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I agree, though I would argue the issue is closer to no one's trying to measure how close we should be. If you take the materialist view - and in this business

by XorNot 1y ago

I agree, though I would argue the issue is closer to no one's trying to measure how close we should be. If you take the materialist view - and in this business you obviously have to - then the question is "do we have human level computational capacity with some level of real-time learning ability?" So the yardstick we need is (1): what actually is the computational capacity of the human brain? (i.e. the brain obviously doesn't simulate it's own neurons, so what is a neuron doing and how does that map to compute operations?) and then (2) - is any computer system running an AI model plausibly working within those margins or exceeding them? With the second part being: and can that system implement the sort of dynamic operations which humans obviously must, unaided? i.e. when I learn something new I might go through a lot of repetitions, but I don't just stop being able to talk about a subject for months while ingesting sum content of the internet to rebuild my model.

naasking 1y ago
> do we have human level computational capacity with some level of real-time learning ability We don't build airplanes by mimicking birds. AGIs computational capacity won't be directly comparable to human computation capacity that was formed by messy, heuristic evolution. You're right that there is some core nugget in there that needs to be replicated though, and it will probably be by accident, as with most inventions. Hardware is still progressing exponentially, and performance improvements from model and algorithmic improvements have been outpacing hardware progress for at least a decade. The idea that AGI will take a century or more is laughable. Now that's borderline deluded.
- XorNot 1y ago
  > We don't build airplanes by mimicking birds. We don't build airplanes by having them flap wings, but we do build planes which are considerably more aerodynamically efficient then a bird. i.e. we considerably exceed the underlying performance metric governed by the physics. Which is my point: an AI need not work anything like a human, but it seems obvious that model training taking months of time to incorporate new information fundamentally cannot be close to AGI since even far simpler creatures can learn and retain information on much shorter timescales. I'd give the notion more credence if we plausibly had say, a model with a context window which could hold a day of information, and then do could train overnight on that such that it would incorporate that context window into the next day of activity.
  - naasking 1y ago
    The infrastructure is almost there now. LLMs are already training small domain specific models. Small language models have already shown excellent performance in specific domains, particularly logic and math. All that's needed is an extensible mixture of experts router so that these small models can be plugged into the larger model and selected when the query is appropriate. This isn't far off.

by XorNot 1y ago

naasking 1y ago

> do we have human level computational capacity with some level of real-time learning ability We don't build airplanes by mimicking birds. AGIs computational capacity won't be directly comparable to human computation capacity that was formed by messy, heuristic evolution. You're right that there is some core nugget in there that needs to be replicated though, and it will probably be by accident, as with most inventions. Hardware is still progressing exponentially, and performance improvements from model and algorithmic improvements have been outpacing hardware progress for at least a decade. The idea that AGI will take a century or more is laughable. Now that's borderline deluded.

XorNot 1y ago
> We don't build airplanes by mimicking birds. We don't build airplanes by having them flap wings, but we do build planes which are considerably more aerodynamically efficient then a bird. i.e. we considerably exceed the underlying performance metric governed by the physics. Which is my point: an AI need not work anything like a human, but it seems obvious that model training taking months of time to incorporate new information fundamentally cannot be close to AGI since even far simpler creatures can learn and retain information on much shorter timescales. I'd give the notion more credence if we plausibly had say, a model with a context window which could hold a day of information, and then do could train overnight on that such that it would incorporate that context window into the next day of activity.
- naasking 1y ago
  The infrastructure is almost there now. LLMs are already training small domain specific models. Small language models have already shown excellent performance in specific domains, particularly logic and math. All that's needed is an extensible mixture of experts router so that these small models can be plugged into the larger model and selected when the query is appropriate. This isn't far off.