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		       6.XXX TALK: ROBERT AJEMIAN

			      Dylan Holmes
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			       2019/May/6


- If we are to capture human learning and cognition, we must envision
  the exotic architectures that make them possible.
  - New models of brain hardware can shed light on new models of brain
    software. 
  - Design rule: Fit the approach to the problem you're trying to solve.
  - The trendiest tools are not always the best tools for the job.
  - For example, modern computer systems are not the best metaphor for
    how brain hardware works. (6 key differences between computers and
    brains)
  - And artificial neural networks with backprop are not the best model
    of how brains learn. (6 unrealistic aspects of back prop)
    - In particular, backprop requires global lockstep coordination
      where brains are mostly asyncronous and local; and even recurrent
      neural networks pass information in one direction rather than
      many, as in the brain (compare recursive functions vs coroutines.)
  - And artificial neural networks with backprop are not the best
    engineering solution for every learning problem.
    - "Bulldozer computing" works well on object recognition, but fails
      on motor control (which has too-large state space) and medical
      diagnosis (which integrates many kinds of info.)
    - We should tailor new tools to such problems. 
    - All of the recent  ML performance improvements have come not from new
      ideas about learning, but rather from advances in computing power and
      data size.
  - Moore's law will asymptote.
  - We must imagine new exotic architectures and learning mechanisms,
    tailored to the problems we're trying to solve. 
  - Surprise: There are other kinds of "neural networks" that are better
  brain models or better at solving ML problems.
     - Marr-Albus model of the cerebellum.
     - Hopfield networks
     - Self-organizing maps
     - Decision trees.



(C) 2019 Dylan Holmes. This work is licensed under the Creative Commons 
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