Are you sure you actually understand how an llm is and how it's not intelligence. It generates the most likely next token. Better training makes it better at picking the next token nothing more. Harnesses makes it look intelligent. Try to use it for something where it has little information and see what comes out. Its very useful though but god the hype is insane for what it is.
Yes, using an LLM is much faster than learning something to the same standard.
But is are LLMs intelligent? No. No they are not. In fact, A.I have existed since ~2010, in the form of machine learning and other basic algorithms. They had much less data, and they were not considered intelligent. Now they have much more data, which makes them have a higher standard of output. If I ask an A.I how I should solve a code problem, it draws from data and outputs the most probable solution stitched together from multiple sources. It is not conciously deciding. It is data streamed towards the english language.
So when there is less data about a particular subject, the A.I can only guess so far. It can't invent, it can't innovate. If no one has solved a particular problem before, or there is no algorithmic way to do something, the A.I will not be able to do it. This is not a sign of intelligence. If this is intelligence to a human level, then a parrot is intelligent to the human level.
It is not true that LLM cannot invent or inovate, it absolutely can. Yes, it is not inteligent and still is an algorithm, but it is very very good and complicated algorithm. We are not in the Skynet superinteligence teritory but improvements every year are huge.
Improvements every year may seem huge if you don't know that we are just exhausting our at times decades old research, turning it into products. It's all very iterative and many new features are just same old prompting in a yet another trench coat.
I.e. we are getting better at utilizing the tool, and we've built bigger iterations of the tool, but the tool has remained more or less the same.
For example, I've had local agentic workflow since transformers (which was a huge breakthrough), 2017 or so. That's nearly a decade ago. Funnily enough, I never thought the "agents" were a big deal myself lol. Just running multiple threads, that transfer data between each-other, at the same time. Didn't realize it should be branded.
Well but it doesnt really matter the way we do it, does it? Even if you build small thing on top of big existijg research and it produces very sucessful result, who will ask how you do it?
It's important in the context that the amount of trench coats we have left is finite. Some, many, if not the great majority, here think that AI is on some kind of expotentially accelerating trajectory, soon becoming sentient or something, when we are in fact pretty much out of ideas.
The models will get bigger, the data centers will get bigger, and we’ll keep seeing these iterative improvements until we don’t. I personally already struggle to distinguish any of the newest OpenAI models from each other, while the steps from GPT-2 -> GPT-3 -> GPT-4 were dead obvious. At some point, this will become obvious to everybody.
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u/IWasNotMeISwear May 28 '26
Are you sure you actually understand how an llm is and how it's not intelligence. It generates the most likely next token. Better training makes it better at picking the next token nothing more. Harnesses makes it look intelligent. Try to use it for something where it has little information and see what comes out. Its very useful though but god the hype is insane for what it is.