How does ChatGPT4 with plugins (Wolfram Alpha etc.), code execution and internet access compare to AGI? Is the difference still a qualitative one, or merely a quantitative one? I’d be curious about both technological or philosophical takes.
When people say the human brain is general, I wondered if to some degree, this is misleading; it is not that the brain is one monolithic thing that can do everything. Rather, it has some capacities with very broad applications, and a number of modules for specific applications with some transferability, and the ability to select them were appropriate, to adapt existing structures to new situations or to compensate for injuries. But it is still more of a bunch of components acting in concert to enable the entity as a whole to deal with lots of different and novel stuff; not everything by a long shot. The system as a whole is flexible, but not without limit. If you destroy the visual cortex, the person stays cortically blind. If humans are given access to a novel kind of sense data, they do not develop vivid qualia like they have for vision. The more humans get out of the range of our ancestral environment, the worse we get at dealing with it; we have a terrible time e.g. getting an accurate intuition for physics the moment we deal with things that have very high mass, or move very fast, or are placed outside of our planets gravity well. We deal poorly with large numbers. We have huge problems wrapping our minds around our own consciousness. We are prone to a lot of bias when it comes to hindsight, loss, probabilities, plausible narratives, superstitions. We can’t deal well with abstract stressors and threats. In many ways, our brain acts less than a one-thing-does-all, and more of a complex Swiss army knife with components that have multiple uses or can be bent somewhat, but still sucks for some tasks.
Intuitively and superficially, what ChatGPT4 with plugins does seems somewhat similar. It can process and generate language, and has a bunch of knowledge. If the knowledge does not suffice, it can reach out to the internet via bing search. If it needs to generate visuals, it can reach out to Dall-e. If it needs to do math, it can reach out to Wolfram Alpha. If it needs to execute code, it uses its sandbox. There are other applications that are not implemented yet, but seem trivial to add and likely around the corner: e.g. audio processing (especially understanding voice commands, like Siri/Alexa), generating audio (especially reading out its own text, like a screenreader) reaching out to a chess or Go AI (where we have AIs massively exceeding human ability, and a simple notation to communicate moves). Others that sound harder to implement, but foreseeable in principle, e.g. accessing robots (drones, automatic cars, factory robots) to access footage or guide motion via third party apps to input goals, without guiding the details. Much of the execution here would not be done by ChatGPT4 themselves; but that seems to me akin to a human brain, where the conscious part of me often just ends up with broad wishes or a a bad feeling, without guiding fine motion control, or realising what sensory processing flagged a situation for high risk. For a huge number of tasks, we already have AI that does reasonable well; we have many where AI are human-competitive, and some where they exceed human ability. If you can cobble together a system for one AI to call on the others, and integrate the results, like ChatGPT4 does with plug-ins, this sounds in practice very similar to a general AI. Am I missing something crucial that changes this?
It doesn’t seem quite there yet; it doesn’t always know when to reach out to which external aid, does not always integrate the information appropriately. For complex tasks, it still needs human prodding, hints, guidance for reasoning its way through. But this looks like a problem that can be solved with more training, naively.
Yet an LLM does seem relatively well positioned for being the center of AGI. Language in humans plays a crucial role in general intelligence; being able to relate and generate novel symbols can be applied to many contexts and make them handleable.
It still seems somewhat different from human AGI; the third party apps are clearly separate, access needs to be given via humans, and could, at least initially, be reversed. And it isn’t generating the third party apps, just using them, so this puts a limit on intelligence explosion, and also simply on adapting to novel situations. I am also not sure if an LLM is really equipped to effectively take over the roles of executive function, global workspace, introspective reasoning etc. But it no longer seems like a completely different realm.
And if this were the path we would take to AGI… what would that entail? See above; it intuitively seems more tricky for such an AI to become truly general, deal with completely novel problems, or have an intelligence explosion/singularity/take-off. Would this put us in a position where we see massive capacity gains and people get alarmed, but it won’t necessarily get entirely out of hand? Or are we going to just see ChatGPT4 learn how to code access to more plug-ins, and read the source-code for the third party apps it uses, and write more and better code for those third party apps, and execute that in its sandbox, and figure out a way out of that?
How does ChatGPT4 with plugins (Wolfram Alpha etc.), code execution and internet access compare to AGI? Is the difference still a qualitative one, or merely a quantitative one? I’d be curious about both technological or philosophical takes.
When people say the human brain is general, I wondered if to some degree, this is misleading; it is not that the brain is one monolithic thing that can do everything. Rather, it has some capacities with very broad applications, and a number of modules for specific applications with some transferability, and the ability to select them were appropriate, to adapt existing structures to new situations or to compensate for injuries. But it is still more of a bunch of components acting in concert to enable the entity as a whole to deal with lots of different and novel stuff; not everything by a long shot. The system as a whole is flexible, but not without limit. If you destroy the visual cortex, the person stays cortically blind. If humans are given access to a novel kind of sense data, they do not develop vivid qualia like they have for vision. The more humans get out of the range of our ancestral environment, the worse we get at dealing with it; we have a terrible time e.g. getting an accurate intuition for physics the moment we deal with things that have very high mass, or move very fast, or are placed outside of our planets gravity well. We deal poorly with large numbers. We have huge problems wrapping our minds around our own consciousness. We are prone to a lot of bias when it comes to hindsight, loss, probabilities, plausible narratives, superstitions. We can’t deal well with abstract stressors and threats. In many ways, our brain acts less than a one-thing-does-all, and more of a complex Swiss army knife with components that have multiple uses or can be bent somewhat, but still sucks for some tasks.
Intuitively and superficially, what ChatGPT4 with plugins does seems somewhat similar. It can process and generate language, and has a bunch of knowledge. If the knowledge does not suffice, it can reach out to the internet via bing search. If it needs to generate visuals, it can reach out to Dall-e. If it needs to do math, it can reach out to Wolfram Alpha. If it needs to execute code, it uses its sandbox. There are other applications that are not implemented yet, but seem trivial to add and likely around the corner: e.g. audio processing (especially understanding voice commands, like Siri/Alexa), generating audio (especially reading out its own text, like a screenreader) reaching out to a chess or Go AI (where we have AIs massively exceeding human ability, and a simple notation to communicate moves). Others that sound harder to implement, but foreseeable in principle, e.g. accessing robots (drones, automatic cars, factory robots) to access footage or guide motion via third party apps to input goals, without guiding the details. Much of the execution here would not be done by ChatGPT4 themselves; but that seems to me akin to a human brain, where the conscious part of me often just ends up with broad wishes or a a bad feeling, without guiding fine motion control, or realising what sensory processing flagged a situation for high risk. For a huge number of tasks, we already have AI that does reasonable well; we have many where AI are human-competitive, and some where they exceed human ability. If you can cobble together a system for one AI to call on the others, and integrate the results, like ChatGPT4 does with plug-ins, this sounds in practice very similar to a general AI. Am I missing something crucial that changes this?
It doesn’t seem quite there yet; it doesn’t always know when to reach out to which external aid, does not always integrate the information appropriately. For complex tasks, it still needs human prodding, hints, guidance for reasoning its way through. But this looks like a problem that can be solved with more training, naively.
Yet an LLM does seem relatively well positioned for being the center of AGI. Language in humans plays a crucial role in general intelligence; being able to relate and generate novel symbols can be applied to many contexts and make them handleable.
It still seems somewhat different from human AGI; the third party apps are clearly separate, access needs to be given via humans, and could, at least initially, be reversed. And it isn’t generating the third party apps, just using them, so this puts a limit on intelligence explosion, and also simply on adapting to novel situations. I am also not sure if an LLM is really equipped to effectively take over the roles of executive function, global workspace, introspective reasoning etc. But it no longer seems like a completely different realm.
And if this were the path we would take to AGI… what would that entail? See above; it intuitively seems more tricky for such an AI to become truly general, deal with completely novel problems, or have an intelligence explosion/singularity/take-off. Would this put us in a position where we see massive capacity gains and people get alarmed, but it won’t necessarily get entirely out of hand? Or are we going to just see ChatGPT4 learn how to code access to more plug-ins, and read the source-code for the third party apps it uses, and write more and better code for those third party apps, and execute that in its sandbox, and figure out a way out of that?