3 ms·

Well, what if you trained on the unbelievable amount of data that enters the human brain? Like, say inputting years of high definition video, audio, propriocep

by wilg 7y ago

Well, what if you trained on the unbelievable amount of data that enters the human brain? Like, say inputting years of high definition video, audio, proprioception, introspection, debug and error logs, data from a bunch of other sensors, etc. Then put that in a really tight loop with high precision motion and audio output devices, and keep learning. Also do it on a really fast computer with lots of memory. Also make the code itself an output. If thats not enough, you could always try self replicating to try to create more successful versions over million year timescales.

Barrin92 7y ago
That's trivially true in the sense that this is how we can only assume humans and animals came to be but I don't think there's any guarantee that this can be replicated in a silicon-based software architecture which is very different from analog and chemical biological organisms. Today already energy and computional costs are high with model computation cost going into five or six figures even in just one domain. But more importantly, I think the problem with this approach is that it's essentially a hail mary of sorts with potentially zero scientific insight into how the brain and the mind works. It's a little bit like behaviourism before the cognitive revolution with AI models being the equivalent of a Skinner box.
- wilg 7y ago
  I don't know the history of it and am not an expert in the field, but it seems to me that it's valid to call the things ML can already do "intelligence" in a generalized sense, and that there is nothing categorically different between that and human intelligence, it's just a matter of how complicated humans are. That seems like a problem you can sort of throw hardware at for a while until it gets good enough to help you figure out how to make something smarter.
  - jsinai 7y ago
    I think there is little evidence to suggest that neural networks and human minds behave in the same way modulo complexity. In science there is a tendency to come up with models of the world - simplifications which we can observe and quantify - and then fall into the trap thinking that these models explain the world. While neural networks are inspired by biological neural connections between synapses and neurons, the converse that neural networks are therefor intelligent does not hold.
    - wilg 7y ago
      I'm not suggesting NNs should be considered intelligent because of their inspiration from biology. Just that they should be considered intelligent because of what they are currently capable of (though that is way less intelligent than humans or animals of course). But it seems like there is a plausible path to increasing their "intelligence" by dumping more data and hardware at them. Like GPT2 shows quite a surprising amount of structural complexity even though it's very dumb in other ways – it feels like if you could pump a million times more data into it you'd get something that seemed really quite intelligent.

by wilg 7y ago

Barrin92 7y ago

That's trivially true in the sense that this is how we can only assume humans and animals came to be but I don't think there's any guarantee that this can be replicated in a silicon-based software architecture which is very different from analog and chemical biological organisms. Today already energy and computional costs are high with model computation cost going into five or six figures even in just one domain. But more importantly, I think the problem with this approach is that it's essentially a hail mary of sorts with potentially zero scientific insight into how the brain and the mind works. It's a little bit like behaviourism before the cognitive revolution with AI models being the equivalent of a Skinner box.

wilg 7y ago
I don't know the history of it and am not an expert in the field, but it seems to me that it's valid to call the things ML can already do "intelligence" in a generalized sense, and that there is nothing categorically different between that and human intelligence, it's just a matter of how complicated humans are. That seems like a problem you can sort of throw hardware at for a while until it gets good enough to help you figure out how to make something smarter.
- jsinai 7y ago
  I think there is little evidence to suggest that neural networks and human minds behave in the same way modulo complexity. In science there is a tendency to come up with models of the world - simplifications which we can observe and quantify - and then fall into the trap thinking that these models explain the world. While neural networks are inspired by biological neural connections between synapses and neurons, the converse that neural networks are therefor intelligent does not hold.
  - wilg 7y ago
    I'm not suggesting NNs should be considered intelligent because of their inspiration from biology. Just that they should be considered intelligent because of what they are currently capable of (though that is way less intelligent than humans or animals of course). But it seems like there is a plausible path to increasing their "intelligence" by dumping more data and hardware at them. Like GPT2 shows quite a surprising amount of structural complexity even though it's very dumb in other ways – it feels like if you could pump a million times more data into it you'd get something that seemed really quite intelligent.