this post was submitted on 09 Nov 2023
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Machine Learning
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I doubt that any model currently is in the “emerging AGI” category (even by there own metric of “general ability and metacognitive abilities like learning new skills”).
The model(s) we currently have are fundamentally unable to update their own weights so they do not “learn new skills”. Also I don’t like how they use “wide range of tasks” as a metric. Yes, LLMs outperform many humans at things like standardized tests, but I have yet to see an LLM who can constantly play tiktaktoe at the level of a 5 year old without a paragraph of “promt engineering”
I’m not the most educated on this topic (still just a student studying machine learning) but imo I think that many researchers are overestimating the abilities of LLMs
Not designed or intended to- is not the same "fundementally unable". There are quite simplistic architectures that are very able of updating their weights, which does not make them AGI or any more intelligent. The discussion is about general capability in intellectual tasks, not the training mechanisms.
I more so meant that to learn something new the model would have to update its own weights (I have my reasoning for this in another reply in this thread).
When I said “fundamentally unable to” I meant that current LLM architectures do not have the capability to update their own weights (although I probably should’ve worded that a bit differently)
They don't have it because it wasn't programmed into it, because it's risky business (see chatbot Tay), not because it's currently impossible. There's nothing preventing you from running backprop weight updates based on user interactions, e.g. with reinforcement from user sentiment.