Machine Learning (Theory)

2/4/2006

Research Budget Changes

Tags: Funding,Research jl@ 2:44 am

The announcement of an increase in funding for basic research in the US is encouraging. There is some discussion of this at the Computing Research Policy blog.

One part of this discussion has a graph of NSF funding over time, presumably in dollar budgets. I don’t believe that dollar budgets are the right way to judge the impact of funding changes on researchers. A better way to judge seems to be in terms of dollar budget divided by GDP which provides a measure of the relative emphasis on research.

This graph was assembled by dividing the NSF budget by the US GDP. For 2005 GDP, I used the current estimate and for 2006 and 2007 assumed an increase by a factor of 1.04 per year. The 2007 number also uses the requested 2007 budget which is certain to change.

This graph makes it clear why researchers were upset: research funding emphasis has fallen for 3 years in a row. The reality has been significantly more severe due to DARPA decreasing funding and industrial research labs (ATnT and Lucent for example) laying off large numbers of researchers about when the governments emphasis on basic research started declining.

It is certainly encouraging to see the emphasis on science growing again.

2/2/2006

Introspectionism as a Disease

Tags: AI,Machine Learning jl@ 11:41 am

In the AI-related parts of machine learning, it is often tempting to examine how you do things in order to imagine how a machine should do things. This is introspection, and it can easily go awry. I will call introspection gone awry introspectionism.

Introspectionism is almost unique to AI (and the AI-related parts of machine learning) and it can lead to huge wasted effort in research. It’s easiest to show how introspectionism arises by an example.

Suppose we want to solve the problem of navigating a robot from point A to point B given a camera. Then, the following research action plan might seem natural when you examine your own capabilities:

  1. Build an edge detector for still images.
  2. Build an object recognition system given the edge detector.
  3. Build a system to predict distance and orientation to objects given the object recognition system.
  4. Build a system to plan a path through the scene you construct from {object identification, distance, orientation} predictions.
  5. As you execute the above, constantly repeat the above steps.

Introspectionism begins when you believe this must be the way that it is done.

Introspectionism arguments are really argument by lack of imagination. It is like saying “This is the only way I can imagine doing things, so it must be the way they should be done.” This is a common weak argument style that can be very difficult to detect. It is particularly difficult to detect here because it is easy to confuse capability with reuse. Humans, via experimental tests, can be shown capable of executing each step above, but this does not imply they reuse these computations in the next step.

There are reasonable evolution-based reasons to believe that brains minimize the amount of computation required to accomplish goals. Computation costs energy, and since a human brain might consume 20% of the energy budget, we can be fairly sure that the evolutionary impetus to minimize computation is significant. This suggests telling a different energy-conservative story.

An energy consevative version of the above example might look similar, but with very loose approximations.

  1. The brain might (by default) use a pathetically weak edge detector that is lazily refined into something more effective using time-sequenced images (since edges in moving scenes tend to stand out more).
  2. The puny edge detector might be used to fill a rough “obstacle-or-not” fill map that coarsens dramatically with distance.
  3. Given this, a decision about which direction to go next (rather than a full path) might be made.

This strategy avoids the need to build a good edge detector for still scenes, avoids the need to recognize objects, avoids the need to place them with high precision in a scene, and avoids the need to make a full path plan. All of these avoidances might result in more tractable computation or learning problems. Note that we can’t (and shouldn’t) say that the energy conservative path “must” be right because that would also be introspectionism. However, it does exhibit an alternative showing the failure of imagination in introspectionism on the first approach.

It is reasonable to take introspection derived ideas as suggestions for how to go about building a (learning) system. But if the suggestions don’t work, it’s entirely reasonable to try something else.

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