rar00
Joined 1 year ago
Joined 1 year ago
> “It’s counterintuitive,” Miller said. “You’d think neurons that signal the wrong pathway would go away with learning.”
Except they don't tweak weights when the model is incorrect, I'm puzzled why he's making that claim?? In equation 32 they show weights are adjusted in the form `dW = K * S * (Wmax - W) * F(A)`, where F(A) is the feedback given the chosen action A (i.e. reward). K is positive and S non-negative, so Weights can be nudged towards a maximal absolute value Wmax in the direction of F(A).
However, they set `F(A) = {1 if A is correct and 0 when incorrect}`. That means weights don't change when the model is wrong and can only be potentiated (`sign(Dw) == sign(F(A)) == +`)
Have you performed any basic evaluation / test of your approach?
I'm also curious if there was any deliberation between pursuing "thinking" (language modality) versus "behaving" (visual modality)?
On the one hand, the approach overlaps a lot with my thinking, and has some original tweaks (like the emotionally valenced reward signals). Saying that as someone from a robotics/AI background nowadays involved in GenAI, with a few years of phd research on NeuroAI, curious about molecular neuroscience and the Free Energy Principle (as conceptualised by Karl Friston and Mark Solms).
On the other:
- this plausibility dilemma is the hallmark of LLMs
- has all the buzzwords imaginable
- no code, no raw outputs, no official confirmation (by ARC)
- Agentic AI play, walled demo page
I might just be too hopeful (and gullible)...
Seems the article is a contemporary example of the first->second-order realisation...
“This was a very unexpected finding given the current assumptions about how psychedelic medicine works”
"Surprisingly, psychedelic treatment was still able to strongly boost connectivity onto these neurons”
Knowing (those types of) psychedelics bind to serotonin receptors scientists studied neurons with such receptors and didn't focus on the others. Their study looked at other neurons and found plasticity changes there too.
expected, yet still funny. Noting that their initial aim was to capitalise on transformers to create business tools after GPT-3 came out.
After getting backed and receiving adequate funding, all that matters is maintaining a good growth rate to remain a purpose-driven business.