Tech experts are starting to doubt that ChatGPT and A.I. ‘hallucinations’ will ever go away: ‘This isn’t fixable’::Experts are starting to doubt it, and even OpenAI CEO Sam Altman is a bit stumped.
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.
Mean while every one is terrified that chatgpt is going to take their job. Ya we are a looooooooooong way off from that.
I’ve already seen many commercials using what is clearly AI generated art and voices (so not specifically ChatGPT). That is a job lost for a designer and an actor somewhere.
Not necessarily, in my work we made some videos using ai generated voices because it’s availability for use made the production of the videos cheap and easy.
Otherwise we just wouldn’t have made the videos at all because hiring someone to voice them would have been expensive.
Before AI there was no job, after AI there was more options to create things.
I mean that’s capitalism step 1. A new thing comes around and is able to generate more income through giving actual value. But soon it will hit step 2 aka profits can only be increased by reducing costs. Then it’s all the jobs going to ai
“AI” are just advanced versions of the next word function on your smartphone keyboard, and people expect coherent outputs from them smh
It is just that everyone now refers to LLMs when talking about AI even though it has sonmany different aspects to it. Maybe at some point there is an AI that actually understands the concepts and meanings of things. But that is not learned by unsupervised web crawling.
On a sidenote, I can’t believe how bad the predictions from smartphone keyboards still are. I would love to have one that actually understands sentence structure and context.
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?
That model could also hallucinate and claim something is false that is actually correct.
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, …)
Hallucinations is common for humans as well. It’s just people who believe they know stuff they really don’t know.
We have alternative safeguards in place. It’s true however that current llm generation has its limitations
Sure, but these things exists as fancy story tellers. They understand language patterns well enough to write convincing language, but they don’t understand what they’re saying at all.
The metaphorical human equivalent would be having someone write a song in a foreign language they barely understand. You can get something that sure sounds convincing, sounds good even, but to someone who actually speaks Spanish it’s nonsense.
GPT can write and edit code that works. It simply can’t be true that it’s solely doing language patterns with no semantic understanding.
To fix your analogy: the Spanish speaker will happily sing along. They may notice the occasional odd turn of phrase, but the song as a whole is perfectly understandable.
Edit: GPT can literally write songs that make sense. Even in Spanish. A metaphor aiming to elucidate a deficiency probably shouldn’t use an example that the system is actually quite proficient at.
Because it can look up code for this specific problem in its enormous training data? It doesnt need to understand the concepts behind it as long as the problem is specific enough to have been solved already.
the models are also getting larger (and require even more insane amounts of resources to train) far faster than they are getting better.
But bigger models have new “emergent” capabilities. I heard that from a certain size they start to know what they know and hallucinate less.
Wow you heard that crazy bro
One of the papers about it https://arxiv.org/pdf/2206.07682.pdf
Not with our current tech. We’ll need some breakthroughs, but I feel like it’s certainly possible.
You can potentially solve this problem outside of the network, even if you can’t solve it within the network. I consider accuracy to be outside the scope of LLMs, and that’s fine since accuracy is not part of language in the first place. (You may have noticed that humans lie with language rather often, too.)
Most of what we’ve seen so far are bare-bones implementations of LLMs. ChatGPT doesn’t integrate with any kind of knowledge database at all (only what it has internalized from its training set, which is almost accidental). Bing will feed in a couple web search results, but a few minutes of playing with it is enough to prove how minimal that integration is. Bard is no better.
The real potential of LLMs is not as a complete product; it is as a foundational part of more advanced programs, akin to regular expressions or SQL queries. Many LLM projects explicitly state that they are “foundational”.
All the effort is spent training the network because that’s what’s new and sexy. Very little effort has been spent on the ho-hum task of building useful tools with those networks. The out-of-network parts of Bing and Bard could’ve been slapped together by anyone with a little shell scripting experience. They are primitive. The only impressive part is the LLM.
The words feel strange coming off my keyboard, but…Microsoft has the right idea with the AI integrations they’re rolling into Office.
The potential for LLMs is so much greater than what is currently available for use, even if they can’t solve any of the existing problems in the networks themselves. You could build an automated fact-checker using LLMs, but the LLM itself is not a fact-checker. It’s coming, no doubt about it.
Honestly, the integration into office is an excellent idea. I’ve been using chatgpt to work on documents, letting it write entirely new sections for me based on my loose notes and existing text. Which for now I have to either paste in or feed as a pdf through a plugin. But the 25USD I paid I literally earned in a single day through the time saved vs the hours I was justified to bill.
Once I have that integrated into word directly it’ll be huge.
People also seem to expect llms to just do all the work. But that’s not my experience, for generative text anyway. You have to have a solid idea of what you want and how you want it. But the time the llm saves on formulation and organisation of your thoughts is incredible.
