3 ms·

I used to think this same way, especially given I have a biology background. It is indeed true if you look at the internals of chatGPT it is doing a lot of basi

by DirkH 3y ago

I used to think this same way, especially given I have a biology background. It is indeed true if you look at the internals of chatGPT it is doing a lot of basic and dare I say dumb suboptimal things. However I find the analogy to flying compelling. It was thought to be decades days before the Wright Brother flight because of how simple, and seemingly dumb the engineering was relative to evolution's design in making a bird fly. There is a TED talk on this. It took us well over a generation later to build bots that can fly the way a bird does. That's how much longer it took us to understand biological flight. Yet by that time we had already made machines that can fly faster than any and all birds. We didn't need to mimic biology's design to get something that outclasses it. Could be the same with AI. We might be able to use engineering methods that are dumb relative to how biology sculpts intelligence, and yet still create an AI that outperforms all humans on all intelligence tasks. We may well create AGI that we end up coexisting with for over a generation before we figure out how intelligence in the biological brain works. And because we keep comparing AI currently to how biological intelligence works I am concerned the same thing will happen where a few days before it is released most people will be saying AGI is decades away. For me at least, this consideration makes "centuries away" look off and "decades away" seem far more likely... Which is in my lifetime. Yikes!

toss1 3y ago
Yes, I definitely agree that it is possible that an AI could emerge/be built without any direct biological analog, and flight is a good example (and the wheel is another — enables us to go faster than any legged creature but no biological analog). The questions are whether this generation of AI is actually intelligent (seems we ~agree different degrees of "Not yet"), and whether will lead to real intelligence and how close that is. Some of this is likely how we define "intelligence". I'm thinking of it in the human+/- level AGI, with the ability to abstract thoughts, sort facts from fiction, reason, and reason about itself sufficiently to set goals. Under this, I consider chess computers, AlphaGo, Watson, current "self-driving" and indeed ChatGPT4 to not meet the criteria, although they do produce very useful output. What's your assessment of these? Now, how far away are we? Let's take the arithmetic problem I cited above. I'd bet the OpenAI team is weeks-months away from solving that by generally recognizing all math problems, abstracting them, and passing it off to the Wolfram solve plug-In (like an old subroutine). And that is a level of abstraction that would massively increase usefulness. But, how many of those abstractions must be solved before it can abstract thoughts, sort facts from fiction, reason, and reason about itself sufficiently to set goals? The generative image systems' problems may be instructive. When asked to show something with a banana, they always show bunches of bananas. This could probably be patch-fixed in weeks with more training to distinguish single/plural bananas. But, I've also seen a "woman on a boat" generating what looks at first like an attractive bikini-clad woman, until we notice that her head & torso are attached the wrong way on the buttocks. This tells us that truly overcoming these problems requires the model to have abstract concepts of all physical objects in the lexicon and understand their relationships, so we don't get legs in impossible tangles or on backwards, etc. That would seem to require an entirely different generation of AI? I agree that it does not need to be biomicry to be intelligent; that only provides possibly useful hints to useful paths forward. I also agree that the likely time frame is years-to-decades, not months nor centuries. >>engineering methods that are dumb relative to how biology sculpts intelligence, and yet still create an AI that outperforms all humans on all intelligence tasks. Yes, this is definitely possible, especially with scaling. But do you see a path for LLMs alone to get us here with massive scaling up? I'm thinking it needs Large Abstraction Models layered on top of the LLM, sort of like the cortex is layered over and modifies the limbic system, which is layered over the brainstem, etc...?
- DirkH 3y ago
  So um... I just started playing with ChatGPT's new code interpreter today. Have you seen it? What it can do is insane. My mind is blown. Math is no longer an issue for ChatGPT. Possibly what it is doing now is analogous to having human system 1 and system 2. It didn't used to have a deep system 2 until now with strict logical steps. I.e. writing and executing code much like how someone doing math follows logical careful steps. Maybe it only had an intuitive find-patterns system 1 before. As for your Large Abstraction Models thought, Look up Vernon Benjamin Mountcastle's work. Neuroscientist that proposed that the whole neocortex operates through a common principle through cortical columns. Any Large Abstraction Model might well just be a distributed epiphenomenon of the network without any single nexus. If that is how we as humans work, wouldn't be surprising if just scaling LLMs up is all we need to do to get it to do anything a human can do online. Bye bye a lot of white-collar jobs if true. I hope your skepticism is warranted though and just scaling it up is not enough because our institutions are not ready for this kind of change.
  - toss1 3y ago
    Yes,I just read a bit about it and will check it out as soon as I get back to a computer (traveling). The math problem sounds like it should have been solved quickly (something akin to parse the language part, identify it as math w/o having the user say so, & hand it off to something like Wolfram Alpha plugin). Yes this is indeed moving fast, and at some point if it can perform all the functions well enough , it's pragmatically intelligent. I first read your sentence as "...out intuitions are not ready for this..." - which is also true, and our institutions -omg, they might as well be fieldmice watching rocket launches for how unready they are... yikes!

by DirkH 3y ago

toss1 3y ago

Yes, I definitely agree that it is possible that an AI could emerge/be built without any direct biological analog, and flight is a good example (and the wheel is another — enables us to go faster than any legged creature but no biological analog). The questions are whether this generation of AI is actually intelligent (seems we ~agree different degrees of "Not yet"), and whether will lead to real intelligence and how close that is. Some of this is likely how we define "intelligence". I'm thinking of it in the human+/- level AGI, with the ability to abstract thoughts, sort facts from fiction, reason, and reason about itself sufficiently to set goals. Under this, I consider chess computers, AlphaGo, Watson, current "self-driving" and indeed ChatGPT4 to not meet the criteria, although they do produce very useful output. What's your assessment of these? Now, how far away are we? Let's take the arithmetic problem I cited above. I'd bet the OpenAI team is weeks-months away from solving that by generally recognizing all math problems, abstracting them, and passing it off to the Wolfram solve plug-In (like an old subroutine). And that is a level of abstraction that would massively increase usefulness. But, how many of those abstractions must be solved before it can abstract thoughts, sort facts from fiction, reason, and reason about itself sufficiently to set goals? The generative image systems' problems may be instructive. When asked to show something with a banana, they always show bunches of bananas. This could probably be patch-fixed in weeks with more training to distinguish single/plural bananas. But, I've also seen a "woman on a boat" generating what looks at first like an attractive bikini-clad woman, until we notice that her head & torso are attached the wrong way on the buttocks. This tells us that truly overcoming these problems requires the model to have abstract concepts of all physical objects in the lexicon and understand their relationships, so we don't get legs in impossible tangles or on backwards, etc. That would seem to require an entirely different generation of AI? I agree that it does not need to be biomicry to be intelligent; that only provides possibly useful hints to useful paths forward. I also agree that the likely time frame is years-to-decades, not months nor centuries. >>engineering methods that are dumb relative to how biology sculpts intelligence, and yet still create an AI that outperforms all humans on all intelligence tasks. Yes, this is definitely possible, especially with scaling. But do you see a path for LLMs alone to get us here with massive scaling up? I'm thinking it needs Large Abstraction Models layered on top of the LLM, sort of like the cortex is layered over and modifies the limbic system, which is layered over the brainstem, etc...?

DirkH 3y ago
So um... I just started playing with ChatGPT's new code interpreter today. Have you seen it? What it can do is insane. My mind is blown. Math is no longer an issue for ChatGPT. Possibly what it is doing now is analogous to having human system 1 and system 2. It didn't used to have a deep system 2 until now with strict logical steps. I.e. writing and executing code much like how someone doing math follows logical careful steps. Maybe it only had an intuitive find-patterns system 1 before. As for your Large Abstraction Models thought, Look up Vernon Benjamin Mountcastle's work. Neuroscientist that proposed that the whole neocortex operates through a common principle through cortical columns. Any Large Abstraction Model might well just be a distributed epiphenomenon of the network without any single nexus. If that is how we as humans work, wouldn't be surprising if just scaling LLMs up is all we need to do to get it to do anything a human can do online. Bye bye a lot of white-collar jobs if true. I hope your skepticism is warranted though and just scaling it up is not enough because our institutions are not ready for this kind of change.
- toss1 3y ago
  Yes,I just read a bit about it and will check it out as soon as I get back to a computer (traveling). The math problem sounds like it should have been solved quickly (something akin to parse the language part, identify it as math w/o having the user say so, & hand it off to something like Wolfram Alpha plugin). Yes this is indeed moving fast, and at some point if it can perform all the functions well enough , it's pragmatically intelligent. I first read your sentence as "...out intuitions are not ready for this..." - which is also true, and our institutions -omg, they might as well be fieldmice watching rocket launches for how unready they are... yikes!