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| ▲ | vanilla_nut 3 hours ago | parent | next [-] | | Quite right. I'm worried about the impact that LLMs will have on the learning process, especially in programming, but also in writing. Programming and writing are both skills that seem simple, but take an absolutely staggering amount of practice to master. Think about how much your own writing (and programming, if you were lucky enough to start early) evolved from, say, age 12 (when a lot of smart kids start to tackle 'real' books) to age 18 (when you supposedly have a good enough education for 50% of work in most countries) to age 25. All of that evolution is a direct result of one thing: practice! But with a magic answer box available in everyone's pocket, it'll take truly Herculean effort from a learner to actually grind through the practice instead of just cheating for an answer. I really worry how much an LLM user will actually comprehend their own code or even prose; if you've scarcely written a line of code, how can you really understand what's going on in a debugger? If you haven't done the legwork of writing essays and constructing coherent arguments and comprehending grammar, how will you ever communicate effectively? Maybe I'm just a dinosaur and these kids will sail a whole level of abstraction above my own understanding of writing and programming, much like how my own generation preferred Python to C, and how the previous generation evolved from assembly to C/BASIC/etc. But then I come back to those missing fundamentals, that empty mental model. It's not like my English or CS teachers had me grind through essays and implementing linked lists and Djikstra's Algorithm for pure busywork. They did it because practice is the only way to truly immerse a student in a practical subject. Maybe it'll work for programming, as long as LLMs get good enough that you can always ask them to fix low-level errors for you? But it seems unlikely to work in prose. And even those generational programming jumps I mentioned (assembly to C to Python) were lossy; most kids I went to school with would be absolutely useless writing C code, and even as a bit of a dinosaur I'm pretty awful at even debugging assembly. Like you said: you still need to learn grammar and spelling. And I suspect a whole skill tree of other fundamentals! | | |
| ▲ | mancerayder 3 hours ago | parent [-] | | One angle I'm exploring, as a non-dev who nonetheless works in tech, is using Claude as a professor. Make learning timelines for me for Leetcode, break it down in phases, start with theory, ask me questions, then give me a coding challenge. Save that to an html artifact I can export and read on my phone. It still gets things wrong, I can tell as I get through problems. But it was either that or that dreary 'Cracking the Coding Interview' book. At least I'm learning fundamentals by asking question after question and making it track the concepts I had trouble with. That's one use. Will most people use it to learn? Probably not. But most people are ... most people. | | |
| ▲ | disgruntledphd2 33 minutes ago | parent [-] | | Yup, I used to believe that people would all use the Internet to educate themselves, and we all know how that turned out (loads of people did, but the majority didn't). |
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| ▲ | holtkam2 3 hours ago | parent | prev [-] | | The way I think of it has evolved a lot over the last 5 years. At this point I think human brains probably do something analogous to next token prediction when we think. For all the hype, I think LLMs are actually more, not less, intelligent than that average person realizes. I think it’s legit, actual intelligence, not just “artificial” intelligence. That may be a hot take but it’s just my perception. | | |
| ▲ | HarHarVeryFunny 2 hours ago | parent | next [-] | | > At this point I think human brains probably do something analogous to next token prediction when we think That's reasonable, but it doesn't mean that LLMs are close to being brains. For a start, when humans think/talk, we often think ABOUT something - whatever is swirling about in our mind, or what we are currently seeing/feeling/etc. An LLM generating tokens/words is doing so only based on it's weights and the word sequence it is currently generating ... the human parallel would be more like a rapper spitting out words based on prior words, essentially on auto-pilot, or when we get triggered into spitting out stock phrases like "have a nice day". If you want to compare an LLM to a human brain, it's basically equivalent to our language cortex if you ripped out all the external connections and ripped out all the feedback paths that make it capable of learning. Of course there is a lot more to our brain than just our language cortex, but that alone should make you realize there is no real comparison beyond the fact that our language generation is also going to be based on prediction, and partly auto-regressive. | |
| ▲ | ahartmetz 3 hours ago | parent | prev | next [-] | | We have spatial / quantitative and social / emotional aspects in our intelligence that are not at all like next token prediction. If LLMs had shame, they'd surely not repeat mistakes (in the same context window) as much as they do. | | |
| ▲ | sjsdaiuasgdia 3 hours ago | parent [-] | | Having shame would require the LLMs to actually be able to recognize mistakes they make. People love to put a lot of meaning on what an LLM responds with when asked why it made a mistake, but it's critical to remember that the answer to that prompt is just another series of probabilistic tokens, and has no actual relation to how the error happened. | | |
| ▲ | ahartmetz an hour ago | parent [-] | | They "recognize" mistakes just fine because you explicitly tell them. They recognize them well enough to correct (...sometimes). The way in which mistakes don't register is "Oh shit, that bad result was a result of my inappropriate actions. I must pay attention to not doing that again or the user will think I'm an idiot. I should even think about it some more to avoid the whole class of mistakes". Think of emotions as an attention mechanism that LLMs lack. |
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| ▲ | ElevenLathe 3 hours ago | parent | prev | next [-] | | It's language. Language itself is the thing that makes us smart in the unique way that we are among the other animals, and it weirdly turns out to be transferable to machines to at least some degree. | | |
| ▲ | HarHarVeryFunny 2 hours ago | parent | next [-] | | At least 50% of humans have no "inner voice" and are not thinking in the same way as you. Many animals like dolphins, dogs, rats, crows are also very intelligent yet appear to only have primitive language capabilities. A lot of human intelligence is really societal rather than individual, based on knowledge transmitted down through generations by writing (the real enabler). If you take that away then what you are left with is something more like an isolated hunter-gather tribe. | | |
| ▲ | ElevenLathe an hour ago | parent [-] | | I personally think that the "inner voice" is a non-falsifiable claim, and therefore more of a religious belief than something which can be part of any materialist theory. In this regard, I'm a strict empiricist and wouldn't be able to claim that I have one myself. In fact, I find that thinking "out loud" or "on paper" produces much better results in most instances, probably because I'm grounding my thinking in natural language, which is a fantastic medium for thought. If my "inner voice" were comparable in efficacy to actually speaking or writing, we wouldn't notice this effect, but I'm definitely not alone in this regard. Your point about writing and social intelligence is, to me, more evidence for the "it's language that's smart, not us" hypothesis. We start off in small bands of hunter-gatherers that store their intelligence in an oral culture. Language then jumps to clay tablets, papyrus, codex books, etc. The printing press allows it to escape containment to a wider public than just a caste of priests and bureaucrats. As soon as we invent automatic calculators, we start networking them and using those to process language, albeit in a primitive way (email, the web, etc.). Recently we discovered some abstruse math that, with the assistance of a bunch of beefy video cards, can crunch centuries of human writing into a mathematical object that encodes at least some of the meaning of that writing into an even more "advanced" symbolic processing machine. There's a clear trajectory of language itself getting more and more free of the specific wetware it grew up on. It's a falsifiable claim, in that if there is a way to train a useful LLM from scratch without any human authored input language to bootstrap it (something I've been on the lookout for but haven't seen, though admittedly I'm not an AI researcher, just some Linux nerd with a day job as an SRE), then we can disprove it. For the religious angle, look no further than John 1: "In the beginning was the Word, and the Word was with God, and the Word was God." (This is admittedly less falsifiable!) |
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| ▲ | adjejmxbdjdn 3 hours ago | parent | prev [-] | | I think you’re right but I wanted to note that we are discovering that other animals also have language. | | |
| ▲ | ElevenLathe 39 minutes ago | parent [-] | | Yes, this to me is also a good sign for the "it's language that's smart, not us" argument. It's an emergent trait that has evolved several times, like flight or carcinization. There's something about language that attracts evolution toward it. One would expect such a trait to have a big survival value (disclaimer: IANA biologist, philosopher, theologian, mathematician, or linguist). |
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| ▲ | roenxi 3 hours ago | parent | prev | next [-] | | > I think it’s legit, actual intelligence, not just “artificial” intelligence. That may be a hot take but it’s just my perception. You might be redefining words here; there isn't a form of intelligence that isn't actual intelligence. It is all actual intelligence. Artificial in this context means it is something we're creating in a lab. LLMs can't avoid being artificial intelligence. The meaning of "AI" is to artificially create actual intelligence. | |
| ▲ | PunchyHamster 3 hours ago | parent | prev [-] | | average person is absolutely awful judge on anything you put in front of average person tho. And if anything, average AI user is vastly overstating how good/useful it is. Papers about it pretty much always show huge gap between "productivity person thinks they are achieving" and "actual growth of productivity" |
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