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Norms will shift, be prepared.
Hmm.. I looked at the benchmark set. I'm conflicted. I don't know that I would necessarily want a model to pass all of these. Here is the fundamental problem. They are putting the rules and foundational context in "user" messages. Essentially I don't think you want to train the models on full compliance to the user messages, they are essentially "untrusted" content from a system/model perspective. Or at least it is n…
The article is suggesting that there should be a way for the LLM to gain knowledge (changing weights) on the fly upon gaining new knowledge which would eliminate the need for manual fine tuning.
Hmm.. I looked at the benchmark set. I'm conflicted. I don't know that I would necessarily want a model to pass all of these. Here is the fundamental problem. They are putting the rules and foundational context in "user" messages. Essentially I don't think you want to train the models on full compliance to the user messages, they are essentially "untrusted" content from a system/model perspective. Or at least it is n…
Ultimately I think we end up with the same sort of considerations that are wrestled with in any society - freedom of speech, paradox of tolerance, etc. In other words, where do you draw lines between beneficial and harmful heterodox outputs?
I think AI companies overly indexing toward the safety side of things is probably more correct, in both a moral and strategic sense, but there's definitely a risk of stagnation through recursive reinforcement.
There's pretraining, training, and finetuning, during which model parameters are updated.
Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model.
We need models that keep on learning (updating their parameters) forever, online, all the time.
Because we don't experience reality through language but direct sensory perception. Language is arbitrary bird song and visual representations dragged forward from history, accepted definitions never uniformly distributed. Testing based on contextual correctness makes no sense when there is no center to the universe. No "one true context to rule them all". We learn from hands on sensory experiences. Our bodies store…
”Because we don't experience reality through language but direct sensory perception” That statement is patently false. We know that language influences our senses to a degree where we are unable to perceive things if our language doesn’t have a word for it, and will see different things as being equal if our language uses the same word for both. There are examples of tribal humans not being able to perceive a green s…
https://languagelog.ldc.upenn.edu/nll/?p=18237 https://www.sciencedirect.com/science/article/abs/pii/S00100...
It is weird to read because they bring up many things a lot of people have been critiquing for years. > But as impressive as these feats are, they obscure a simple truth: being a "test-taker" is not what most people need from an AI. > In all these cases, humans aren't relying solely on a fixed body of knowledge learned years ago. We are learning, in real-time, from the context right in front of us. > To bridge this g…
> But as impressive as these feats are, they obscure a simple truth: being a "test-taker" is not what most people need from an AI.
I haven't looked at the benchmark details they've used, and it may depend on the domain, empirically it seems coding agents improve drastically on unseen libs or updated libs with the latest documentation. So I think that a matter of the training sets, where they've been optimized with code documentation.
So the interim step until a better architecture is found is probably more / better training data.
Bit by bit, we need to figure out how to rebuild human contextual understanding in a way that LLMs can understand. One thing that gets overlooked is the problem if incorrect data. You can provide all of the context in the world but LLMs tend to choke on contradictions or, at the minimum, work a whole lot harder to determine how to ignore or work around incorrect facts. "Forgetting" and "ignoring" are hugely valuable…
And, yeah. Imagine if our concept-words were comprehensible, transmittable, exhaustively checked, and fully defined. Imagine if that type inference extended to computational execution and contradictions had to be formally expunged. Imagine if research showed it was more efficient way to have dialog with the LLM (it does, btw, so like learning Japanese to JRPG adherents should learn Haskell to LLM optimally). Imagine if multiple potential outcomes from operations (test fail, test succeeds), could be combined for proper handling in some kind of… I dunno, monad?
Imagine if we had magic wiki-copy chat-bots that could teach us better ways of formalizing and transmitting our taxonomies and ontologies… I bet, if everything worked out, we’d be able to write software one time, one place, that could be executed over and over forever without a subscription. Maybe.
Because we don't experience reality through language but direct sensory perception. Language is arbitrary bird song and visual representations dragged forward from history, accepted definitions never uniformly distributed. Testing based on contextual correctness makes no sense when there is no center to the universe. No "one true context to rule them all". We learn from hands on sensory experiences. Our bodies store…
”Because we don't experience reality through language but direct sensory perception” That statement is patently false. We know that language influences our senses to a degree where we are unable to perceive things if our language doesn’t have a word for it, and will see different things as being equal if our language uses the same word for both. There are examples of tribal humans not being able to perceive a green s…
It's basically continual learning. This is beyond a hard problem it's currently an impossible one. I know of no system that solve CL even at small scale let alone large models. Annoyingly, they have SOME inherent capability to do it. It's really easy to get sucked down this path due to that glimmer of hope but the longer you play with it the more annoying it becomes. SSI seems to be focused on this problem directly s…
LLMs of the future will need good data for proper context, but it is less and less making it onto the internet. Unpublished data stores like Discord or meeting recordings are going to be the only way forward. How else can you get up to date information except to be where the people are. Norms will shift, be prepared.
We need to discover the set of learning algorithms nature has, and determine whether they’re implementable in silicon