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Nematode Neural System Uploaded to a Computer and Trained to Balance a Pole
- ggggtez 9y agoWhat a confusing title. Tldr; NN based on real life nematode learned a simple thing.
- mgiannopoulos 9y agoThis is why I always read the HN comments first. Thank you.
- goblins 9y agoMe too, I tend to decide whether to read an article based on the comments. I find I hardly ever read an article.
- userbinator 9y agoIt's because "Worm Uploaded to a Computer" primes the reader to expect something about malware, but then "Trained to Balance a Pole" completely violates that expectation.
- sctb 9y agoThanks, we've updated the headline to clarify.
- Y_Y 9y agoNeural network balances pole while programmers mumble something vague about C. Elegans. Non-story.
- anewhnaccount2 9y agoThis uses a more accurate neuron model than your everyday neural networks.
- gwern 9y agoNevertheless, Cartpole is a trivial RL task which only requires a few neurons to solve, much less hundreds. Since they're training it in silico, I'm not sure what this demonstrates other than you can cudgel a vaguely spiking-like neural network into learning a simple task. It's not a C. elegans behavior.
- guskel 9y agoCartpole solved with 1 neuron, no weights. https://gym.openai.com/evaluations/eval_A7rFUDisQiOsADyvqYhVSA/#reproducibility https://gym.openai.com/evaluations/eval_A7rFUDisQiOsADyvqYhV...
- simonster 9y agoIt's a more accurate neuron model of some pretty weird neurons. In nearly all organisms, the vast majority of neurons fire all-or-nothing action potentials ("spikes"). C. elegans neurons do not.
- gargarplex 9y agoWait until we can 3D print a C. Elegans. A new kind of matrix printer.
- egeozcan 9y agoMaybe The Great Filter (in the context of the Fermi paradox) is this: People printing their own organisms? (only half-joking)
- sudhirj 9y agoMore likely people uploading themselves into a virtual world, securing the planetary data center and dialating time to feel like everyone lives forever in the manner of their choosing.
- booleandilemma 9y agoThere’s something sad about a civilization closing its eyes and turning inward on itself instead of exploring the vast universe that it has been presented with.
- jonathanyc 9y ago“Conquering the galaxy is what bacteria with spaceships would do—knowing no better, having no choice.” From Diaspora, by Greg Egan :)
- pasquinelli 9y agothe universe may be vast, but it's pretty empty.
- scentoni 9y agoWhat makes you think you can simulate an atom faster than an atom does?
- sethev 9y ago
- molyss 9y agoIt’ll become interesting when we can teach the worm before “uploading”it, and the resulting NN already knows how to balance that pole without any further training. As is, The article sounds underwhelming
- gambler 9y agoThe worm isn't "uploaded". Scientists painstakingly mapped out the physical structure of its neurons using some genetic engineering and lasers. This structure does not change from worm to worm.
- molyss 9y agoI'm clear. That didn't prevent the authors from using that term anyway. And in their title, just to make sure not to confuse anyone. EDIT : the author(s) of the press release used the word "upload". That term is nowhere to be found in the original paper as far as I can tell.
- grondilu 9y agoI vaguely recall that experts consider artificial neural networks to be a very gross approximation of biological ones. They often state that one reason we don't have AI today is that we don't really know how the brain and the neurons it is made of work. Then I wonder : how does openworm deal with that lack of knowledge? Is there any chance progress in modeling C. Elegans could be used to improve machine learning?
- ared38 9y agoANNs aren't supposed to model biological neurons, they're more inspired by them. We actually know a great deal about how real neurons work, although the emergent effects keep us from learning much about the brain as a whole. But modeling biological neurons is expensive; they're far more complex than a logistic function. Just modeling a worm is taking them an entire cluster, and they appear to be using a pretty simple model (the Hodgkin–Huxley equations way back from 1952). I don't think we'll ever simulate real neurons to detect cat photos. Instead, modeling entire brains might allow us to see how learning happens, which could be simplified into new algorithms for machine learning.
- red75prime 9y agoIt could be other way around. Evolution invented imprecise and roundabout way of doing gradient descent.
- tzahola 9y agoWe believe that the underlying implementation (biological neuron vs logistic function) doesn’t matter as long as it has the fundamental properties from which intelligence can emerge. Like how you can build computers from gears, relays, vacuum tubes or silicon - as long as you have a transistor-like nonlinearity to work with, you’re good.
- akvadrako 9y agoFrom what I understand, the complication is that each neurone acts behaves like a complex neural network by itself, so to get the same overall behavior, you would need many more fundamental units.
- pwks 9y agoA very well written article, simple to understand and short!
- gambler 9y agoI first learned about C. elegans neuron-mapping project from this Society of Mind video: https://www.youtube.com/watch?v=6Px0livk6m8 https://www.youtube.com/watch?v=6Px0livk6m8 My immediate interest was in seeing the differences and similarities between real and simulated worm. I haven't spent much time searching for resulting papers, but it's been 7 years since then and I'm not aware of any ground-breaking publications on the subject. Unless I'm missing something massive, describing this paper as training the worm to "balance a pole at the tip of its tail" is highly misleading. In this paper researchers use an external algorithm to tweak parameters of a part of worm's neural model until that part can perform a certain task. The neural circuit effectively serves as a controller for a mechanism that has nothing to do with the original worm. The task, the setup, the subset of the model and the training algorithms are all chosen by the researchers.
- nonbel 9y agoI think their idea is to use biologically inspired network architectures. Looking at Fig1 of the paper[1], it seems they have drawn the schematic in an overly complicated way... For example, FWD and REV motor neurons are totally determined by AVB and AVA sensory neurons so can be left out. I would bet if it is worked out this reduces to some simple ANN architecture. [1] https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx3d25pcDIwMTd8Z3g6NDQ3YjZhZTZiYWJiNDI5NA https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsd...
- gambler 9y ago>I think their idea is to use biologically inspired network architectures. It seems that way. This approach could have interesting applications in engineering. But I wish it wasn't pitched to imply that "machine learning and the activity of our brain the same on a fundamental level". (This is a direct quote from the article, except it was posed as a rhetorical question there.)
- mr_toad 9y agoFrom the summary, they’re not altering the topology of the network at all, just the connection strength between neurons. While this isn’t the way a natural neural network would learn (I’m not sure a nematode can learn), it’s still interesting that you can take a copy of a natural neural network and force it to learn in this way.
- gasull 9y ago"Uploaded" no, copied into a perfect or near-perfect simulation. A perfect copy of an organism is still not the organism, but a copy. Just like your twin brother is not you. You won't live forever in a computer.