Current LLMs can not learn online, and I do not expect them to gain this capabilities any time soon. There are several technical and social reasons why I expect this. The current approach to AI is not really compatible with it, and it would probably be prohibitively computational expensive. AI researchers seem uninterested in studying the extant examples of learning (in animal brains). And finally, it scares the model makers by threatening their control over the model weights.
Sensory-motor control is another area that the current paradigm seems to be wandering off track.
ASI might be on the horizon, but they are not going to have the same mental faculties as humans.
Mammalian/human memory systems feel seamless to us, but neurological/psychological study of them shows they’re actually quite a complicated cludge of working memory, short-term, medium-term, and long-term episodic memory, procedural memory, traumatic memory, and so forth: there are probably something of the order of half-a-dozen-to-a-dozen subsystems working together in human memory/learning.
For LLMs, the capabilities people have been working hard on this for the last half-decade or so, and so far as widely-used memory/learning mechanisms we have: initial model training, retrieval-augmented generation and memory summarization, and in-context learning plus context compaction. That’s not nothing, and each of them is improving, but the combination still has a distinct weakness in the sort of on-them-job learning that a lot of human workers do, often even during a specific extended project.
At this rate of advance, it seems unlikely to me that we’re going to devise a complicated architecture as capable as the human one in the next couple of years. So this makes me a little septical about some of the “full AGI in the next 2–3 years” claims: on those timescales I’m expecting “sort of AGI but with online learning less good than humans”.
Current LLMs can not learn online, and I do not expect them to gain this capabilities any time soon. There are several technical and social reasons why I expect this. The current approach to AI is not really compatible with it, and it would probably be prohibitively computational expensive. AI researchers seem uninterested in studying the extant examples of learning (in animal brains). And finally, it scares the model makers by threatening their control over the model weights.
Sensory-motor control is another area that the current paradigm seems to be wandering off track.
ASI might be on the horizon, but they are not going to have the same mental faculties as humans.
Mammalian/human memory systems feel seamless to us, but neurological/psychological study of them shows they’re actually quite a complicated cludge of working memory, short-term, medium-term, and long-term episodic memory, procedural memory, traumatic memory, and so forth: there are probably something of the order of half-a-dozen-to-a-dozen subsystems working together in human memory/learning.
For LLMs, the capabilities people have been working hard on this for the last half-decade or so, and so far as widely-used memory/learning mechanisms we have: initial model training, retrieval-augmented generation and memory summarization, and in-context learning plus context compaction. That’s not nothing, and each of them is improving, but the combination still has a distinct weakness in the sort of on-them-job learning that a lot of human workers do, often even during a specific extended project.
At this rate of advance, it seems unlikely to me that we’re going to devise a complicated architecture as capable as the human one in the next couple of years. So this makes me a little septical about some of the “full AGI in the next 2–3 years” claims: on those timescales I’m expecting “sort of AGI but with online learning less good than humans”.
I suggest a self-optimizer will use similar solutions if it helps further optimization, otherwise not.