AI models don't hallucinate 

That's an interesting question. Many autistic people suffer from sensory overload, so in that aspect it's a little like hallucination; I believe they benefit from a highly structured routine and environment (but I'm not an expert there). With memory problem, that's confabulation so the key to combatting it is increasing information density. LLMs do this via Retrieval Augmented Generation (RAG), supplying pertinent information to the model. For humans, the equivalent would be a caregiver helping to prompt with relevant memory cues. In the Amazon series Humans, there's an aging robotics scientist who suffers from memory issues; his wife has passed on and he keeps his synthetic (android) Odie, who is well past recycle date, because Odie remembers all the shared experiences with his wife and helps act as an artificial memory system, prompting him by telling him stories so he doesn't forget her.

@@davidrostcheck Great comment. That's what I was or am hoping self-hosted AI would be for me. A way to help me remember my experiences & understand the world around me and be a kind of truth North Star and analogue to a benevolent caregiver/guard/parent/friend/etc. To me truth (or accuracy of my internal world model) is more and more important as I grow older. I also wanted to say that your communication about "hallucinate" versus "confabulate" is one of those truths that really should matter but society will "lie" or "misinform" about. I wish ultimate truth or accuracy would be more valued.

I used riverside.fm

Yes, the ship has sailed. But I think, like electrical engineering defining the current as the opposite fo the actual flow or computer scientists and cognitive scientists using 'schema' differently, it's going to remain a point of friction for students in future years. ¯\_(ツ)_/¯

@@davidrostcheck They'll get over it. What does log mean to you, eh?

What papers did you write?

@@CheapSushi lol, I'm not going to dox myself

If I understand the question correctly (let me know if not), for an actual visual hallucination, yes, you'd need a visual layer, a source of random noise merged with the visual layer, and a way to control the thresholding. Interestingly enough: that's the basic architecture of a diffusion model, the AI models we use for creating visual imagery. And if the noise vs. information density gets high, you get some trippy and often creepy hallucinations from them.

The problem is for practitioners in the AI field. As we build cognitive entities, they ways that we interact with them come more and more from cognitive science. If we understand that a model is confabulating, we can do things about it. All those techniques come from cognitive science, so there's a limit to how good you can be at AI without learning cog-sci, and the misaligned terms cause confusion there.

@@davidrostcheck it's a good point, but I get the feeling that anyone this would apply to would not be confused by calling it a hallucination either.

I think it's common across many sciences that researchers use terms from another field without a full understanding. Many AI researchers don't know cognitive science well. This is a handicap since many techniques, such as model prompting, are now really directly taken from their human cog-sci equivalents. But LLM models do produce consistent output, provided you set their temperature (creativity) to 0.

They're again a sensory thresholding problem. For example, if you put someone in a sensory deprivation tank, they get no visual stimuli so their visual system will progressively lower the noise threshold, trying to increase sensitivity, until it starts perceiving the noise in the visual system (from random firings, pulse pressure causing pressure waves through the eye, etc) as objects.

@@davidrostcheck What about those caused by mental disorders?