> LLMs can form new memories dynamically. Just pop some new data into the context.
No, that's an illusion.
The LLM itself is static. The recurrent connections form a soft-of temporary memory that doesn't affect the learned behavior of the network at all.
I don't get why people who don't understand what's happening keep arguing that AIs are some sci-fi interpretation of AI. They're not. At least not yet.
It isn't temporary if you keep it permanently in context (or in a RAG store) and pass it into every model call, which is how long-term memory is being implemented both in research and in practice. And yes it obviously does affect the learned behavior. The distinction you're making between training and context is arbitrary.
Endless ink has been spilled on the most banal and useless things. Deconstructing ice cream and physical beauty from a Marxist-feminist race-conscious postmodern perspective.
Every single discussion of ‘AGI’ has endless comments exactly like this. Whatever criticism is made of an attempt to produce a reasoning machine, there’s always inevitably someone who says ‘but that’s just what our brains do, duhhh… stop trying to feel special’.
It’s boring, and it’s also completely content-free. This particular instance doesn’t even make sense: how can it be exactly the same, yet more sophisticated?
The problem is that we currently lack good definitions for crucial words such as "understanding" and we don't know how brains work, so that nobody can objectively tell whether a spreadsheet "understands" anything better than our brains. That makes these kinds of discussions quite unproductive.
I can’t define ‘understanding’ but I can certainly identify a lack of it when I see it. And LLM chatbots absolutely do not show signs of understanding. They do fine at reproducing and remixing things they’ve ‘seen’ millions of times before, but try asking them technical questions that involve logical deduction or an actual ability to do on-the-spot ‘thinking’ about new ideas. They fail miserably. ChatGPT is a smooth-talking swindler.
I suspect those who can’t see this either
(a) are software engineers amazed that a chatbot can write code, despite it having been trained on an unimaginably massive (morally ambiguously procured) dataset that probably already contains something close to the boilerplate you want anyway
(b) don’t have the sufficient level of technical knowledge to ask probing enough questions to betray the weaknesses. That is, anything you might ask is either so open-ended that almost anything coherent will look like a valid answer (this is most questions you could ask, outside of seriously technical fields) or has already been asked countless times before and is explicitly part of the training data.
Your understanding of how LLMs work isn’t at all accurate. There’s a valid debate to be had here, but it requires that both sides have a basic understanding of the subject matter.
How is it not accurate? I haven’t said anything about the internal workings of an LLM — just what it able to produce (which is based on observation).
I have more than a basic understanding of the subject matter (neural networks; specifically transformers, etc.). It’s actually not a hugely technical field.
By the way, it appears that you are in category (a).
As the comment I replied to very correctly said, we don’t know how the brain produces cognition. So you certainly cannot discard the hypothesis that it works through “parroting” a weighted average of training data just as LLMs are alleged to do.
Considering that LLMs with a much smaller number of neurons than the brain are in many cases producing human-level output, there is some evidence, if circumstantial, that our brains may be doing something similar.
Perhaps our brains are doing exactly the same, just with more sophistication?