6 ms·

I really wish we could talk about new kinds of hardware without the comparison to the human brain. SNN models capture some characteristics of biological neurons

• falcor84 3y ago
  Apologies for the quack philosophy, but isn't it just that we as human observers of the world can't help but identify our reflection in everything we see or do?
  • knodi123 3y ago
    or, conversely, a quack marketing approach: the more you can relate a product to an existing successful product, the more people will think it's a good idea.
    • abeppu 3y ago
      According to this table [1] the 1.15B artificial neurons they're claiming is just at little below the estimate for raccoon dogs and Tanimbar corella / cockatoo. Even if it's a technically impressive system, "inspired by the racoon dog brain" doesn't make for as good a press release. [1] https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n...
      • falcor84 3y ago
        Well, even then, it's massive progress that we've reached the realm of mammals. Only 2 more orders of magnitude to go.
        • nicklecompte 3y ago
          It's not even close to a mammal. It might have 1bn artificial neurons, while a cat has 700m biological neurons. But a) individual mammal neurons are far more computationally complex than any existing artificial neuron, even in SNNs. b) a cat has 10 trillion synapses compared to the 100 billion artificial synapses in Intel's computer. So even ignoring how simple artificial neurons are, mammal brains have far more topological complexity.
          • hollerith 3y ago
            But semiconductors operate so much faster than neurons that it is hard to be confident that AI hadware has not already caught up to cat brains.
            • nicklecompte 3y ago
              Well, don't underestimate the sheer back-of-the-envelope power of a cat's brain: 700m neurons x 200Hz per neuron is a LOT of compute, like ~10,000 modern CPU cores in terms of ops/second. Pretty impressive when you consider the thermal efficiency.... Regardless, looking at AI power that way is a mistake. There are already a few supercomputers that can perform more operations/second than the human brain and I wouldn't be too surprised if this neuromorphic computer can perform more ops/s than a cat. But the structure of the brain is important: brains aren't just simply glued-together layers of neurons. The incredible topological complexity and integration of mammal brains surely means that certain "parallelizable algorithms" can be run much more efficiently, and that disparate information can be pulled together more readily for identifying patterns and making mental models. All mammals (and birds) are faster learners than any artificial neural network, and they understand ideas like object permanence and causality, which no AI is even close to getting right. It is reasonable to suppose that the structural complexity of mammal/bird brains is an important part why their cognitive abilities are so much greater than 2024 AI. And I really can't overemphasize how complex mammal neurons are compared to artificial ones: https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/ https://www.quantamagazine.org/how-computationally-complex-i... It might not matter that individual biological neurons are slow when by themselves they're as sophisticated as deep artificial neural networks. A neuron might operate at 200Hz but it's not solving "what's the sum of these floating-point vectors?" like a CPU/GPU. It's solving something way more sophisticated: there was a paper recently arguing that humans had individual neurons for visually classifying 2 things, 3 things, etc. (Cats can do this "subitizing" almost as well as we can and probably have their own neurons for it, but their understanding of numbers seems shallower than a primate's.) So: - the artificial neurons are far simpler than cat neurons - the links between the artificial neurons in neuromorphic computers are far simpler than the links between cat neurons in cat brains - even in naive ops/second cats are not slouches Simply matching the number of cat neurons and waving hands about "fast semiconductors" is not enough for me to think this machine is as powerful as a cat's brain. (And even if Intel made a computer theoretically capable of emulating a cat, I suspect it'll take several decades to develop AI software which is sophisticated enough to do the job.)
• phkahler 3y ago
  >> all of these can be exciting and interesting without pretending that it's closely related to human brains Why do you say they are not closely related? They are certainly an analogy to parts of the human brain. They are inspired by the way real neurons work at some level. But using the word "pretending" you sound as if you think these similarities are pure fiction. Why is that?
  • nicklecompte 3y ago
    I have three basic problems with using "brain" and especially "human brain": 1) Nobody is even bothering to explain why this is like a human brain rather than 1M spider brains or 1B nematode brains - I suspect 1M spider brains is probably closer to reality, and I doubt that adds up to one human. The choice of "human" is a marketing gimmick. 2) Even in nematode brains, each neuron is very different, in a way that can't be explained by a small array of floating-point numbers. These SNN architectures are too homogenous and simple to explain the actual behavior of individual neurons. (See https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/ https://www.quantamagazine.org/how-computationally-complex-i... which discusses standard ANNs, but I strongly doubt a single layer of SNN neurons can actually emulate an individual rat neuron.) 3) Epigenetic effects are clearly important for thinking and memory formation, yet totally ignored by these architectures: https://www.smithsonianmag.com/smart-news/scientists-say-they-have-transferred-memories-between-snails-180969087/ https://www.smithsonianmag.com/smart-news/scientists-say-the...
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  • Retric 3y ago
    Because it’s nowhere close to the scale of the human brain, calling it a mouse brain would be closer. People have 100x the neurons and 10,000x the synaptic connections and wildly different architecture, so it’s really got nothing in more in common with a human brain over some other kind of brain.
• Solvency 3y ago
  this will never happen. why? because humans are outrageously self-centeredly narcissistic. so narcissistic that we invented religions where god is just an omnipotent/omnipowerful version of a man, and then we go "god created man in his image" to justify it. humans can't help but be obsessed with other humans, the same way every species is hardwired in a specific way to relate to their own species. the idea of any computer system "brain" not being compared and contrasted to our own is against our very narcissistic nature.
• BobaFloutist 3y ago
  I do think there's some value in recognizing when people create something directly inspired by looking at how the brain works and trying to mimic it with hardware. Whether or not that's what's happening here is another matter.
  • abeppu 3y ago
    ... but the thing being mimicked is just as true of a mouse brain or (I think?) any vertebrate brain.
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    • BobaFloutist 3y ago
      Then the article title should probably be "mimicking biological brains"! Oh well.
• 2OEH8eoCRo0 3y ago
  Remove the human brain part and try to explain why this is news to a layperson.
  • abeppu 3y ago
    I'm not an expert in these systems, and their announcement materials don't make a bunch of stuff clear, but my attempt at summarizing for a layperson would be: - Current computers represent values as patterns of binary values. Analog computers sometimes represent values as e.g. voltages within a range. SNNs represent values with the timing of "spike" events. You could think of this like if you and your friends arranged a scheme to communicate using only the original Yo app, just by varying timing and frequency. - Current computers move values back and forth between memory and the CPU, and has very little room to hold data "close" to the CPU. Suppose your e-reader can only have one doc page open at a time; if you need to figure something out by closely comparing values between several docs, you might spend more time opening/closing/finding/paging through docs than actually reading them or thinking. These systems put (some) memory to be held by the same physical components which are doing the computations, so they aren't moving stuff back and forth in the same way. - However, current computers are quiet general in what they can do. In exchange for speed an efficiency, these systems are not general purpose. For example, b/c they have 1.15B "neurons" and 128B "synapses", any value can only (directly) influence on average 112 other values. You could think of this a bit like if Excel only let your formula reference a capped number of cells. - But for research problems which fit these constraints, this system can be extremely fast and use less power than a normal computer would.
    • 2OEH8eoCRo0 3y ago
      If I'm LARPing as a layperson then you've lost me in the first sentence.
• AndrewKemendo 3y ago
  Human capacity is the benchmark for capacity comparison for every thing humans care about on the planet For example even though the impulse-striking power or multi spectral vision system of the mantis shrimp is much more capable than human systems, it is not a benchmark for any of our systems Therefore we’re going to continue to evaluate everything. On human action until we create something that surpasses it. Comparing it to the brain, I agree is silly comparing them to human capabilities however, (which is effectively what people are saying when they compared to the brain, even though they don’t know that) is very reasonable