
New technique would reveal the basis for machine-learning systems’ decisions - renafowler
http://sciencebulletin.org/archives/7082.html
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YeGoblynQueenne
>> Neural networks are so called because they mimic — approximately — the
structure of the brain.

Grumble.

ANNs are "approximately" like the brain [1] as much as Pong is "approximately"
like the game of Tennis. In fact, much less so.

ANNs are _algorithms for optimising systems of functions_. The "neurons" are
functions, their "synapses" are inputs and outputs to the functions. That's an
"approximation" of a brain only in the most vague sense, in the broadest
possible strokes, so broad in fact that you could be approximating any
physical process or object [2].

Like, oh, I dunno- trains.

Trains, right? The functions are like coaches and the parameters they pass
between each other are like rails. Artificial Neural Networks --> Artificial
Train Networks; they mimic - approximately - the structure of the train.

Stop the madness. They're _nothing_ like brains, in any way, shape or form.

And grumble some more.

_____________

[1] Wait- _which_ brain? Mammalian brain? Primate brain? Human brain? Grown-up
brain? Mathematician's brain? Axe-murderer's brain?

[2] Because... that's what they do, right? They approximate physical
processes.

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egocodedinsol
You're not technically wrong, but I think this is a bit pedantic. Most people
realize this. Convolutional networks don't actually seem to be a totally bad
approximation of feedforward visual processes in the brain at a computational
level. And I say this as a neuroscientist.

~~~
argonaut
Most people don't realize this. Most people will just read popular science
articles in the media. They will never read discussions on HN/Reddit/Twitter.

~~~
bitCromwell
which is precisely why analogies are helpful in communicating with them.

~~~
argonaut
Which is precisely why false analogies are not helpful.

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wcrichton
Title is a little general. This specifically is a technique for breaking down
text analysis, where the goal is to give semantic meaning to a block of text.
In their example, they want to condense beer reviews into star ratings of a
few categories. A totally black box technique would take the review and spit
out the scores, whereas their technique has two jointly trained networks: one
identifies relevant text fragments for each category, and the other gets the
corresponding category score for the fragment.

This is not groundbreaking, but still a good example of a larger trend in
trying to understand neural network decision making. Here's a cool paper that
analyzes how CNNs can learn image features for attributes like "fuzziness" and
other higher level visual constructs while training for object recognition:
[https://pdfs.semanticscholar.org/3b31/9645bfdc67da7d02db766e...](https://pdfs.semanticscholar.org/3b31/9645bfdc67da7d02db766e17a3e0a37be47b.pdf)

~~~
hammock
From a business point of view (getting executives to want to use ML)
understanding "the black box" is important. But the two-step process you
outline would tend to be less accurate than a one-step process, no?

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avivo
The general concept: we can figure out what lead to a particular
classification for an item by finding smaller subsets of the item that still
gave the same classification.

For example, this can show which snippet of text implies a particular review
should be classified as "very negative", or which part of an image lead to a
classification of "cancerous" for a biopsy image.

This doesn't give you much predictive power about the network however, or tell
you how it actually works in general. It simply tells you how it made a
particular classification.

Paper link:
[https://people.csail.mit.edu/taolei/papers/emnlp16_rationale...](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf)

~~~
avivo
From the paper:

"Our goal is to select a subset of the input sequence as a _rationale_.

In order for the subset to qualify as a rationale it should satisfy two
criteria: 1) the selected words should be interpretable and 2) they ought to
suffice to reach nearly the same prediction (target vector) as the original
input. In other words, a rationale must be short and sufficient."

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intro-b
any ideas on how this is similar to or differs from the prospects and
structure of Darpa's explainable AI (XAI) contract?

[https://www.fbo.gov/index?s=opportunity&mode=form&id=1606a25...](https://www.fbo.gov/index?s=opportunity&mode=form&id=1606a253407e8773bdd1a9e884cc5293)

curious about the inherent trade-off between predictive power/complexity in ML
model and the accuracy of system explanation's inferred by these models

~~~
_0ffh
IIRC the last XAI approch I read about basically gives you a local linear
approximation or Jacobian of the decision surface. You take a look at the
biggest terms and call them an explanation. Not too shabby, probably. I don't
know if all of the XAI project follows that approach.

Edit: Framing

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Houshalter
So this is pretty limited. It only works on text data, and just picks the part
of the text that most determines the output. The basic idea of doing this
isn't new. You can easily ask a naive bayes spam filter what words it thinks
are the highest evidence of spam or not spam in a document. It is interesting
to see this done with neural nets though. I recall reading something similar
that uses gradients to find what words changed the network's output the most,
I'm not sure if this new method is actually better and it's not as general.

But this is a long way away from the NN being able to give understandable
reasons for its decisions. These methods will always be limited to pointing to
a part of the input and saying "that part seemed relevant". But it can never
articulate why it's relevant, or what it's "thinking" internally.

I think this is ok though. I mean looking at what features the model is using
to make predictions is pretty useful and should give you a rough idea how it
works.

I've wondered in the past if neural networks could _train_ humans to
understand them. The human would be shown an input, and try to predict the
value of a neuron in the network. So the human would learn what the network
has learned, and gain intuition about the inner workings of the model.

You can also do a similar process with other machine learning methods. You can
train a simpler, more understandable model, like decision trees, to predict
the neurons of a neural net. And then the human can study that. You can even
train a smaller neural network to fit to a bigger, more complex one.

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hyperion2010
I'm still curious about whether it is possible at all to deal with "who sunk
the boat" or "the straw that broke the camel's back" cases or whether there
will just end up being a list of a thousand reasons that summed up to a
decision, perhaps ranked by their weight.

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quantum_state
Noting that neural nets are associative memories, one would think the best one
can get out for the rationale of a specific outcome would be the high level
land marks of the dynamics, starting from a given input and all the way to the
outcome ... is there anything else one would expect to get out from the
system?

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godmodus
the name neural network is unfortunate. but then again you can't call them
"continuous function best fit support vector feedback machines on
cocaine"-networks.

the name will cause confusions for many years to come. but it's what we have.
nice article, it's good they're trying.

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jbclements
Yes! I wrote about this in a widely-discussed
^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H totally-unread blog post back in 2014:
[https://www.brinckerhoff.org/blog/2014/12/12/the-why-
button/](https://www.brinckerhoff.org/blog/2014/12/12/the-why-button/)

~~~
Edmond
Hmmmm

You're making quite a bold claim, I don't see you make any reference to ML, if
I were to draw inference from your post I would conclude you were talking
about better ways to instrument software so you can track what it is doing,
not a particularly novel idea :)

The paper referenced in this post talks about a technique for teasing out the
"logic" for a learning model's decision.

