On Aug 20, 2006, at 11:15 AM, Matt Mahoney wrote:
The argument for lossy vs. lossless compression as a test for AI
seems to be motivated by the fact that humans use lossy compression
to store memory, and cannot do lossless compression at all. The
reason is that lossless compression requires the ability to do
deterministic computation. Lossy compression does not.
I think this needs to be qualified a bit more strictly in real (read:
finite) cases. There is no evidence that humans are incapable of
lossless compression, only that lossless compression is far from
efficient and humans have resource bounds that generally encourage
efficiency. A distinction with a difference. Being able to recite a
text verbatim is a different process than reciting a summary of its
semantic content, and humans can do both.
Even a probabilistic (e.g. Bayesian) computational model can
reinforce some patterns to the point where all references to that
pattern will be perfect in all contexts over some finite interval. I
expect it would be trivial to prove a decent probabilistic model has
just such a property over any arbitrary finite interval for any given
pattern with proper reinforcement.
I do not disagree that measures of lossy models is a significant
practical issue for the purposes of a contest. But on the other
hand, lossless models demand certain levels of inefficiency that a
useful intelligent system would not exhibit and which impacts the
solution space by how poorly these types of algorithms scale
generally. If we knew an excellent lossless algorithm could fit
within the resource constraints common today such that a lossy
algorithm was irrelevant to the contest, I would expect a contest
would be unnecessary. Which is not to say that I think the rules
should be changed, just that this is quite relevant to the bigger
question.
Cheers,
J. Andrew Rogers
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