Comment by teiferer
20 hours ago
Human intelligence does not separate training and inference. Both are happening continuously. That's one of the major things the AI community is still completely missing.
20 hours ago
Human intelligence does not separate training and inference. Both are happening continuously. That's one of the major things the AI community is still completely missing.
> Human intelligence does not separate training and inference.
Well, systems governed by LLMs only are said to do that because we only call what happens off-line "training", and online capacity development "in-context learning", while we call online guided learning in humans "training" and what happens to configure them before they come online "evolution" which sets, for instance, "instincts".
IOW, the issue is not because there is not an analogy to the divide you point to in humans, but merely that processes in AI were not named in a way which maps well to what they are analogous to in humans.
But it is true that human intelligence relies much more on in-context learning with only the most basic functions necessary to maintaining what we view as autonomous functions and basic drives really set through "pretraining",
If a new physics break through gets published today, no existing model will be able to fully integrate it - beyond a context window. If I put the paper in my session and it isnt in yours the model knows nothing. It wont retain it past that session.
Models are trained, they do not learn.
I think GP is using a different level of abstraction from yours in their metaphor.
You are saying:
Pre-Training == Everything you store in your memory throughout your life. Model weights == The lessons you learned
Context == whatever you're currently thinking about
One inference run == one thought
They are saying:
Pre-Training == building the DNA template of human brain through millions of years evolution. Model weights == Human DNA
Context == Everything you store in your memory throughout your life, plus whatever you're currently thinking about
One inference run == One human life. One instance == one human
Applying their metaphor, your sentence becomes:
> If a new physics breakthrough gets published today, no existing DNA structure will be able to fully integrate it - beyond an individual person. If I put the paper in my mind by learning it, and it isn't in yours, the DNA of human species stores nothing. It won't retain it past my lifetime.
> The human species is trained (through evolution), it doesn't learn.
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My personal opinion for the last two years or so has been that current AI agents are forever going to be highly limited so long as they don’t possess a real “memory” process. Right now they just have absurdly big working memories, and a few hacky ways of making the equivalent of Post-It notes to future iterations, but no true integration of memory into a new future self. Meaning their “learning” is fundamentally kneecapped to one specific and imperfect modality.
Their memory lasts their entire life, they just have really short lives.
You mean simultaneously, and of course they are separate in humans, just not temporally. The models are learning continuously, the problem is that this process is fragile and has to be carefully curated, that's why it's separated in time from the inference.
That's one of the major things the AI community is still completely missing.
That isn't true. It's not continuous like in humans, but it's clear that models are using prompts, feedback, etc to improve. They're learning from the signals we give them between versions.