3 ms·

So, a bunch of symbolic logic vets are bitter that statistical techniques are producing better results than they were ever able to?

_delirium 15y ago
I don't see them as opposed to statistical techniques, at least in this interview, just a focus on methods as ends-in-themselves, rather than tools to be used. Plenty of statistical-AI people themselves hold similar views on that subject, and almost every machine-learning or AI conference has a panel or keynote that expresses dismay at what the field looks like (balkanized, too focused on methods rather than problems, too focused on incremental algorithm tweaks). For example, Leslie Pack Kaelbling's AAAI-2010 keynote had the thesis that we need to stop focusing on incremental algorithm improvements in tiny sub-areas of AI, tested on implausibly pure toy benchmark problems with a focus on optimality proofs, and start focusing more on putting things together into integrated systems that handle real-world problems, which will probably require more hybrid methods (like statistical-AI + logical-AI). Though she expressed it a bit more diplomatically, with the tone that we're not fulfilling the potential of the field with the current approach, and has actually done some concrete work following her own proposal. (Slides: http://people.csail.mit.edu/lpk/AAAI10LPK.pdf http://people.csail.mit.edu/lpk/AAAI10LPK.pdf)
- jimbokun 15y ago
  One thing I find disconcerting, are more and more complex models that "work" in the sense of predicting what will happen, but the model is too complex to give humans insight into what is happening. Does that mean that the universe is just inherently too complicated for humans to understand, or do we need models that better lend themselves to explaining the phenomena they predict?
  - _delirium 15y ago
    That's been one disconnect between machine learning and some other applied areas as well. For example, vanilla decision trees aren't really seen as a state-of-the-art ML technique anymore, but they still get a ton of use in medicine, environmental science, and the social sciences, because they're seen as more interpretable, rather than a black-box predictor. There is a bit of work on interpretable ML aiming to bridge that gap, though.
  - cellis 15y ago
    Great comment. It appears to be a huge problem in synthetic biology too. For instance you can often, using genetic programming, find a gene that is far more fit than anything synthetic bioengineering would produce. The problem is genetic programming is undocumented, so you don't know how/why the gene is fit.

_delirium 15y ago

I don't see them as opposed to statistical techniques, at least in this interview, just a focus on methods as ends-in-themselves, rather than tools to be used. Plenty of statistical-AI people themselves hold similar views on that subject, and almost every machine-learning or AI conference has a panel or keynote that expresses dismay at what the field looks like (balkanized, too focused on methods rather than problems, too focused on incremental algorithm tweaks). For example, Leslie Pack Kaelbling's AAAI-2010 keynote had the thesis that we need to stop focusing on incremental algorithm improvements in tiny sub-areas of AI, tested on implausibly pure toy benchmark problems with a focus on optimality proofs, and start focusing more on putting things together into integrated systems that handle real-world problems, which will probably require more hybrid methods (like statistical-AI + logical-AI). Though she expressed it a bit more diplomatically, with the tone that we're not fulfilling the potential of the field with the current approach, and has actually done some concrete work following her own proposal. (Slides: http://people.csail.mit.edu/lpk/AAAI10LPK.pdf http://people.csail.mit.edu/lpk/AAAI10LPK.pdf)

jimbokun 15y ago
One thing I find disconcerting, are more and more complex models that "work" in the sense of predicting what will happen, but the model is too complex to give humans insight into what is happening. Does that mean that the universe is just inherently too complicated for humans to understand, or do we need models that better lend themselves to explaining the phenomena they predict?
- _delirium 15y ago
  That's been one disconnect between machine learning and some other applied areas as well. For example, vanilla decision trees aren't really seen as a state-of-the-art ML technique anymore, but they still get a ton of use in medicine, environmental science, and the social sciences, because they're seen as more interpretable, rather than a black-box predictor. There is a bit of work on interpretable ML aiming to bridge that gap, though.
- cellis 15y ago
  Great comment. It appears to be a huge problem in synthetic biology too. For instance you can often, using genetic programming, find a gene that is far more fit than anything synthetic bioengineering would produce. The problem is genetic programming is undocumented, so you don't know how/why the gene is fit.