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> This has completely replaced human code review for anything that isn't functional correctness Isn’t functional correctness pretty much the only thing that matters though?
Well no, style is important too for humans when they read a codebase, so the LLMs the parent is running clearly have some value for them. They're not claiming LLMs solved every problem, just that they made life easier by taking care of busywork that humans would otherwise be doing. I think personally this is quite a good use for them - offering suggestions on PRs say, as long as humans still review them as well.
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> there are significant limitations Where can we read about those significant limitations?
Well here's some: Confabulation/Hallucination - https://github.com/lechmazur/confabulations Failure to read context - https://georggrab.net/content/opus46retrieval.html Deleting tests to make them pass - https://www.linkedin.com/posts/jasongorman_and-after-it-did-... Going rogue and deleting data - https://x.com/jasonlk/status/1946069562723897802 Agent security nightmares because they are not in fact intelligent assi…
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>“predict the next token” is not a useful way That is the exact thing to say because that is exactly what it does, despite how it does so. It is not useful to say it if you are an AI-shill though. You bought up AI-hater, so I think I am entitled to bring up AI-shills.
My neurons are also just passing electric signals back and forward and exchanging water and salts with the rest of my body.
"Thinking rocks" vs "thinking meat sacks" isn't much of a distinction really.
Conversely if you approach conversations the same way an LLM does and just repeat what you've heard other people say a lot without actually knowing what it means then you're also likely to be compared to a feathery chatterbox.
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But the programming language has explicitly laid out rules. It was not trained on those sets of rules, but it was trained on many trillions of lines of code. It has a map of how programs work, and an explanation of this new language. It's using training data and data it's fed to generate that result.
What doesn't that explain tho? What behavior would you need to see for that explanation to no longer hold? Because it seems like it explains too much.
IMO, the writer is overzealous with their comments on LLMs. As a coder, it feels like an outsider trying out a product that was amazed me over and over so many times.
> They aren’t perfect, but the kind of analysis the program is able to do is past the point where technology looks like magic.
But as you use this product over a long period of time, there are many obvious gaps - hallucinations / repeated tool calls / out of context outputs / etc.
To me, refine.ink sounds like a company that has built heavy tooling around some super high context window LLMs and then some very good prompts. Their claim is to compare it against any good off-the-shelf LLM with any prompt. But when you are spending bunch of money to build a whole ecosystem around LLMs, it's obvious that it's not going to beat their output.
I won't be surprised if the next version of an LLM within the next few months completely outperforms their output -- that's usually the case with all the coding tools and scaffoldings. They are rendered useless by a superior LLM.Earlier quoted context omitted.
People who produced the works LLMs are trained on are not compensated for the value they are now producing, and their skills are increasingly less valued in a world with LLMs. The value the LLMs are producing is being captured by employees of AI companies who are driving up rent in the Bay Area, and driving up the cost of electricity and water everywhere else. Your surprise to people’s objections makes sense if you c…
> People who produced the works LLMs are trained on are not compensated for the value they are now producing the value being extracted via LLM techniques is new value, which did not previously exist. The producer(s) of the old data had an asking price, which was taken by the LLM trainers. They cannot make the argument that since the LLM is producing new value, they should retroactively update their old asking price f…
Someone else already pointed out that many works used to train LLMs were stolen, but also, it’s unclear whether this is true, either. Can you opt out? Because copyright should have been enough to prevent a company from stealing and profiting from your work, but it wasn’t in the case of every existing LLM.
The whole next word thing is interesting isn't it. I like to see it with Dennett's "Competence and comprehension" lens. You can predict the next word competently with shallow understanding. But you could also do it well with understanding or comprehension of the full picture. A mental model that allows you to predict better. Are the AIs stumbling into these mental models? Seems like it. However, because these are suc…
Given their failure on novel logic problems, generation of meaningless text, tendency to do things like delete tests and incompetence at simple mathematics, it seems very unlikely they have built any sort of world model. It’s remarkable how competent they are given the way they work. Predict the next word is a terrible summary of what these machines do though, they certainly do more than that, but there are significa…
Btw if you read the actual paper that proposes the Turing test, Turing actually rejects the framing of "can machines think"; preferring to go for the more practical "can you tell them apart in practice".
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The distinction you're making reads like substance dualism to me. Are you able to provide a clear and objective metric for assessing "understanding"? If not then you're just handwaving an effectively meaningless semantic distinction.
>objective metric for assessing "understanding" It should involve consciousness. You would not call an AI reacting to red color as "seeing" red. Same thing.
Some people argue that consciousness emerges in early childhood. I can get an infant to understand what I am saying even if they aren’t conscious.