

Machine learning is teaching us the secret to teaching - dnetesn
http://nautil.us/issue/6/secret-codes/teaching-me-softly

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vanderZwan
Although the article makes no mention of them, George Lakoff and Mark
Johnson's research on conceptual metaphor[0] seems to be intricately tied to
this, somehow. I recently read two books by them: _Metaphors We Live By_ (see
Peter Norvig's description here[1]), and _Philosophy In The Flesh_ [2] and
found the ideas in them on how humans use metaphors to make sese of things
very interesting and intuitive. It actually left me wondering when and how AI
would use these insights.

[0]
[http://en.wikipedia.org/wiki/Conceptual_metaphor](http://en.wikipedia.org/wiki/Conceptual_metaphor)

[1] [http://norvig.com/mwlb.html](http://norvig.com/mwlb.html) \- because it
stays focused on language it manages to make a clear and convincing case

[2] [http://www.nytimes.com/books/first/l/lakoff-
philosophy.html](http://www.nytimes.com/books/first/l/lakoff-philosophy.html)
\- by comparison a flawed but nonetheless good read. I agree with the
critiques laid out this review:
[http://lesswrong.com/lw/871/review_of_lakoff_johnson_philoso...](http://lesswrong.com/lw/871/review_of_lakoff_johnson_philosophy_in_the_flesh/)

~~~
arvinjoar
I started reading "Louder Than Words: The New Science of Meaning" which is
based on embodiment. Instead of making up these grand hypotheses like Lakoff
tends to do (not necessarily a bad thing) Bergen ties embodiment and more
specifically simulation to new empirical research. I think that we could
probably benefit from cross-pollination between cognitive
linguistics/psychology and more technical AI.

~~~
vanderZwan
Yeah, that's a valid criticism of Lakoff especially. This blog sums up the
problems with his writins quite nicely, without losing sight of the good bits
and the contributions of his work:

[http://blog.apperceptual.com/criticisms-of-lakoff-s-
theory-o...](http://blog.apperceptual.com/criticisms-of-lakoff-s-theory-of-
metaphor)

Thanks for the tip, I'll check out "Louder Than Words"

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skybrian
I found some slides [1] explaining how this works.

A less poetic example of privileged information: if you're training on time-
series information, you can include events from the future in the training
examples, even though they won't be available while making predictions in
production.

Apparently this helps the machine learning algorithm find the outlying data
points when the data isn't linearly separable.

[1]
[http://web.mit.edu/zoya/www/SVM+.pdf](http://web.mit.edu/zoya/www/SVM+.pdf)

~~~
tehwalrus
Unfortunately, I don't find that reference very helpful - it's just pages of
annotated equations.

What's an example of pseudocode that would actually implement this? Surely you
don't load a natural language module in order to parse the pathologist's notes
(in the example given in the reference about biopsies)?

(I should also note that the original article is devoid of _any_ technical
examples, making it completely opaque to me what it actually entails.)

~~~
skybrian
Yes, I mostly don't understand the math either. But apparently with the
poetry, they converted it into a vector somehow based on the appearance of
keywords. Perhaps someone will find a friendlier example.

~~~
theoh
I think an example is this: for a concrete set of data, see the distributions
on page 28 of this document (page 40 of the PDF)
[http://mi.eng.cam.ac.uk/reports/svr-ftp/auto-
pdf/harpur_thes...](http://mi.eng.cam.ac.uk/reports/svr-ftp/auto-
pdf/harpur_thesis.pdf)

These different distributions are difficult to distinguish with statistics.
However, if you can see the shape snd therefore know the "rule" or the
structure of the distribution, it's easy to design/train a network to
recognise them.

(note that the content of that PDF is not about this problem per se)

------
mikegioia

        “In many cases, humans use their own knowledge about
        actions to recognize those actions in others,” he told
        me. “If a robot knows how to grasp, it has better chance
        of recognizing grasping actions of a person,” he said.
        Metta is going one step further, by teaching iCub to
        follow social cues, like directing its attention to
        an object when someone looks at it. These cues become
        another channel through which to collect privileged
        information about a complex world.
    

This article kept getting better and better. I thought this was particularly
interesting because it leads to a world where the AI software is available to
all, but what's of value is the rules or metaphors you've developed for your
robots. That would give garage hobbyists a chance of coming up with their own
special formulas and possibly be the ones to figure out something novel or
world-altering in AI.

~~~
crater
This could potentially provide insight into Autism cases? Training an AI to be
attentive to social cues, and then training another one to be not as attentive
- and see what kind of results come out of each. This could also point us in a
direction of what type of cues are the most important to pay attention to.

------
skinnybatch
As I see it, the natural extension of identifying this "secret to teaching,"
is to try and extrapolate it to "unlearning," or memory loss, dementia and
Alzheimer's.

If we can mimic and model the neuronal pathways and firings within humans,
using AI, then we should also be able to study the relationship between the
failure and degradation of pathways. AI pathways are subject to break and fail
as well, though the causes are of course different. However, it would be
imagined that there must be some shared structural stresses that result in the
"unlearning" and failure to fire or function.

The potential for AI is immeasurable. If we can teach a robot, surely we can
stress its system enough to "unteach" it. Despite being unable to force-feed
it junk-food and/or vitamins/minerals, we can replicate environmental
stressors, and once having generated the "unlearning" process, examine how to
halt the degradation and perhaps reverse the trajectory.

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afro88
> His advice gave no specific information on what angle the bow should
> describe, or how to move the fingers across the frets to create vibrato

And it's a good thing he didn't - violins don't have frets /nitpick

~~~
qubyte
I wouldn't fret too much about it.

I'll get my coat...

~~~
deathcakes
Yes, perhaps you should bow out.

~~~
hyperbovine
Aaaand the Reddit ~ HN equivalence is complete.

~~~
sliverstorm
Actually it wasn't technically complete until your comment- the self-aware
reference to the progression is the final step :)

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melipone
I stopped reading where it says that SVM is used for big data. SVM can only
process about 10000 examples.

~~~
j2kun
Then you ignored all the inaccuracies before that sentence, and all of the
insights after it.

