neuromorphic hardware is just hardware that has biologically inspired designs.
spiking neural networks are artificial neural networks that actually simulate the dynamics of spiking neurons. rather than sums, ramps and squashing, they simulate actual spike trains and the integration of energy that occurs in the dendrites.
neuromorphic hardware can range from specialized asics for doing these simulations efficiently to more experimental hybrid analog-digital systems that use analog elements to do more of the computation.
it's all very cool stuff, but i tend to think of snns as similar to the wings on the avion 3 where simplified unit functions look more like a modern jet wing.
but who knows, maybe the neuromorphic route will open the door to far more efficient computations. personally, i'm very excited about potential wins that could come from novel computational substrates!
Lots of people involved in explaining away AI are labouring under the axiom that intelligence is mysterious. Therefore, if I can understand how a system works, it logically follows that it can't be intelligent.
I predict that many of those people will continue to believe that up until human cognition is mechanistically understood, at which point there will be some other reason that humans are "real" thinkers and machines are not. The problem is that theoretical opposition to the existence of AIs is incompatible with materialism and thus just doesn't fit with our world, which is very much built using the scientific truths that materialism enables us to discover.
It is insane to me that views of consciousness and cognition other than physicalism still exist in mainstream scientific and philosophical discourse. As far as I can tell, no matter how much discourse you dress it up in, any alternative boils down to "it's magic, I ain't gotta explain shit".
We… can’t really understand how neural networks work, but we can definitely tell they’re not intelligent beyond making good sounding word soup (as demonstrated in their minimal practical reasoning abilities)
I wouldn’t call pagerank intelligent, even though I can give it a text prompt and get relevant information back.
In my view, the only difference between that and an llm is the natural language interface.
I’m no expert on intelligence, but I’d expect being able to introspect and continually learn to be part of it.
One way to help you notice this is to try and estimate how many billions of people you've defined out of "being intelligent" with your latest goalpost movement.
Be honest, how many people do you think "introspect and continually learn" on a daily basis?
> how many people do you think "introspect and continually learn" on a daily basis?
At the very least, every single person who plays sports, video games, tries finding a way around traffic, a faster route home, a way to do less work, take a longer break, or a way to save some extra money getting food.
Literally any optimization task at all requires an observation, analysis (read: introspection,) and adjustment. That’s why we model training loops as optimization problems.
We spoof that with REACT prompts in LLMs, but it becomes clear after a few iterations that there’s no real optimization going on, just guessing at tokens (a gross oversimplification, as this guessing has real uses). It’s doing what it was trained to do, completes text.
Not to mention that those steps all disappear when the prompt is changed.
> One way to help you notice this is to try and estimate how many billions of people you've defined out of "being intelligent" with your latest goalpost movement.
I think there is a difference from people upset around over hyped LLMs and arguing about intelligence in "A.I.". Most of the "intelligence" arguments I've seen are fighting against putting too much stock in chatgpt and Sam's fever dreams.
Words are useful to the extent they effectively communicate with the intended audience.
This can be accomplished by a mix of familiarity (has this word been already used enough in the target audience with the intended meaning) and the ability to evoke new meanings by intuitive derivation rules (word composition, affixes, ...)
In the case of this title, fwiw, it was perfectly clear to me what this was about because I'm already familiar with related topics and they were using the same terminology
spiking neural networks are artificial neural networks that actually simulate the dynamics of spiking neurons. rather than sums, ramps and squashing, they simulate actual spike trains and the integration of energy that occurs in the dendrites.
neuromorphic hardware can range from specialized asics for doing these simulations efficiently to more experimental hybrid analog-digital systems that use analog elements to do more of the computation.
it's all very cool stuff, but i tend to think of snns as similar to the wings on the avion 3 where simplified unit functions look more like a modern jet wing.
but who knows, maybe the neuromorphic route will open the door to far more efficient computations. personally, i'm very excited about potential wins that could come from novel computational substrates!