Comment on Vibe coding service Replit deleted production database
hisao@ani.social 2 days agoAre you aware of generalization and it being able to infer things and work with facts in highly abstract way? Might not necessarily be judgement, but definitely more than just completion. If a model is capable of only completion (ie suggesting only the exact text strings present in its training set), it means it suffers from heavy underfitting in AI terms.
ChairmanMeow@programming.dev 2 days ago
Completion is not the same as only returning the exact strings in its training set.
LLMs don’t really seem to display true inference or abstract thought, even when it seems that way. A recent Apple paper demonstrated this quite clearly.
hisao@ani.social 2 days ago
Coming up with even more vague terms to try to downplay it is missing the point. The point is simple: it’s able to solve complex problems and do very impressive things that even human struggle to, in very short time. It doesn’t really matter what we consider true abstract thought of true inference. If that is something humans do, then what it does might very well be more powerful than true abstract thought, because it’s able to solve more complex problems and perform more complex pattern matching.
Jhex@lemmy.world 2 days ago
You mean like a calculator does?
hisao@ani.social 2 days ago
Yeah, this is correct analogy, but much more complex problems than calculator. How much it is similar or not to humans way of thinking is completely irrelevant. And how much exact human type of thinking is necessary for any kind of problem solving or work is not something that we can really calculate. Considering that scientific breakthroughs, engineering innovations, medical stuff, complex math problems, programming, etc, do necessarily need human thinking or benefit from it as opposed to super advanced statistical meta-patterning calculator is wishful thinking. It is not based on any real knowledge we have. If you think it is wrong to give it our problems to solve, to give it our work, then it’s a very understandable argument, but you should say exactly that. Instead this AI-hate hivemind tries to downplay it using dismissive braindead generic phrases like “NoPe ItS nOt ReAlLy UnDeRsTaNdInG aNyThInG”. Okay, who tf asked? It solves the problem. People keep using it and become overpowered because of it. What is the benefit of trying to downplay its power like that? You’re not really fighting it this way if you wanted to fight it.
ChairmanMeow@programming.dev 2 days ago
Well the thing is, LLMs don’t seem to really “solve” complex problems. They remember solutions they’ve seen before.
The example I saw was asking an LLM to solve “Towers of Hanoi” with 100 disks. This is a common recursive programming problem, takes quite a while for a human to write the answer to. The LLM manages this easily. But when asked to solve the same problem with with say 79 disks, or 41 disks, or some other oddball number, the LLM fails to solve the problem, despite it being simpler(!).
It can do pattern matching and provide solutions, but it’s not able to come up with truly new solutions. It does not “think” in that way. LLMs are amazing data storage formats, but they’re not truly ‘intelligent’ in the way most people think.
hisao@ani.social 2 days ago
This only proves some of them can’t solve all complex problems. I’m only claiming some of them can solve some complex problems. Not only by remembering exact solutions, but by remembering steps and actions used in building those solutions, generalizing, and transferring them to new problems. Anyone who tries using it for programming, will discover this very fast.
PS: Some of them were already used to solve problems and find patterns in data humans weren’t able to get other ways before (particle research in CERN, bioinformatics, etc).