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Wait what are you referring to specifically? Any anthropomorphism in the article is _clearly_, clearly the author's admitted simplification due to the incredibl

Wait what are you referring to specifically? Any anthropomorphism in the article is _clearly_, clearly the author's admitted simplification due to the incredible density of the subject matter. Given that, I honestly can't find anything too upsetting. In any case, anthropomorphism is something I don't mind, mostly. Is it misleading? For the layman. But the domain is one of modeling intelligence itself and there are many instances where an existing definition simply makes sense. This happens in lots of fields and causes similar amounts of frustration in those fields. So it goes.

janalsncm 3y ago
> Humans also use neural nets to reason about concepts. We have a lot of neurons, but so does GPT-4. I feel this is an abuse of the language. Biological neurons and ANN neurons aren’t the same or even all that similar. Brains don’t do backprop for example. Only forward passes. There’s a zoo of neurotransmitters which change the behavior of individual neurons or regions in the brain. Unused neurons in the brain can be repurposed for other things (for example if your arm is amputated).
- famouswaffles 3y ago
  >Biological neurons and ANN neurons aren’t the same or even all that similar. They're not the same but they're definitely similar. >Brains don’t do backprop for example. We've developed numerous different learning algorithms that are biologically plausible, but they all kinda work like backpropagation but worse, so we stuck with backpropagation. We've made more complicated neurons that better resemble biological neurons, but it is faster and works better if you just add extra simple neurons, so we do that instead. Spiking neural networks have connection patterns more similar to what you see in the brain, but they learn slower and are tougher to work with than regular layered neural networks, so we use layered neural networks instead. The only reason modern NNs aren't closer to their biological counterparts is because they genuinely suffer for it. The secret of bird flight was wings. Not feathers. Not flapping.
  - tsimionescu 3y ago
    > The only reason modern NNs aren't closer to their biological counterparts is because they genuinely suffer for it. And yet, there's no ANN that's as good at interacting with the real world as the simplest worms we've studied, despite having many times more neurons than those worms have cells. We are clearly still missing some key pieces of the puzzle for intelligence, so claiming that the difference between ANNs and biological neurons is irrelevant is quite premature. We are far away from having an airfoil moment in AI research.
    - FeepingCreature 3y ago
      Simple organisms are not necessarily easier to imitate than more complex ones. Our vehicles can drive faster than the fastest animals, but no car can absorb nutrients across a cell membrane. The simpler a system is, the more an imitation has to stick to the specifics to the system's particular implementation. Larger, more complex systems can usually perform more abstract tasks, which allows more diverse approaches.
    - ben_w 3y ago
      I assert that most robotics AI, including self driving cars, are at least as good at interacting with the real world as the simplest worms we've studied. Do different things, but at least as good.
  - janalsncm 3y ago
    My point isn’t about whether ANNs work, it’s that they’re so fundamentally different (both locally at the level of the neuron and globally at the level of the brain) that calling them both “neurons” is pretty imprecise. Silicon simulations of brains may “suffer” from being faithful but this also discounts the advantages that brains have. As I mentioned for example, brains can repurpose neurons for other tasks. Brains can also generalize from a single example, unlike neural networks which require thousands if not millions of examples. Brains also generally do not suffer from catastrophic forgetting in the same way that our simulations tend to. If I ask you to study a textbook on cats you won’t suddenly forget the difference between cats and dogs.

janalsncm 3y ago

> Humans also use neural nets to reason about concepts. We have a lot of neurons, but so does GPT-4. I feel this is an abuse of the language. Biological neurons and ANN neurons aren’t the same or even all that similar. Brains don’t do backprop for example. Only forward passes. There’s a zoo of neurotransmitters which change the behavior of individual neurons or regions in the brain. Unused neurons in the brain can be repurposed for other things (for example if your arm is amputated).

famouswaffles 3y ago
>Biological neurons and ANN neurons aren’t the same or even all that similar. They're not the same but they're definitely similar. >Brains don’t do backprop for example. We've developed numerous different learning algorithms that are biologically plausible, but they all kinda work like backpropagation but worse, so we stuck with backpropagation. We've made more complicated neurons that better resemble biological neurons, but it is faster and works better if you just add extra simple neurons, so we do that instead. Spiking neural networks have connection patterns more similar to what you see in the brain, but they learn slower and are tougher to work with than regular layered neural networks, so we use layered neural networks instead. The only reason modern NNs aren't closer to their biological counterparts is because they genuinely suffer for it. The secret of bird flight was wings. Not feathers. Not flapping.
- tsimionescu 3y ago
  > The only reason modern NNs aren't closer to their biological counterparts is because they genuinely suffer for it. And yet, there's no ANN that's as good at interacting with the real world as the simplest worms we've studied, despite having many times more neurons than those worms have cells. We are clearly still missing some key pieces of the puzzle for intelligence, so claiming that the difference between ANNs and biological neurons is irrelevant is quite premature. We are far away from having an airfoil moment in AI research.
  - FeepingCreature 3y ago
    Simple organisms are not necessarily easier to imitate than more complex ones. Our vehicles can drive faster than the fastest animals, but no car can absorb nutrients across a cell membrane. The simpler a system is, the more an imitation has to stick to the specifics to the system's particular implementation. Larger, more complex systems can usually perform more abstract tasks, which allows more diverse approaches.
  - ben_w 3y ago
    I assert that most robotics AI, including self driving cars, are at least as good at interacting with the real world as the simplest worms we've studied. Do different things, but at least as good.
- janalsncm 3y ago
  My point isn’t about whether ANNs work, it’s that they’re so fundamentally different (both locally at the level of the neuron and globally at the level of the brain) that calling them both “neurons” is pretty imprecise. Silicon simulations of brains may “suffer” from being faithful but this also discounts the advantages that brains have. As I mentioned for example, brains can repurpose neurons for other tasks. Brains can also generalize from a single example, unlike neural networks which require thousands if not millions of examples. Brains also generally do not suffer from catastrophic forgetting in the same way that our simulations tend to. If I ask you to study a textbook on cats you won’t suddenly forget the difference between cats and dogs.