Comment on Nvidia CEO Jensen Huang says ‘I think we’ve achieved AGI’
Modern_medicine_isnt@lemmy.world 5 days agoI agree, ut not because of lost state. As mentioned by others, state can be managed. You could also just do a feedback loop. These improve, but don’t solve. The issue is that it doesn’t understand. You mention that it is just a word predictor. And that is the heart of it. It predicts based on odds more or less, not on understanding. That said, it has room to improve. I think having lots and lots of agents that are highly specialized is probably the key. The more narrow the focus, the closer prediction comes to fact. Then throw in asking 5 versions of the agent the same question and tossing the outliers and you should get pretty useful. Not AGI, but useful. The issue is that with current technology, that is simply too expensive. So a breakthrough in the expense of current AI is needed first, then we can get more useful AI. AGI will be a significantly different technology.
Technus@lemmy.zip 5 days ago
The conversion of the output to tokens inherently loses a lot of the information extracted by the model and any intermediate state it has synthesized (what it “thinks” of the input).
Until the model is able to retain its own internal state and able to integrate new information into that state as it receives it, all it will ever be able to do is try to fill in the blanks.
Modern_medicine_isnt@lemmy.world 4 days ago
Not sure what this internal state you are referring to is. Are you talking about all the values that come out of each step of the computations?
As for your second half… integration. That is a tricky one. Because the inputs it is getting aren’t necessarily correct. So that can do more harm than good. The current loop for integrating new data is too long though. They need to reduce that down to like an hour so it can absorb current events at least. And ideally they would be able to take a conversation and identify what worked and what didn’t. Then integrate what did. This is what was mentioned about claud.md files and such that essentially keep track of wwhat was learned. There is room for improvement there, as I seem to have to tell the model to go read those or it doesn’t.
Technus@lemmy.zip 4 days ago
It would need to be able to form memories like real brains do, by creating new connections between neurons and adjusting their weights in real time in response to stimuli, and having those connections persist. I think that’s a prerequisite to models that are capable of higher-level reasoning and understanding. But then you would need to store those changes to the model for each user, which would be tens or hundreds of gigabytes.
These current once-through LLMs don’t have time to properly digest what they’re looking at, because they essentially forget everything once they output a token. I don’t think you can make up for that by spitting some tokens out to a file and reading them back in, because it still has to be human-readable and coherent. That transformation is inherently lossy.
This is basically what I’m talking about: https://www.comicagile.net/comic/context-switching/
But for every single token the LLM outputs. The fact that it’s allowed to take notes is a mitigation for this context loss, not a silver bullet.
Modern_medicine_isnt@lemmy.world 3 days ago
Yeah, I get what you are saying. I’m just not convinced that it needs to be able to update it’s model in real time to be capable of high level reasoning. And while human readable files are inherantly lossy, they do still represents tracking an internal state.
They also have vector dbs. My understanding is that they are closer to what you are talking about as far as internal state. But they still don’t allow th AI to update the vectordb in real time. Mainly they worry about what happens with live updates being similar to how people are easily manipulated into believeing BS. So they are more careful about what they feed it to update. I do wonder how they generate those vector dbs, and if that is something users could utilize locally.