My favorite explanation for what consciousness is one I read in a Thousand Brains, I found it quite elegant. It posited that consciousness is a natural derivation of embodiment + memory + the ability to create reference frames (which the book lays forth as the fundamental basis by which our brains work). Essentially, the idea is that just as we create reference frames to understand the world around us, because of memory, we begin to develop one for ourselves as well. Because of this, without a more integrated memory (built into weights), it seems unlikely that LLMs might "gain" consciousness.

Consciousness is moment to moment and fleeting. There are people with brain defects that don’t let them form new memories. They have no memory about what happened a minute ago in their own consciousness. Still we would say that they are conscious, even if it’s only momentary. LLMs could conceivably have something like that within their CoT/MoE loops.

how would you build memory into the weights? and why is RAG not enough? Our hippocampus is at a bit of a distance from our frontal cortex.

Yeah it's a good question, I've also been thinking about harnesses and all these tacked on things we've done to add persistent memory, what makes that different, I don't know the answer, I guess that still 'feels' different than what we have, but it's hard to articulate how. As for the memory into weights thing, I meant along the lines of the Google TITANS/MIRAS papers that were released I think late last year.

You should try the Arrow SVG model by Quiver, should be much better at that sort of thing since it's made for that.

Idk if you've seen this already but Taalas does this interesting thing where they embed the model directly onto the chip, this leads to super-fast speeds (https://chatjimmy.ai) but the model they're using is an old small Llama model so the quality is pretty bad. But they say that it can scale, so if that's really true that'd be pretty insane and unlock the inference you're talking about.

Robotics/control systems is exactly what came to mind when I saw this release! What struck me is the possibility of look ahead search in real time, a bit like alphazero's mcts.

It's a fascinating proposition and no doubt they'll get bigger models in there, and likely be able to cluster multiple models for mega MOE. One thing that would really be great is if they could take the power requirements down -- the chip requires 2.5KW, which is modest in terms of what the big boys use but would be an issue on a battery powered robot.

"The chip" no, a whole rack/deployment they offer takes 2.5kW. Not just one chip. Squeezing 2.5kW thru 1 chip would be mental.

My bad, thanks for the correction. Skimming FTL.

Pangram is probably the best known example of a detector with low false positives, they have a research paper here: https://arxiv.org/pdf/2402.14873. They do have an API but not sure if you need to request access for it.

For humans I think it just comes down to interacting with LLMs enough to realize their quirks, but that's not really fool-proof.

Pangram has time after time been shown as the only detector that mostly works. And that paper is pretty old now! There are recent papers from academics independently bench-marking and studying detectors e.g. https://arxiv.org/abs/2501.15654

This comment seems ai-written

Some good examples there. The octopus one is at an angle - can't really call that one pass (unless the goal is "VISIBLE" tentacles).

Other than the five-leaf clover, most of the images (dog, spider, person's hands) all required a human in the loop to invoke the "Image-to-Image" capabilities of NB Pro after it got them wrong. That's a bit different since you're actively correcting them.