91–100 of 276 posts
Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
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> I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. We keep assigning adjectives to this technology that anthropomorphize the neat tricks we've invented. There's nothing "omniscient" or "dim-witted" about these tools. They have no wit. They do not think or reason. All Large "Reasoning" Models do is generate data that they use as context to gen…
>There's nothing "omniscient" or "dim-witted" about these tools I disagree in that that seems quite a good way of describing them. All language is a bit inexact. Also I don't buy we are no closer to AI than ten years ago - there seem lots going on. Just because LLMs are limited doesn't mean we can't find or add other algorithms - I mean look at alphaevolve for example https://www.technologyreview.com/2025/05/14/11164…
The fact that this technology can be very useful doesn't imply that it's intelligent. My argument is about the language used to describe it, not about its abilities.
The breakthroughs we've had is because there is a lot of utility from finding patterns in data which humans aren't very good at. Many of our problems can be boiled down to this task. So when we have vast amounts of data and compute at our disposal, we can be easily impressed by results that seem impossible for humans.
But this is not intelligence. The machine has no semantic understanding of what the data represents. The algorithm is optimized for generating specific permutations of tokens that match something it previously saw and was rewarded for. Again, very useful, but there's no thinking or reasoning there. The model doesn't have an understanding of why the wolf can't be close to the goat, or how a cabbage tastes. It's trained on enough data and algorithmic tricks that its responses can fool us into thinking it does, but this is just an illusion of intelligence. This is why we need to constantly feed it more tricks so that it doesn't fumble with basic questions like how many "R"s are in "strawberry", or that it doesn't generate racially diverse but historically inaccurate images.
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They're similar to self-driving vehicles. Both are around the corner, but neither can negotiate the turn.
I saw your comment and counted — in May I took a Waymo thirty times.
However, Waymo is Deep Blue of self-driving cars. Doing very well in a closed space. As a result of this geofencing, they have effectively exhausted their search space, hence they work well as a consequence of lack of surprises.
AI works well when search space is limited, but General AI in any category needs to handle a vastly larger search space, and they fall flat.
At the end of the day, AI is informed search. They get inputs, and generate a suitable output as deemed by their trainers.
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>They have no wit. They do not think or reason. Computers can't think and submarines can't swim.
But if you need a submarine that can swim as agiley as a fish then we still aren't there yet, fish are far superior to submarines in many ways. So submarines might be faster than fish, but there are so many maneuvers that fish can do that the submarine can't. Its the same with here with thinking. So just like computers are better at humans at multiplying numbers, there are still many things we need human intelligence…
So if an LLM generates working code, correct translations, valid points relating to complex matters and so on it doesn't matter if it does so by thinking or by some other mechanism.
I think that's an interesting point.
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They're similar to self-driving vehicles. Both are around the corner, but neither can negotiate the turn.
Waymo's pretty good at unprotected lefts
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As a test, I asked Gemini 2.5 Flash and Gemini 2.5 Pro to decode a single BASE64 string. Flash answered correctly in ~2 seconds, at most. Pro answered very wrongly after thinking and elaborating for ~5 minutes. Flash was also giving a wrong answer for the same string in the past, but it improved. Prompt was the same: "Hey, can you decode $BASE64_string?" I have no further comments.
well that's not a very convincing argument. That's just a failure to recognize when the use of a tool- base64 decoder- is needed, not a reasoning problem at all, right?
All the environments the test (Tower of Hanoi, Checkers Jumping, River Crossing, Block World) could easily be solved perfectly by any of the LLMs if the authors had allowed it to write code. I don't really see how this is different from "LLMs can't multiply 20 digit numbers"--which btw, most humans can't either. I tried it once (using pen and paper) and consistently made errors somewhere.
> I don't really see how this is different from "LLMs can't multiply 20 digit numbers"--which btw, most humans can't either. I tried it once (using pen and paper) and consistently made errors somewhere. People made missiles and precise engineering like jet aircraft before we had computers, humans can do all of those things reliably just by spending more time thinking about it, inventing better strategies and using mo…
With thinking LLMs, they can think, but they often can only think in one big batch before starting to "speak" their true answer. I think that needs to be rectified so they can switch between the two. In my previous framework, I would say "would I be able to solve this if had all the knowledge, but could only think then start typing?".
I think for larger problems, the answer to this is no. I would need paper/a whiteboard. That's what would let me think, write, output, iterate, draft, iterate. And I think that's where agentic AI seems to be heading.
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> I don't really see how this is different from "LLMs can't multiply 20 digit numbers"--which btw, most humans can't either. I tried it once (using pen and paper) and consistently made errors somewhere. People made missiles and precise engineering like jet aircraft before we had computers, humans can do all of those things reliably just by spending more time thinking about it, inventing better strategies and using mo…
No. a huge population of humans did while standing on the shoulders of giants.
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> I don't really see how this is different from "LLMs can't multiply 20 digit numbers"--which btw, most humans can't either. I tried it once (using pen and paper) and consistently made errors somewhere. People made missiles and precise engineering like jet aircraft before we had computers, humans can do all of those things reliably just by spending more time thinking about it, inventing better strategies and using mo…
Some specialized people could probably do 20x20, but I'd still expect them to make a mistake at 100x100. The level we needed for space crafts was much less than that, and we had many levels of checks to help catch errors afterwards. I'd wager that 95% of humans wouldn't be able to do 10x10 multiplication without errors, even if we paid them $100 to get it right. There's a reason we had to invent lots of machines to h…
The reason was efficiency, not that we couldn't do it. If a machine can do it then we don't need expensive humans to do it, so human time can be used more effectively.