their "understanding" is not low rank, it's the opposite. That's exactly why I would not call that understanding, especially if they have to memorize a book full of ailments and symptoms. "Understanding" would entail a (causal) model of underlying physiological problems and the symptoms they give rise to, with a recipe for inferring in reverse. > Low rank does not necessarily mean "understanding", it's just a local minimum in some function of a random variable. I don't understand that statement at all. PS: I don't have any particular affinity to neural networks over other ML models, and don't mean to escalate the hype :-)