This is another open problem: given a data set
which classifies outcomes in terms of some world model,
how can dimensions along which the data set gives little
information be identified?
Ambiguity identification may be difficult to do correctly.
It is easy to imagine a system which receives
value-labeled training data, and then spends the first
week querying about wind patterns, and spends the second
week querying about elevation differentials, only
to query whether brains are necessary long after the
programmers lost interest. There is an intuitive sense
in which humans “obviously” care about whether the
human-shaped-things have brains more than we care
about whether the people are on mountains, but it may
not be obvious to the system. On the other hand, it
could plausibly be the case that there is some compact
criterion for finding the most likely or most surprising
ambiguities. Further research into ambiguity identifi-
cation could prove fruitful.
This seems like an appropriate place to cite my concept learning paper? The quoted paragraphs seems like it’s basically asking the question of “how do humans learn their concepts”, and ambiguity identification is indeed one of the classic questions within the field. E.g. Tenenbaum 2011 discusses ambiguity identification:
The same principles can explain how people
learn from sparse data. In concept learning, the
data might correspond to several example objects
(Fig. 1) and the hypotheses to possible extensions
of the concept. Why, given three examples
of different kinds of horses, would a child generalize
the word “horse” to all and only horses
(h1)? Why not h2, “all horses except Clydesdales”;
h3, “all animals”; or any other rule consistent with
the data? Likelihoods favor the more specific
patterns, h1 and h2; it would be a highly suspicious
coincidence to draw three random examples
that all fall within the smaller sets h1 or h2
if they were actually drawn from the much larger
h3 (18). The prior favors h1 and h3, because as
more coherent and distinctive categories, they
are more likely to be the referents of common
words in language (1). Only h1 scores highly
on both terms. Likewise, in causal learning, the
data could be co-occurences between events; the
hypotheses, possible causal relations linking
the events. Likelihoods favor causal links that
make the co-occurence more probable, whereas
priors favor links that fit with our background
knowledge of what kinds of events are likely to
cause which others; for example, a disease (e.g.,
cold) is more likely to cause a symptom (e.g.,
coughing) than the other way around.
Or see this talk or even e.g. just the first 10 minutes of it, where the concept learning problem is basically defined as being the same thing as the ambiguity identification problem.
Oh good, you guys are reading Tenenbaum. Now I can come over some day and give you my talk on probabilistic programming for funsies rather than in a desperate attempt to catch FAI researchers up on what computational cognitive science has been doing for years.
In all honesty, my expected lifespan got a little bit longer after updating on you guys indeed knowing about the Tenenbaum lab and its work.
/u/JoshuaFox was telling me to wait until I actually recorded the voiced lecture before posting to LW, but oh well, here it is. I’ll make a full and proper Discussion post when I’ve gotten better from my flu, taken tomorrow’s exam, submitted tomorrow’s abstracts to MSR, and thus fully done my full-time job before taking time to just record a lecture in an empty room somewhere.
This seems like an appropriate place to cite my concept learning paper? The quoted paragraphs seems like it’s basically asking the question of “how do humans learn their concepts”, and ambiguity identification is indeed one of the classic questions within the field. E.g. Tenenbaum 2011 discusses ambiguity identification:
Or see this talk or even e.g. just the first 10 minutes of it, where the concept learning problem is basically defined as being the same thing as the ambiguity identification problem.
Good point, thanks! I added references to both you and Tenenbaum in that section.
Oh good, you guys are reading Tenenbaum. Now I can come over some day and give you my talk on probabilistic programming for funsies rather than in a desperate attempt to catch FAI researchers up on what computational cognitive science has been doing for years.
In all honesty, my expected lifespan got a little bit longer after updating on you guys indeed knowing about the Tenenbaum lab and its work.
Sounds interesting, do you have some reference (video/slides/paper)?
/u/JoshuaFox was telling me to wait until I actually recorded the voiced lecture before posting to LW, but oh well, here it is. I’ll make a full and proper Discussion post when I’ve gotten better from my flu, taken tomorrow’s exam, submitted tomorrow’s abstracts to MSR, and thus fully done my full-time job before taking time to just record a lecture in an empty room somewhere.
Thanks!
Cool, thanks!
Sounds interesting, do you have some reference (video/slides/paper)?
I think you replied to the wrong comment. :)
Indeed. Thanks for noticing.