3 ms·

Actually, I'm comparing the "neuron-neuron connection count," while admitting that the comparison is not apples-to-apples. This kind of comparison isn't a new

by cs702 2y ago

Actually, I'm comparing the "neuron-neuron connection count," while admitting that the comparison is not apples-to-apples. This kind of comparison isn't a new idea. I think Hans Moravec[a] was the first to start making these kinds of machine-to-organic-brain comparisons, back in the 1990's, using "millions of instructions per second" (MIPS) and "megabytes of storage" as his units. You can read Moravec's reasoning and predictions here: https://www.jetpress.org/volume1/moravec.pdf https://www.jetpress.org/volume1/moravec.pdf --- [a] https://en.wikipedia.org/wiki/Hans_Moravec https://en.wikipedia.org/wiki/Hans_Moravec

legacynl 2y ago
I think he was approaching the concept from the direction of "how many mips and megabytes do we need to create human level intelligence". That's a different take than "human level is this many mips and megabytes", i.e. his claims are about artificial intelligence, not about biological intelligence. The machine learning seems to be modeled after the action potential part of neural communication. But biological neurons can communicate also in different ways, i.e. neuro transmitters. Afaik this isn't modeled in the current ml-models at all (neither do we have a good idea how/why that stuff works). So ultimately it's pretty likely that a ml with a billion parameters does not perform the same as an organic brain with a billion synapses
- cs702 2y ago
  I never claimed the machines would achieve "human level," however you define it. What I actually wrote at the root of this thread is that we have no way of knowing in advance what the future capabilities of these AI systems might be as we scale them up.
joe_the_user 2y ago
Your "not apples to apples" concession isn't adequate. You are essentially still saying that a machine running a neural network is compare to the brain of an animal or a person - just maybe different units of measurement. But they're not. It's a matter of dramatically different computing systems, systems that operate very differently (well, don't know exactly how animal brains work but we know enough to know they don't work like GPUs). Your Moravec article is only looking at what's necessary for computers to have the processing power of animal brains. But you've been up and down this thread arguing that equivalent processing power could be sufficient for a computer to achieve the intelligence of an animal. Necessary vs sufficient is big distinction.
- cs702 2y ago
  It might be sufficient. We do not know. We have no way of knowing. Given their current scale, I don't think we can judge whether current AI systems "represent a dead end" -- or not.

by cs702 2y ago

legacynl 2y ago

I think he was approaching the concept from the direction of "how many mips and megabytes do we need to create human level intelligence". That's a different take than "human level is this many mips and megabytes", i.e. his claims are about artificial intelligence, not about biological intelligence. The machine learning seems to be modeled after the action potential part of neural communication. But biological neurons can communicate also in different ways, i.e. neuro transmitters. Afaik this isn't modeled in the current ml-models at all (neither do we have a good idea how/why that stuff works). So ultimately it's pretty likely that a ml with a billion parameters does not perform the same as an organic brain with a billion synapses

cs702 2y ago
I never claimed the machines would achieve "human level," however you define it. What I actually wrote at the root of this thread is that we have no way of knowing in advance what the future capabilities of these AI systems might be as we scale them up.

joe_the_user 2y ago

Your "not apples to apples" concession isn't adequate. You are essentially still saying that a machine running a neural network is compare to the brain of an animal or a person - just maybe different units of measurement. But they're not. It's a matter of dramatically different computing systems, systems that operate very differently (well, don't know exactly how animal brains work but we know enough to know they don't work like GPUs). Your Moravec article is only looking at what's necessary for computers to have the processing power of animal brains. But you've been up and down this thread arguing that equivalent processing power could be sufficient for a computer to achieve the intelligence of an animal. Necessary vs sufficient is big distinction.

cs702 2y ago
It might be sufficient. We do not know. We have no way of knowing. Given their current scale, I don't think we can judge whether current AI systems "represent a dead end" -- or not.