7 ms·

Artificial brains help understand real brains

82 points by Jeff_Brown 3y ago

adamnemecek 3y ago
[flagged]
- civilized 3y ago
  Isn't renormalization a technique by which divergent integrals can yield finite results? How would it be a mechanism by which anything operates?
  - adamnemecek 3y ago
    Read up on renormalization groups. There's this very fundamental problem in a lot of sciences, given some phenomena, find the patterns to compress the phenomena without knowing all the patterns a priori. It's like wavelet decomposition where your wavelets are updates as new data is coming in. That's renormalization.
    - bigdict 3y ago
      The fundamental problem is that you keep coming up with random bullshit to post on HN and you keep inviting people to your discord basement to discuss it.
      - adamnemecek 3y ago
        > The fundamental problem Problem for whom? > keep coming up with new random bullshit Elaborate, what's bullshit? > you keep inviting people to your discord basement to discuss it. Want to come? I can explain it to you.
kuprel 3y ago
The left hand in that photo should be a humanoid robot hand. Didn’t actually read the article
eep_social 3y ago
https://archive.is/ryTWs https://archive.is/ryTWs
TaupeRanger 3y ago
Not really. The only real example given in the article is when you hook someone up to an fMRI machine, collect data about how the brain looks when it sees a certain image, and then have a computational statistics program (NOT an artificial brain, in any sense) do some number crunching and output the most likely thing it's looking at based on things you specifically trained it to recognize beforehand. We learn precisely nothing from this, no medical or computer science advances are made from it, and it doesn't remotely support the title of the article.
- HarHarVeryFunny 3y ago
  I think the Economist article is exactly right - that despite the massive differences between ANNs and the brain, ANNs are indeed highly suggestive of how some aspects of the brain appear to work. People can criticize the shortcomings of GPT-4, but it's hard to argue that it's at least capable of some level of reasoning (or functionally equivalent if you object to that word!). It's not yet clear exactly how a Transformer works other than at mechanical level of the model architecture (vs the LLM "running on" the architecture), but we are at least starting to glean some knowledge of how the trained model is operating... It seems that pairs of attention heads in consecutive layers are acting in coordination as "induction heads" that in one case are performing a kind of analogical(?) A'B' => AB match-and-copy type of operation. The induction head causes a context token A to be matched (via "attention" key query) with an earlier token A' whose following token B' then causes related token B to be copied to the residual stream at position following A. This seems a very basic type of operation, and no doubt there's a lot more interpretability research to be done, but given the resulting reasoning/cognitive power (even in absense of any working memory or looping!), it seems we don't need to go looking for overly complex exotic mechanisms to begin to understand how the cortex may be operating. It's easy to imagine how this same type of embedded key matching might work in the cortex, perhaps with cortical columns acting as complex pattern matchers. Perhaps the brain's well known ~7 item working memory corresponds to a "context" of sorts that is updated in same way as induction heads update the residual stream. Anything I've written here about correspondence between transformer and cortex is of course massive speculation, but the point is that the ANN's operation does indeed start to suggest how the brain, operating on similar sparse/embedded representations, may be working.
viableSprocket1 3y ago
Oversimplifying for brevity (and there is definitely more nuance to this). This is basically the modeling approach: 1. Have a biological brain do a task, record neuronal data + task performance 2. Copy some of those biological features and implement in an ANN 3. Tune the many free parameters in the ANN on task performance 4. Show that the bio-inspired ANN performs better than SOTA and/or shows "signatures" that are more brain-like. The major criticisms of Yamins' (and similar) groups are either that correlation != causation, or correlation != understanding, or that it is tautological (bio-inspired ANNs will be more biological). I'm not sure how seriously this work is taken vs. true first principles theory.
phkahler 3y ago
For me the most interesting parallel is from (I think) GANs, and other generative AIs. This is similar to the idea in psychology that we are really doing a lot of projection with some correction based on sensory input - as opposed to actually perceiving everything around us. Also, real synapses are one of the most abundant features of real brains and are the direct inspiration for NN weights. I'm not sure the artificial brains help understand real ones, but they do seem to validate some ideas we have about real ones.
cosmojg 3y ago
As a computational neuroscientist, I find myself both terribly disappointed and unfortunately reminded of Gell-Mann amnesia.
- ggm 3y ago
  This ++ Somebody should have shown the journalist the paper on brain function in a dead fish under fMRI
imtringued 3y ago
Yes indeed. Attempts to simulate human neurons have shown that a single neuron can be simulated with a realtively large ANN consisting of several layers. This tells us that human neurons are more computationally complex and capable compared to other animals and orders of magnitude more complex than neurons in ANNs.
worik 3y ago
This article reminded me of looking at clouds, and seeing shapes. Or constellations...
pfdietz 3y ago
This sounds like a conclusion that can be reached only when we understand real brains.
iso8859-1 3y ago
Can someone explain to me why "help understand" is grammatically correct? Why wouldn't help be following by a gerund like on https://www.ef.com/wwen/english-resources/english-grammar/verbs-followed-gerunds/ https://www.ef.com/wwen/english-resources/english-grammar/ve... ?
- siddbudd 3y ago
  Called the "bare infinitive". Use your favorite search engine, many sites will explain it better than I can. Short: "Help is a verb that can be used with or without to and with or without an object before the infinitive. When we use it without an infinitive it sometimes sounds more informal." (from Bare infinitive - Learning English | BBC World Service)
nathias 3y ago
scientific metaphors have been useful for science on all fields, it doesn't mean they are accurate or anything, they just help you think about a thing in a better way

82 points by Jeff_Brown 3y ago

adamnemecek 3y ago

[flagged]

civilized 3y ago
Isn't renormalization a technique by which divergent integrals can yield finite results? How would it be a mechanism by which anything operates?
- adamnemecek 3y ago
  Read up on renormalization groups. There's this very fundamental problem in a lot of sciences, given some phenomena, find the patterns to compress the phenomena without knowing all the patterns a priori. It's like wavelet decomposition where your wavelets are updates as new data is coming in. That's renormalization.
  - bigdict 3y ago
    The fundamental problem is that you keep coming up with random bullshit to post on HN and you keep inviting people to your discord basement to discuss it.
    - adamnemecek 3y ago
      > The fundamental problem Problem for whom? > keep coming up with new random bullshit Elaborate, what's bullshit? > you keep inviting people to your discord basement to discuss it. Want to come? I can explain it to you.

kuprel 3y ago

The left hand in that photo should be a humanoid robot hand. Didn’t actually read the article

eep_social 3y ago

https://archive.is/ryTWs https://archive.is/ryTWs

TaupeRanger 3y ago

Not really. The only real example given in the article is when you hook someone up to an fMRI machine, collect data about how the brain looks when it sees a certain image, and then have a computational statistics program (NOT an artificial brain, in any sense) do some number crunching and output the most likely thing it's looking at based on things you specifically trained it to recognize beforehand. We learn precisely nothing from this, no medical or computer science advances are made from it, and it doesn't remotely support the title of the article.

HarHarVeryFunny 3y ago
I think the Economist article is exactly right - that despite the massive differences between ANNs and the brain, ANNs are indeed highly suggestive of how some aspects of the brain appear to work. People can criticize the shortcomings of GPT-4, but it's hard to argue that it's at least capable of some level of reasoning (or functionally equivalent if you object to that word!). It's not yet clear exactly how a Transformer works other than at mechanical level of the model architecture (vs the LLM "running on" the architecture), but we are at least starting to glean some knowledge of how the trained model is operating... It seems that pairs of attention heads in consecutive layers are acting in coordination as "induction heads" that in one case are performing a kind of analogical(?) A'B' => AB match-and-copy type of operation. The induction head causes a context token A to be matched (via "attention" key query) with an earlier token A' whose following token B' then causes related token B to be copied to the residual stream at position following A. This seems a very basic type of operation, and no doubt there's a lot more interpretability research to be done, but given the resulting reasoning/cognitive power (even in absense of any working memory or looping!), it seems we don't need to go looking for overly complex exotic mechanisms to begin to understand how the cortex may be operating. It's easy to imagine how this same type of embedded key matching might work in the cortex, perhaps with cortical columns acting as complex pattern matchers. Perhaps the brain's well known ~7 item working memory corresponds to a "context" of sorts that is updated in same way as induction heads update the residual stream. Anything I've written here about correspondence between transformer and cortex is of course massive speculation, but the point is that the ANN's operation does indeed start to suggest how the brain, operating on similar sparse/embedded representations, may be working.

viableSprocket1 3y ago

Oversimplifying for brevity (and there is definitely more nuance to this). This is basically the modeling approach: 1. Have a biological brain do a task, record neuronal data + task performance 2. Copy some of those biological features and implement in an ANN 3. Tune the many free parameters in the ANN on task performance 4. Show that the bio-inspired ANN performs better than SOTA and/or shows "signatures" that are more brain-like. The major criticisms of Yamins' (and similar) groups are either that correlation != causation, or correlation != understanding, or that it is tautological (bio-inspired ANNs will be more biological). I'm not sure how seriously this work is taken vs. true first principles theory.

phkahler 3y ago

For me the most interesting parallel is from (I think) GANs, and other generative AIs. This is similar to the idea in psychology that we are really doing a lot of projection with some correction based on sensory input - as opposed to actually perceiving everything around us. Also, real synapses are one of the most abundant features of real brains and are the direct inspiration for NN weights. I'm not sure the artificial brains help understand real ones, but they do seem to validate some ideas we have about real ones.

cosmojg 3y ago

As a computational neuroscientist, I find myself both terribly disappointed and unfortunately reminded of Gell-Mann amnesia.

ggm 3y ago
This ++ Somebody should have shown the journalist the paper on brain function in a dead fish under fMRI

imtringued 3y ago

Yes indeed. Attempts to simulate human neurons have shown that a single neuron can be simulated with a realtively large ANN consisting of several layers. This tells us that human neurons are more computationally complex and capable compared to other animals and orders of magnitude more complex than neurons in ANNs.

worik 3y ago

This article reminded me of looking at clouds, and seeing shapes. Or constellations...

pfdietz 3y ago

This sounds like a conclusion that can be reached only when we understand real brains.

iso8859-1 3y ago

Can someone explain to me why "help understand" is grammatically correct? Why wouldn't help be following by a gerund like on https://www.ef.com/wwen/english-resources/english-grammar/verbs-followed-gerunds/ https://www.ef.com/wwen/english-resources/english-grammar/ve... ?

siddbudd 3y ago
Called the "bare infinitive". Use your favorite search engine, many sites will explain it better than I can. Short: "Help is a verb that can be used with or without to and with or without an object before the infinitive. When we use it without an infinitive it sometimes sounds more informal." (from Bare infinitive - Learning English | BBC World Service)

nathias 3y ago

scientific metaphors have been useful for science on all fields, it doesn't mean they are accurate or anything, they just help you think about a thing in a better way