I'm not sure exactly how they got this image, but it sure looks like it came from the Google Deep Dream project where a deep learning network was asked to 'dream' about images and produce 'overpreceived' images, which look a lot like hallucinating on psychoactive mycotoxins. |
Is there such a thing as Bad Information? If so, what is
the difference between Good and Bad? How do we know that difference?
Artificial intelligence, but information theory in
general, is a common theme in Hidden Scents. How can you not write about it
these days? We are computers. At least, we are becoming computers. Or they us.
At least, that's what say the analogies we use to make sense of our world. Do
we know anything aside from the analogies we use? (We should probably be asking
Douglas Hofstader
about that one)
Back when pneumatics was the technology du jour, we
thought the nervous system worked according to pressure in the nerves. That was
correct for the circulatory system, but the utility of that analogy ended
there. Eventually, the computer analogy will run out, but until then, we are
computers. And these days, specifically we are computers learning to recognize
patterns in our environment using forward-feebacked layers of feature
detection.
This brings us to the premier of a new infotech textbook.
“The Deep Learning textbook is a resource intended to
help students and practitioners enter the field of machine learning in general
and deep learning in particular.”
Deep Learning, An
MIT Press book
Ian Goodfellow and
Yoshua Bengio and Aaron Courville, 2016
It's a textbook, so it's too technical for the interested
layperson. But there is some goo dintroductory materials that could help
straighten things out for people who want to know what it is, but don't have
the contect-specific knowledge to digest the whole thing.
Here's from the chapter on Information Theory:
“Likely events should have low information content, and
in the extreme case, events that are guaranteed to happen should have no
information content whatsoever.
“Less likely events should have higher information
content.
“Independent events should have additive information. For
example, finding out that a tossed coin has come up as heads twice should
convey twice as much information as finding out that a tossed coin has come up
as heads once. “
The text then goes on to translate these maxims into
mathematical formulae.
Sometimes someone says something and I'm like, wow, that
was really stupid. But then later on, when I try to think about -why- it was
stupid, I find it difficult to articulate. Above we have a good rationale for
explaining why a particular statement is 'stupid' or not. It depends on how
much information it has. And this is how we measure that information. In
laymen's terms, we would call this the Captain Obvious principle. If you just
said something that everyone already knows or should expect, but you said it
like it's got good information value (as if nobody knows or expects it) then
that would come across as stupid.
There we go again, turning a branch of applied
mathematics into a magnifying glass for human behavior; probably not what the
authors of this text intended to be done with their work.
Anyway, I like the word hard-coding. They use it to
describe the 'older' way of writing-in knowledge about the world into a program
(instead of 'letting the program figure it out for itself,' as these newer deep
learning programs are done).
They point out in the introduction that "A person's
everyday life requires an immense amount of knowledge about the world. Much of
this knowledge is subjective and intuitive, and therefore difficult to
articulate in a formal way. Computers need to capture this same knowledge in
order to behave in an intelligent way. One of the key challenges in artificial
intelligence is how to get this informal knowledge into a computer." Instead,
when computers get their own data, by extracting patterns from raw data, this
is known as machine learning. Deep learning is a type of machine learning.
Still, figuring out which details are valuable and which
are inconsequential is the hardest part. -Disentangling- is a word emphasized
by the authors. That's a favorite word in Hidden Scents as well. So is
inextricable, the information-opposite of disentangle. So is disambiguate, the
big brother of disentangle.
If you're into this stuff, and a bit more on the application
side than the theoretical side, you might want to check this book out. And if
you're just into machine-generated hallucinations, or if you've ever tripped on
psilocybic mushrooms and want to see something reminiscent - very reminiscent -
unnervingly reminiscent - check out the front cover.
notes:
Analogy as the Core of Cognition, Douglas Hofstadter,
Stanford lecture, 2006
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