Tech experts are starting to doubt that ChatGPT and A.I. ‘hallucinations’ will ever go away: ‘This isn’t fixable’

"AI" are just advanced versions of the next word function on your smartphone keyboard, and people expect coherent outputs from them smh

Yet I've still seen many people clamoring that we won't have jobs in a few years. People SEVERELY overestimate the ability of all things AI. From self driving, to taking jobs, this stuff is not going to take over the world anytime soon

In my limited experience the issue is often that the "chatbot" doesn't even check what it says now against what it said a few paragraphs above. It contradicts itself in very obvious ways. Shouldn't a different algorithm that adds a some sort of separate logic check be able to help tremendously? Or a check to ensure recipes are edible (for this specific application)? A bit like those physics informed NN.

People make a big deal out of this but they forget humans will make shit up all the time.

This is trivially fixable. As is jailbreaking.

It's just that everyone is somehow still focused on trying to fix it in a single monolith model as opposed to in multiple passes of different models.

This is especially easy for jailbreaking, but for hallucinations, just run it past a fact checking discriminator hooked up to a vector db search index service (which sounds like a perfect fit for one of the players currently lagging in the SotA models), adding that as context with the original prompt and response to a revisionist generative model that adjusts the response to be in keeping with reality.

The human brain isn't a monolith model, but interlinked specialized structures that delegate and share information according to each specialty.

AGI isn't going to be a single model, and the faster the industry adjusts towards a focus on infrastructure of multiple models rather than trying to build a do everything single model, the faster we'll get to a better AI landscape.

But as can be seen with OpenAI gating and depreciating their pretrained models and only opening up access to fine tuned chat models, even the biggest player in the space seems to misunderstand what's needed for the broader market to collaboratively build towards the future here.

Which ultimately may be a good thing as it creates greater opportunity for Llama 2 derivatives to capture market share in these kinds of specialized roles built on top of foundational models.

We're likely already (or soon) hit a peak with current AI approach. Unless another breakthrough happen in AI research, ChatGPT probably won't improve much in the future. It might even regress due to OpenAI's effort to reduce computational cost and making their AI "safe" enough for general population.

the models are also getting larger (and require even more insane amounts of resources to train) far faster than they are getting better.

I was excited for the recent advancements in AI, but seems the area has hit another wall. Seems it is best to be used for automating very simple tasks, or at best used as a guiding tool for professionals (ie, medicine, SWE, …)

Not with our current tech. We'll need some breakthroughs, but I feel like it's certainly possible.

Mean while every one is terrified that chatgpt is going to take their job. Ya we are a looooooooooong way off from that.

I don't understand why they don't use a second model to detect falsehoods instead of trying to fix it in the original LLM?

Disclaimer: I am not an AI researcher and just have an interest in AI. Everything I say is probably jibberish, and just my amateur understanding of the AI models used today.

It seems these LLM's use a clever trick in probability to give words meaning via statistic probabilities on their usage. So any result is just a statistical chance that those words will work well with each other. The number of indexes used to index "tokens" (in this case words), along with the number of layers in the AI model used to correlate usage of these tokens, seems to drastically increase the "intelligence" of these responses. This doesn't seem able to overcome unknown circumstances, but does what AI does and relies on probability to answer the question. So in those cases, the next closest thing from the training data is substituted and considered "good enough". I would think some confidence variable is what is truly needed for the current LLMs, as they seem capable of giving meaningful responses but give a "hallucinated" response when not enough data is available to answer the question.

Overall, I would guess this is a limitation in the LLMs ability to map words to meaning. Imagine reading everything ever written, you'd probably be able to make intelligent responses to most questions. Now imagine you were asked something that you never read, but were expected to respond with an answer. This is what I personally feel these "hallucinations" are, or imo best approximations of the LLMs are. You can only answer what you know reliably, otherwise you are just guessing.

As long as you can't describe an objective loss function, it will never stop "hallucinating". Loss scores are necessary to get predicable outputs.

The way that one learns which of one's beliefs are "hallucinations" is to test them against reality — which is one thing that an LLM simply cannot do.

Correct, it's not. It could be reduced but it will never go away.

Hers, try this mushroom and Ayahuasca smoothie.

The way to solve this is still largely through more focus on the provided context as the space of "facts" from which to operate. This combined with well thought out domain-specific context engines should still get the average user an absolutely enormous amount of utility. All that said I am not sure if OpenAI's business model will get us that sort of application of the technology. I am looking forward to improvements in the open source space as I think advancement there is necessary for further development of the technology.

I don't think thats the case. If I understand correctly, the current issue is processing power, they can only load so much data before response time goes to absolute shit. I would think that layering different AI logic checks to verify statements made, recall previous conversations, and other mental processes that humans do automatically, would correct this issue. But with current technology its not even an option. My theory is that once quantum computers are actually finally realized and economically feasible, developers will be able to overcome the response time hurdle and all of the layered logic checks will be able to run simultaneously and instantly. My personal opinion is that I think the eventual layering of numerous AI models to overlap, check, and recheck one another, will be what brings on the emergence of what could be considered actual AI consciousness.