4 ms·

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.)
        3y ago

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.)
      - 3y ago