[HN Gopher] DeepDream: How Alexander Mordvintsev excavated the c...
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DeepDream: How Alexander Mordvintsev excavated the computer's
hidden layers
Author : DamnInteresting
Score : 46 points
Date : 2020-08-03 19:22 UTC (3 hours ago)
(HTM) web link (thereader.mitpress.mit.edu)
(TXT) w3m dump (thereader.mitpress.mit.edu)
| colordrops wrote:
| Ugh, I really dislike articles that are about to tell you the key
| idea(s) at the beginning then veer off into a personal interest
| story before doing the reveal. It's enough to get me to quit the
| article.
| zitterbewegung wrote:
| In college I took a reading class for quantum computation .
|
| The thing I learned about reading anything is reading the
| abstract and the ending and then reading the middle part if you
| find it interesting.
| dang wrote:
| Ok, but please don't post unsubstantive comments to Hacker
| News.
| j88439h84 wrote:
| It is a substantive comment about the presentation of the
| subject matter.
| Koshkin wrote:
| To be fair, judging by the title this is indeed a personal
| interest story. (Edit: It is another thing that way too many
| popular articles disguise themselves as personal stories as
| though assuming that more readers will be attracted to a
| tabloid kind of piece than to the subject itself; the problem
| may be that most subjects are old and generally not interesting
| any more, whereas new personal stories appear every day!)
| colah3 wrote:
| I've been incredibly lucky to work with Alex on several projects,
| including DeepDream. He's amazing. If you think you have a new
| idea about how to understand neural networks, there's a decent
| chance Alex did a prototype of it five years ago.
|
| Regarding DeepDream, it often feels to me -- I don't wish to
| speak on behalf of Alex or Mike -- that we didn't really
| understand what our results meant when we published DeepDream. It
| was kind of like discovering that warped glass can distort and
| magnify images: a really interesting discovery, but a lot more
| work was needed to turn it into a scientific instrument like
| glass can be used to form a microscope. As the community got
| single neuron or direction feature visualizations that worked
| well, lots of research possibilities began to open up. And in
| retrospect, one of the most important tricks was jitter, which
| Alex introduced. This style of feature visualization is probably
| the single tool I rely on most in my research to this day.
|
| (If you're curious what this has led to as we've continued to
| pursue it, check out Circuits
| (https://distill.pub/2020/circuits/zoom-in/), Building Blocks
| (https://distill.pub/2018/building-blocks/) and Activation
| Atlases (https://distill.pub/2019/activation-atlas/).)
|
| I'd also encourage people to check out Alex' new line of
| research, Neural Cellular Automata
| (https://distill.pub/2020/growing-ca/). I think it's a really
| interesting line of exploration. And as usual, Alex has an
| incredible deep trove of small fascinating results relating to
| NCA if you talk to him about it.
| 2bitencryption wrote:
| > The crucial point is that the machine does not see a cat or
| dog, as we do, but a set of numbers.
|
| This seems to miss the point - to follow that pattern, "Humans do
| not see a cat or a dog, they receive a set of neural impulses".
|
| If a human "knows" those impulses represent a cat, you could also
| surely say an artificial neural net "knows" those numbers
| represent a cat - and if you ask "how" a human/NN knows this, I
| guess the answer is the same -- different levels of visual
| abstraction (numbers/impulses trigger neurons that recognize
| edges and shapes, which become eyes become faces become bodies
| become animals...) trigger different levels of the network that
| are familiar with those abstractions and turn them into the end
| result: "That is a cat."
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(page generated 2020-08-03 23:00 UTC)