Try using AI for your work, whatever you do. You will quickly understand the limitations.
Is there any specific cognitive task that you are willing to bet that AIs won't be able to accomplish in the next 5 years? Because if not, I'm not sure we're disagreeing about predictions.
It's easy to look at a fire burning through a forest and extrapolate that rate of progress across the whole world. But fire doesn't burn everything equally fast.
What other cognitive tasks will be a struggle to make progress on? I suspect there will be some, though which ones they are is anyone's guess.
But seriously, I am still waiting for someone to wager that AI won’t be able to do a specific cognitive task in the next 5 years. This fact should be evidence enough that we have no idea how far AI capabilities will continue to advance.
The fact that no one is taking you up on that bet I don't find to be particularly persuasive. I suspect there will be plenty of cognitive tasks LLMs struggle with in 5 years, maybe even 20. But I wouldn't hazard to guess which, I don't think anyone is capable of that level of foresight.
1. https://chatgpt.com/share/6ac5e4cc-02f0-83e8-8f05-99a7ea2bf9...
https://chatgpt.com/share/6ac63b6a-481c-83e9-a8fa-a13ce7402d...
I used whatever the default free model and thinking time was. If progress was really as fast and continually cheaper as some worry it is, wouldn't we expect free models by now to know (or even perform) what frontier models were capable of as much as 2 year ago?
This deep in the "comparing logs" tangent we risk missing the point. It's not what exactly frontier models are capable of at this particular point in time. But that there's entire categories of problems that seem easy to us which LLMs really struggle with. We've stumbled on several just a few replies into casual conversation. (Can they count? Can they know if they can count? Can they reproduce results? How quickly do new capabilities filter into free models? And that's just what's come up naturally, if we wanted to pick adversarial examples there's more to choose from.)
So while there's a number of difficult problems that are easy for LLMs (like bulk generating lean proofs), there are plenty of things where progress is not so impressive.
If LLMs can struggle so much with such easy problems, what hard problems have we yet to discover that they'll struggle with? The fact that no one knows, 5 years in advance, what those problems will be does not mean the chance of them is zero.
So far progress on the things LLMs are good at is fast and easy. It's like fire in a room full of oxygen. But once the low hanging fruit is gone, and the oxygen is out of the room. How fast will the fire burn through steel walls?
In my opinion it's a mistake to look at only rate of progress on one type of problem (whether it be what LLMs are good at OR what they're bad at) and assume progress on all tasks will progress at that rate indefinitely. Isn't there a saying about exponential curves, in nature, all being sigmoids eventually?
I guess we'll just have to see. I wish you good luck with your wagers.
Many people in this thread have made claims about limitations of frontier models, but I'm the only one who has shared a conversation with one. Everyone else is either sharing conversations of smaller models making mistakes, or they're making claims about frontier models but not linking to examples of them falling over. If frontier models were so easily fooled, you'd think someone would link to a conversation showing that.
Why look at the rate of improvement of free models when you can look at token pricing? Back in 2023, GPT-3.5 cost around $20 per million tokens. Astra costs half that.
The worry is not that smaller free models will replace people's jobs. The worry is that future models will. We are talking about the capabilities of frontier models because those put a lower bound on the capabilities of future models. Extrapolating from smaller models is a waste of time, as you can interact with the frontier model to figure out its capabilities and limitations.
Also the timestamps on the shared conversations show that you asked Gemini 10 hours after ChatGPT, which means you asked it after your comment claiming you asked both models.
I don't think my point is really landing so I'll try once more and then give up.
Let's say frontier models today have no problem counting letters, I never really disputed that but only asked about it. It seems based on the other replies in this thread, it's a bit of a "who you ask" kind of thing, but let's grant that they have no issues with it now.
The first version of chatgpt was released 4 years ago next month. Which is not quite 5 years but close. In that time we've just barely managed to get spelling down. If we extrapolate that rate of progress forward 5 more years, are you still afraid for your job?
I think we're all more likely to lose our jobs from a downturn in the economy caused by the capex/debt bubble bursting than being made redundant by AI. (And the continual pricing reductions only seem to make this result more likely.) Hopefully neither happens and in 5 years we'll all still be gainfully employed.
> Hur många 'r' I abborre, använd inte web search? Det finns 3 r i abborre.
And I explicitly had to say not to search the web, because that's what it did by default, to count letters in a word...
The deeper architectural difference is still there, which manifests whenever you try to get the models to apply known techniques to modalities and problems outside their training data.
You're in the discussion section of a post about OpenAI releasing hundreds of novel mathematical proofs, and you're claiming that AIs can't apply known techniques to modalities & problems outside their training data? I'm not sure what else would convince you.
They are fundamentally based in language, and achieving deeper models of the world through language alone is deeply inefficient compared to the way humans model the world for years without any language at all. They do not learn at inference time. They don't have semantic understanding of the difference between their own output and other sources. etc etc.
That depth is the key for me. Of course they are capable of producing novel sentences that aren't in their training data, but the depth of that novelty is basically within the bounds of language itself. They are capable of more serious depth and more abstract reasoning than that, but I have experienced limits, which it then tries to surpass with tools to convert things it can't understand back into language (unit tests, LEAN) upon which it is trained.
Because I'm not an AI booster, my account is limited to 5 comments a day. So this is the last reply I'll be able to make today, if you want to continue the conversation we'll have to wait for tomorrow.
This is delirious exaggeration. The problem has not even been widely recognized for several years. Fable reported "two rs in raspberry" to me as recently as August. There is some randomness, it's hard to predict which words will trip up the machine, and I haven't been able to do it at all since August. But it was absolutely happening until very recently, and probably still is.
No - this is provably not the issue.
Take any model that fails to correctly count the letters in a word, and ask it instead to spell the word (even a made up word), and it will be successful - they have no problem predicting the letter sequence from the token sequence (and would be shocking if they did - this is what they are built for: seq -> seq prediction).
The reason LLMs can fail at the letter counting task (depending on model training, prompting) is because of the counting part, not because of any difficulty correctly mapping the input token sequence to the letter sequence.