this post was submitted on 09 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 10 months ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
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.