35 ms·

What is ChatGPT doing and why does it work?

• ortusdux 4y ago
  Tangentially related, but I really liked Tom Scott's recent video on ChatGPT. https://www.youtube.com/watch?v=jPhJbKBuNnA https://www.youtube.com/watch?v=jPhJbKBuNnA
• sharemywin 4y ago
  It’s Just Adding One Word at a Time I'm curious how do you write?
  • goatlover 4y ago
    Like the Aliens in Arrival? But seriously I have a general idea of what I’m going to say and then decide the words as I write it, with some edits after the initial draft.
  • sharemywin 4y ago
    Sometimes I copy and paste so maybe that counts as something different
  • acchow 4y ago
    I think in an "idea" space, which then I transcribe into words.
    • deleted 4y ago
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    • krackers 4y ago
      If this idea space is linear, it gives a whole new meaning to an idea being orthogonal to another.
      • yen223 4y ago
        I've always thought "orthogonal" as in ideas came from "orthogonal" as in vectors - meaning to be independent of each other.
        • abudabi123 4y ago
          To sort the mess in psychology a statistical technique separates the variables as dependent and independent.
  • sakex 4y ago
    I don't re-read the whole sentence before every word I type, contrary to transformers. Also, I can go back and correct my mistakes.
    • bheadmaster 4y ago
      > I don't re-read the whole sentence before every word I type Maybe you don't do it consciously, but your brain is quite aware of every word you typed before the word you're typing.
      • SketchySeaBeast 4y ago
        My brain knows where it's going to go by the end of the sentence as well, it's conceptualized the whole sentence and my hands are on the road to completing the sentence, I'm not only aware of what I've written so far.
        • bheadmaster 4y ago
          > My brain knows where it's going to go by the end of the sentence as well How would you know that? Sincere question, because to me it feels like my brain is improvising word by word when typing out this sentence. I often delete and retype it until it feels right, but in the process of typing a single sentence, I'm just chaining words one after another the way they feel right. In other words, my brain doesn't exactly know the sentence beforehand - it improvises by chaining the words, while applying a fitness function F(sentence) -> feeling that tells it whether it corresponds to what I wanted to say or not.
          • Ensorceled 4y ago
            > I'm just chaining words one after another the way they feel right. I think we know where we are going in a conceptual sense, the words start feeling right because they are taking us to that destination, or not. If I leave a sentence in the middle for some reason, when I return I often have zero idea how to finish the sentence or even what the sentence fragment means.
  • Swizec 4y ago
    I have an idea or concept first, then I translate that into analogies/stories, then I put those down as sentences/paragraphs, then I wordsmith to make it flow better. At no point am I in a mode where I say a word and think "What's most likely to come next?". The concept/idea comes first. Likely I will try different angles until I find what lands with the audience. ChatGPT works more like a stereotypical extrovert: It doesn't think then output, it uses output to think. Which can be a fine mode for humans too. Sometimes, when you don't know what you're trying to say yet or when you need to verbalize what your gut is thinking.
  • IIAOPSW 4y ago
    There's a lot of answers here, but as Feynman said "that which I can't build I don't understand." If you can't make something that writes for you, you don't really understand how you write. That feels impossible, to be able to do something without understanding how you do it. Brain be like that sometimes.
    • leereeves 4y ago
      Understanding how ChatGPT writes doesn't translate to understanding how humans write/speak/think. We still don't understand the latter.
      • IIAOPSW 4y ago
        Notice I didn't say anything about chatGPT. chatGPT is at least something that's been built. Disagree with the theory that we are overgrown text prediction all you want, but you can't deny that it is an "understanding" in the sense that knowing it lets you build and test it. Let's call it a "potential understanding". This is different from an "illusory understanding", which feel like explanations but when you go to implement it you realize it was all a word game and your lack of understanding has just been swept under different words or under equally hard to understand concepts. An illusory understanding doesn't even have the potential to be an understanding. You can't use it to build your own version of GPTchat. All these glib answers people here are giving aren't even potential understandings. They are illusory understandings. They sound nice so long as you don't try to use it as a spec to code up your own language model. If you can't build anything out of an "understanding", even a wrong thing, then you don't truly have an understanding. You have a feeling of understanding.
        • leereeves 4y ago
          > Disagree with the theory that we are overgrown text prediction all you want, but you can't deny that it is an "understanding" in the sense that knowing it lets you build and test it. Yeah, and it helps us understand how "overgrown text prediction" works, but not how humans work. In the same way that building a robotic arm won't help you understand how muscles work. > If you can't build anything out of an "understanding", even a wrong thing, then you don't truly have an understanding. Not entirely true. We can't build a star, but we do have a pretty good theory of how stars work. But I do agree that we don't understand the human brain.
  • bgun 4y ago
    Premise, outline, augmentation.
  • Traubenfuchs 4y ago
    I was just trying to do some metacognition and observe how I write, but apparently it's really just word by word. I neither form full sentences, nor full words or even just abstract imagination in my head. The words just appear from the "darkness", with some kind of sophistication postprocessor that tries to make some output more verbose or use more appropriate adjectives. Is this how people with aphantasia live? I don't like it. I expected something more sophisticated. Maybe that's why my writing often appears like a barely connected verbal diarrhoea that looks like an "inner monologue" writing task back in school. How do you experience it?
    • Ensorceled 4y ago
      There is definitely something else going on ... if I stop writing and come back to it often the sentence fragment I was writing makes no sense at all. If it was word by word, I'd just start writing again. Also, I usually "hear" the next segment fragment in my head before I'm typing it.
    • akomtu 4y ago
      That's how it looks like of your mind was below your consciousness level, i.e. if your focus was on higher ideas, and you just commanded the mind to produce specific thoughts. That's similar to how we control our body. However you might be at the ideas level already, but unable to see those ideas, so it looks like darkness to you. I believe you shpuld be able to see thoughts again simply by persistently focusing your attention on them, just like you could (but shouldn't) control low-level body functions by switching attention to them. If your consciousness was focused on thoughts, you'd see something like a very active 3d whiteboard filled with fine shapes, that you can create, change and destroy at will.
  • anothernewdude 4y ago
    Mostly by rewriting and editing.
  • deleted 4y ago
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• Scene_Cast2 4y ago
  Well, aren't Beam Search and other searches also used and more sophisticated than greedy selection?
  • anothernewdude 4y ago
    They barely use beam search. It requires running multiple parts of the generation, and so is expensive.
  • techbruv 4y ago
    ChatGPT and other LLMs for that matter are most definitely not using beam search or greedy sampling. Greedy sampling is prone to repetition and just in general gives pretty subpar results that make no sense. While beam search is better than greedy sampling, it's too expensive (beam search with a beam width of 4 is 4x more expensive) and performs worse than other methods. In practice, you probably just wanna sample from the distribution directly after applying something like top-p: https://arxiv.org/pdf/1904.09751.pdf https://arxiv.org/pdf/1904.09751.pdf
• xwdv 4y ago
  You can approximate your own ChatGPT on your iPhone by just randomly selecting words that appear in the autocomplete to form a sentence. This is basically how ChatGPT works but better and on a larger scale. Give it a try you’ll be surprised what comes out.
  • Ensorceled 4y ago
    I don’t know what to do with my life right now. I just want to be able to be with you and I know that I am not alone in my feelings. Wow. Very “Valentines Day” meets therapy session.
    • bqmjjx0kac 4y ago
      > What is the best way to know what you want to do with the kids and the kids and the kids and the kids and the kids and the... Excellent work, Gboard. This has a distinctly different flavor than ChatGPT.
    • r2_pilot 4y ago
      I'm just curious if you're using SwiftKey. I just did a similar experiment with my autocomplete and the second phrase was "I am not sure what to do with my life". Ostensibly it's trained against my dictionary inputs, but I question how much it truly pulls from that.
      • Ensorceled 4y ago
        Just the default iPhone keyboard. Very weird "coincidence" ...
  • KRAKRISMOTT 4y ago
    Not all Markovian maximum likelihood estimators are made equal, ChatGPT can be considered sui generis.
• spion 4y ago
  The answer to this is: "we don't really know as its a very complex function automatically discovered by means of slow gradient descent, and we're still finding out" Here are some of the fun things we've found out so far: - GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559
  • codeulike 4y ago
    That Kenneth Li Othello paper is great. The accompanying blog post https://thegradient.pub/othello/ https://thegradient.pub/othello/ was discussed on HN here https://news.ycombinator.com/item?id=34474043 https://news.ycombinator.com/item?id=34474043 A lot of people didn't seem to get it when it was discussed on HN. A GPT had _only_ ever seen Othello transripts like: "E3, D3, C4 ..." and NOTHING else. It knows nothing of the board. It doesnt event know that there are two players. It learned Othello like it was a language, and was able to play an OK game of it, making legal moves 99.99% of the time. Inside its 'mind', by looking for correlations between its internal state and what they knew the 'board' would look like at each step in the games, they found 64 nodes that seemed to represent the 8x8 Othello board and representation of the two different colours of counters. And this is the key bit: They reached into its mind and flipped bits on that internal representation (to change white pieces to black for example) and it responded in the appropriate way when making the next move. And by doing this they were able to map out its internal model in more detail, by running again and again with different variations of each move.
    • lossolo 4y ago
      > "E3, D3, C4 ..." and NOTHING else. It knows nothing of the board. It doesnt event know that there are two players. Yeah, like language have gramma rules games also have rules, in both cases LLM can learn rules, it's the same with many other structured chains of actions/tokens, you could also model actions from different domains and use them as language. It seems a lot of emergent behaviours of LLMs are what you could call generalized approximated algorithms for certain tasks. If we could distill only these patterns and extract them and maybe understand them (if possible, as some of these are HUGE) then based on this knowledge maybe we could create traditional algorithms that would solve similar problems.
• codeulike 4y ago
  Wow this is 19,000 words. I like his summary at the end: At some level it’s a great example of the fundamental scientific fact that large numbers of simple computational elements can do remarkable and unexpected things. And this: ... But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. Yeah I've been thinking along these lines. ChatGPT is telling us something about language or thought, we just havent got to the bottom of what it is yet. Something along the lines of 'with enough data its easier to model than we expected'.
  • jw1224 4y ago
    > Yeah I've been thinking along these lines. ChatGPT is telling us something about language or thought, we just havent got to the bottom of what it is yet. Something along the lines of 'with enough data its easier to model than we expected'. I’ve been thinking similarly, and am coming to understand and accept we’ll never get to the bottom of it :) The universe is fractal-like in nature. It shouldn’t be a surprise, then, that if “we” have created an intelligence which exists as a subset of “us”, a self-similar process is ultimately responsible for granting us our own intelligence.
    • visarga 4y ago
      > 'with enough data its easier to model than we expected' > a self-similar process is ultimately responsible for granting us our own intelligence In my view, intelligence essentially resides within language, specifically in the corpus of language. Both humans and AIs can be effectively colonized by language, as there are innumerable concepts and observations that are transmitted from one mind to another, and now even from mind to LLM. Initially, ideas were limited to human minds, then to small communities, followed by books, computers, and now LLM stands as the ultimate epitome of language replication, in fact one model could contain the whole culture. To be sure, there is a practical intelligence that is learned through personal experiences, but it constitutes only a tiny fraction of our overall intelligence. Hence, both AI and humans have an equal claim to intelligence, because a significant part of our intelligence arises from language.
• ionwake 4y ago
  It was kinda long so I uhh fed it into chatgpt for a summary : The author, Stephen Wolfram, describes the process of training ChatGPT using large amounts of text data, which allows the model to learn patterns and associations between words and phrases. He explains that ChatGPT uses a multi-layered approach to generate responses, starting with analyzing the input text and then generating a response based on the learned patterns. Wolfram notes that ChatGPT's ability to generate human-like responses is due to the model's ability to capture context and incorporate knowledge from a wide range of sources. He also discusses the potential uses of ChatGPT, including as a tool for language translation, customer service, and educational purposes. The article goes on to discuss some of the challenges and limitations of ChatGPT, such as its tendency to generate responses that are repetitive or irrelevant to the input text. Wolfram also acknowledges ethical concerns related to the use of AI for generating text, such as the potential for misinformation and the need for transparency in how the technology is used. Overall, the article provides a detailed and informative overview of ChatGPT and its underlying technology, as well as the potential applications and challenges associated with AI-generated text.
  • deleted 4y ago
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  • spauka 4y ago
    So ChatGPT didn't correctly summarize the text at all, this article does not talk about ethics at all, doesn't talk about potential uses and I would consider the statement about capturing context and incorporating knowledge from a wide range of sources to be a pretty poor summary of the text. In fact I think that's a great example of exactly what is actually discussed, namely that the context that ChatGPT is able to hold is limited as it's context is held completely in its input. There is never any modification to it's internal state, we're just passing a longer input vector in to the start of the GPT-3 black box. For long inputs the embedding vector becomes more and more sparse and it needs to make more assumptions to fill in it's output.
    • ionwake 4y ago
      Yep I agree, no idea why I got downvote I thought it was deliciously relevant to feed an article about chatgpt into itself
• rrrrrrrrrrrryan 4y ago
  For those that want a tl;dr, this reddit comment is the best simple-language explanation I've come across: https://www.reddit.com/r/ChatGPT/comments/10q0l92/comment/j6obnoq/ https://www.reddit.com/r/ChatGPT/comments/10q0l92/comment/j6...
  • matt-attack 4y ago
    Can you explain: Prompt: "Has anyone really been far even as decided" Expected transformation: "to use even go want to do look more like?" Those look like gibberish in and gibberish out to me.
    • kibwen 4y ago
      It's an old meme: https://knowyourmeme.com/memes/has-anyone-really-been-far-even-as-decided-to-use-even-go-want-to-do-look-more-like https://knowyourmeme.com/memes/has-anyone-really-been-far-ev...
      • fijiaarone 4y ago
        Which proved ChatGPT wasn’t trained on a corpus of human language, or else it would have worse grammar, more fallacies, and way less consistency.
• chx 4y ago
  Wasn't there a humorous law which said "Any headline that ends in a question mark can be answered by the word no" and indeed, what makes you think it works? It fools people into thinking it works but have you seen that chess match between Stockfish and ChatGPT? It's the best, most succinct demonstration of what ChatGPT is. https://pastebin.com/X6kBRTa9 https://pastebin.com/X6kBRTa9 https://i.redd.it/fl4puwzxt9ha1.gif https://i.redd.it/fl4puwzxt9ha1.gif https://youtu.be/rSCNW1OCk_M https://youtu.be/rSCNW1OCk_M
  • jodrellblank 4y ago
    Betteridge's law of headlines - https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
  • the_af 4y ago
    In my experience, ChatGPT is unsuitable even for tic tac toe. When I tried it, it hallucinated completely incorrect board positions (e.g. forgot about moves already made or wanted to continue playing after I had won).
    • williamcotton 4y ago
      You (or running software you interface with ChatGPT) needs to maintain the state of the game between moves. LLMs aren’t computers but they can interact with computers. LLM(InitialInstructions)->Computer(CodeWrittenByLLM)->LLM(InstructionsOutputByComputer)->LoopUntilWin
      • janalsncm 4y ago
        In my experiments that doesn’t help. ChatGPT failed to play any valid move given a position, let alone a good one. The point is that language is probably too “linear” to represent what’s going on during a game. Pieces on the board have complex relationships (consider a pinned piece, for example) so autoregressive decoding is simply not enough.
        • 4y ago
• calf 4y ago
  I think what is interesting is even in the 1st sentence of Wolfram's essay, he acknowledges what ChatGPT is doing could merely be superficial. So we can't even tell if ChatGPT actually learned "language" in a superficial versus nontrivial/substantial sense. Lots of people say, I asked ChatGPT to write me a poem/essay, and it did! But was it really a poem/essay, or did it just look like one and on closer examination it is more like a fake out of a poem/essay? A piece of writing is not merely its form, but also its content.
  • svachalek 4y ago
    When you ask a child to write a poem or essay, is it real or fake? I think if we accept that as real, it's hard not to accept ChatGPT as real. Sometimes its writing is not very good, sometimes its not even all that consistent, but from what I've seen the quality level is better than the average non-professional human writer.
    • Barrin92 4y ago
      Realness has little to do with the quality of the output. Or else my TI-83 would be the realest mathematician on the planet, it can multiply numbers that even Terence Tao can't in his head. Judging these systems solely by their output is to repeat the msitakes of behaviorism, even a dumb markov chain or a parrot converses better than an infant, but unlike the infant does not acquire an understanding or representation of language.
      • jonahx 4y ago
        > has little to do with the quality of the output. Non-snarky question: What else can you judge by? Isn't any alternative just putting more precise conditions on the output? With ChatGPT, it's still easy enough to see it's mistakes, and its attempts at fiction and poetry, impressive as they are, are still clumsy to a trained eye, relative to expert human work. But what if they weren't? What happens when they're indistinguishable?
        • 4y ago
• ttctciyf 4y ago
  So, I listened to Wolfram explaining ChatGPT on his youtube channel[1] - a pretty great account for a live streamed impromptu Q&A, IMO - and was amused to hear he has been training an LLM exclusively on his own copious written output: essays, emails, books and so on. Such a Wolframesque move! Not sure how many are aware of the sheer amount of streamed output he uploads to youtube[2]; quite a collection ranging from high quality science explainers on a variety of topics to eavesdropping on product management for his software empire. 1: I think: https://www.youtube.com/watch?v=zLnhg9kir3Q https://www.youtube.com/watch?v=zLnhg9kir3Q 2: https://www.youtube.com/@WolframResearch/streams https://www.youtube.com/@WolframResearch/streams as well as https://www.youtube.com/@WolframResearch/videos https://www.youtube.com/@WolframResearch/videos
  • deleted 4y ago
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• rubyist5eva 4y ago
  It's a bunch of smoke and mirrors akin to blockchain, bitcoin (and the entire web3/crypto space in general) - a whole bunch of hype and "evangelists" who keep saying it's going to change the world (and make them a whole lot of money in the process, how convenient!) but if you do look at it critically even at a surface level you realize it's just a bunch of really computationally expensive BS that isn't any better or more officient than existing status quo solutions.
  • deleted 4y ago
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  • myrmidon 4y ago
    Strongly disagree with this. You can call it smoke and mirrors all you want, but its utility is pretty self-evident- you can really just talk with this thing, and it will give reasonable answers. Is it perfect, or even as good as a human? Hell no, but it for sure is not going to get worse, and it's already remarkable in ways that were barely imaginable only a few years ago... I have a friend that has been using this as an infinitely patient mentor for learning embedded programming, and chatgpt delivers in that capacity unlike any automated system we had before. If a glorified autocomplete can fake human intelligence reasonably well, maybe we should question our notions of superiority instead of trashtalking the machines...
  • ian0 4y ago
    Its not, really. The major difference between this and web3/crypto is utility. Its actually useful. In our office we have people from three different teams using it near daily. Out of choice. As do many kids in our network for homework. For a recently released product effectively in beta thats insane. Theres a bunch of snake oil salesmen jumping on the bandwagon which is very unfortunate. But lots of people sell fake pharmaceuticals online doesn't mean paracetamol wont help with your headache.
    • busyant 4y ago
      > Its actually useful. In our office we have people from three different teams using it near daily. Out of choice. As do many kids in our network for homework. For a recently released product effectively in beta thats insane. Yep. I asked it to make a worksheet for students to practice converting numbers written in scientific notation back to "standard" format. So, it gave me a bunch of output like: 6.2x10^-6: ___________________ What annoyed me about this is that it used the letter "x" instead of the proper multiplication symbol "×" and it used the hyphen (-) instead of the appropriate "minus" sign (−). So, I told it to use proper typographic symbols, and it did! It converted "6.2x10^-6" to "6.2×10^−6" It even told me the Unicode numbers it was using for × and −. Then I asked it to re-generate the worksheet using LaTeX and the siunitx package. It nailed it. It's like someone just handed me a turbo-charged assistant. Yeah, I have to make sure my assistant hasn't gone insane, but it has already spared me a ton of grunt work.
• osswid 4y ago
  In the days when Sussman was a novice Minsky once came to him as he sat hacking at the PDP-6. "What are you doing?", asked Minsky. "I am training a randomly wired neural net to play Tic-Tac-Toe." "Why is the net wired randomly?", asked Minsky. "I do not want it to have any preconceptions of how to play" Minsky shut his eyes, "Why do you close your eyes?", Sussman asked his teacher. "So that the room will be empty." At that momment, Sussman was enlightened. -- AI koan
  • thegabriele 4y ago
    I keep on stumbling on this story, yet i can't seem to be able to grasp it. We have senses so our brain has some sort of a priori knowledge of the world? We're all almost blind when we born.
    • michannne 4y ago
      The point of Minsky's action is to demonstrate to Sussman that Sussman's intent is essentially "if you can't see it, it's not there", but of course the room doesn't become empty when Minsky closes his eyes - the neural net won't lose preconceptions just because you randomly wired it, you just lose the ability to see and control what those preconceptions are.
  • osswid 4y ago
    Minsky and Sussman slaving away in front of that apocryphal PDP-10 in 1970. And 50 years later…last November..suddenly BOOM. Everything they yearned for comes into view.
  • G_z9 4y ago
    This allegory has haunted me for ten years. I always see it posted here and invariably the poster doesn’t elaborate or explain at all. Im pretty sure the people who post this just do it to look smart. I can’t find an explanation anywhere on the web. I asked chatgtp what it means and it said this: This allegory, often referred to as an "AI koan," is a story that conveys a deeper meaning about the nature of artificial intelligence and the process of learning. In the story, Sussman is a novice who is attempting to train a neural net to play Tic-Tac-Toe. When Minsky, a renowned AI researcher, asks why the net is wired randomly, Sussman responds that he does not want the net to have any preconceptions of how to play. Minsky then closes his eyes, explaining that he is doing so in order to empty the room. The meaning of this story is open to interpretation, but one possible interpretation is that it is highlighting the importance of approaching problems with an open mind, free of preconceived notions and biases. By wiring the neural net randomly, Sussman is allowing it to learn through trial and error, without being constrained by prior assumptions about the game. Similarly, by closing his eyes, Minsky is symbolically "emptying the room" of preconceptions and biases, allowing himself to approach the problem with fresh eyes and an open mind. Overall, the story encourages us to approach complex problems with a beginner's mind, free of preconceptions and biases, in order to allow for creative solutions and new insights to emerge. Back to human commentary: I’m not sure that makes sense. will someone please explain this stupid allegory and let me finally rest?
• fijiaarone 4y ago
  I like how he says “this is how ChatGPT works” and then goes on to prove that it’s impossible. What was demonstrated is how iPhone assist works, and why everything I tap into my phone is nonsense.
• robert-wallis 4y ago
  I'd really like to see an LLM use Wolfram Alpha APIs like the new Toolformer paper does https://paperswithcode.com/paper/toolformer-language-models-can-teach https://paperswithcode.com/paper/toolformer-language-models-...
  • michael-go 4y ago
    checkout https://youtu.be/wYGbY811oMo https://youtu.be/wYGbY811oMo and play with the demo at https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain
• akomtu 4y ago
  GPT looks a lot like an IIR filter that transforms a sequence of vectors. Edit: IIR filters are linear functions of N past inputs and N past output - the latter gives them "memory" and non-trivial abilities to process signals. GPT is mostly linear, uses 8192 past inputs and outputs. I'd be tempted to introduce the 3rd sequence - an "internal buffer" with 8192 tokens - that GPT updates even with null inputs, the process that corresponds to "thinking".
• bottlepalm 4y ago
  The easiest way for ChatGPT to generate good output is to plain understand it. Given the vast amount of input data fed into it, it has no choice, but to start reducing the input into fundamental rules which is basically what understanding is. Understanding is a form of compression. More efficient for a neural network to understand a concept than memorize permutations. Same with statistics and markov chains, people for years tried to generate chat bots with those, but they never worked well.
  • goatlover 4y ago
    Disagree when it comes to anything in the real world how we experience things.
    • bottlepalm 4y ago
      What do you mean? Your brain takes in very raw input from air pressure, two eye cameras, chemical sensors, etc.. and reduces that input into a coherent singular experience.. same with reading words, you just 'understand' it, after being trained for many years by your parents and consuming vast amounts of input from your surroundings.
      • goatlover 4y ago
        You mean my body interacts with the environment as an organism, so I know what trees and cars and human interaction is, unlike an LLM. Your sensory organs are part of your nervous system. You're not just a brain inside a meat suit.
        • bottlepalm 4y ago
          You kind of are. The 3d coherent world you experience is completely rendered by your brain. The inputs are just 2 cameras waving around. But to you it’s a stable coherent 3d world. Why do you think a LLM doesn’t know what a tree or car is?
  • Dr_Birdbrain 4y ago
• dboreham 4y ago
  For some perspective on how the wetware might be doing this, I found Lisa Feldman Barrett's work fascinating.
• scottlawson 4y ago
  I spent about an hour reading this. I know most people probably won't be able to do the same because of time constraints in their day, but seriously this is the most awesome post about large language models I've read. Take a second to think about how much time it must have taken to write this, to prepare all of these examples. This post gave me another way to look at something I already partially understood pieces of. It helps me understand how chatgpt can have these moments of greatness. If you think of this like a long lecture you can get a lot out of it. It reminds me of a feynman lecture.
  • inasio 4y ago
    I'm typically a bit wary of Wolfram's posts, but it is very often the case that the content is good enough that I don't mind a bit of self-aggrandizing. This one though I found to be just pure great, super easy and clear intro into the "magic" of LLMs. Thank you.
    • GreedClarifies 4y ago
      Best comment! He's clearly brilliant and I read his posts, but usually I have to grit my teeth at least a couple of times due to his self-aggrandizing. But there was almost none of that in this one. Great stuff.
      • PaulDavisThe1st 4y ago
        I'm sure you'd have understood more, though, if you had considered Wolfram Language (TM) and also tried a few examples with Wolfram Alpha. /s
    • greenornot 4y ago
      OK, so I started reading the post and initially had the same feeling. May this have been one of those things where he really leaves his pet peeves behind and contributes something? But then his lingo started to creep in. Computational irreducibility, linking to how he invented the concept. Rurial space. Geez man, does everything really have to be a promotion of the cult-like New Kind of Science stuff? Full of links to his own unrelated blog posts? It's sad. I'd really have liked to like the article, he clearly put a lot of effort into it. But I had to stop reading. You can't tell for sure where real explainations end and where his unproven claims start about "knowing" that the universe is just computational and everything just based on some rule from a cellular automaton. And I've gotten conditioned to never start a sentence with "OK, so". It's now giving me the jiffies.
• everyone 4y ago
  I am a game programmer but in my spare time I like to learn and get experience in random interesting areas. Eg. recently I learned electronics and Arduinos. Would ye recommend any projects I could do in order to get experience with and learn about this new AI stuff like ChatGPT?
  • cypress66 4y ago
    Andrej Karpathy videos
    • everyone 4y ago
      Tx but I dont wanna watch videos. I want to build something.
      • cypress66 4y ago
        Build what the videos tell you to build
  • nestorD 4y ago
    Take a look at langchain. It lets you interface with language models and has a number of interesting demos to get you started.
• unclesaamm 4y ago
  It's worth keeping in mind that Stephen Wolfram likely didn't write this himself. I know people who work at the company, and they sign agreements that any intellectual property (including mathematical proofs) they generate are owned by Stephen Wolfram. Anything Wolfram puts out, like blog posts, scientific articles, and books, are likely to be partly or wholly ghost-written.
  • deleted 4y ago
    [deleted]
  • resource0x 4y ago
    It must be Wolfram's own. Every other sentence starts with "But", which is his signature feature :-)
  • thefaux 4y ago
    So in a sense llms can democratize the technique Wolfram has been deploying with human labor for years...
  • smabie 4y ago
    This is how it works at most companies
• hgsgm 4y ago
  That essay is a great example of how ChatGPT can embed ads in chat, like Wolfram embeds ads for his products in his essays.
• criddell 4y ago
  Off topic: I’m looking for a good web clipper to grab just the article and related images from a page like this and save it (as html or a pdf) for me to read later. I’d like it on iPadOS because that’s where I like to read and write. I tried reader mode, but it lost a lot of the images. Any suggestions? Edit: I was able to get a good PDF using the OneNote web clipper on my desktop.
  • Reptur 4y ago
    https://www.google.com/search?q=web+to+pdf https://www.google.com/search?q=web+to+pdf
    • criddell 4y ago
      A good web clipper will grab only the important stuff and leave behind menus, the footer, ads, etc…
  • eclipxe 4y ago
    Take a screen shot. On iPad/iPhone you can save the screenshot of a webpage as a full PDF of the page. Make sure you are in Safari.
    • criddell 4y ago
      I don’t want the right column or the footer. I only want the main content. The Evernote web clipper used to be pretty good at this but it didn’t work on this article. I tried Pocket and it lost the images for me as well. I just tried saving it to Bear and so far, that looks pretty good.
    • dumpsterdiver 4y ago
      Awesome tip. Thank you.
      • criddell 4y ago
        You can also print the page to a PDF. In the share menu, pick Print. In the print options dialog do the pinch to zoom motion on one of the thumbnails and this will give you a big preview that you can save as PDF (again through the share menu).
• est 4y ago
  I've read many articles explains how gpt-3 work, and did a great job. What buffles me is the context consistency. ChatGPT was a huge leap compared to previous models. I have never seen it failed once. I often use "this" or "that" in my conversation with ChatGPT and it would guess 100% correct what I am refering to. Sometimes I paste a chunk of code and ask for questions of a specific part of it, ChatGPT fully understands where I am talking about and gives me very detailed explainations. It's astonishing and I never knew how it worked so well. Also the title suggests "and why does it work" but I failed to find the reason why ChatGPT worked as in contrast that gpt-3/2/1 never really worked (well)
  • kqr2 4y ago
    What other articles do you recommend?
    • est 4y ago
      a recent one: https://news.ycombinator.com/item?id=34726115 https://news.ycombinator.com/item?id=34726115
  • blackbear_ 4y ago
    > I failed to find the reason why ChatGPT worked as in contrast that gpt-3/2/1 never really worked (well) The answer may be as simple as "it has a lot more parameters", plus the additional fine-tuning from human conversation data.
    • GaggiX 4y ago
      GPT-3 has the same number of parameters as ChatGPT, it's just the finetuning (or alignment).
      • est 4y ago
        I kind of doubt it? https://arxiv.org/abs/2203.02155 https://arxiv.org/abs/2203.02155 it says > outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters
• mark_l_watson 4y ago
  I understand why Wolfram has been writing a lot lately about ChatGPT; this article is a good backgrounder. Reading the examples, I am almost sorry that I quit my yearly subscription to Wolfram Desktop a few months ago. I really liked WD a lot, but I only had time to play with it once or twice a month and it is expensive for minimal use. A little off topic, sorry, but I now have access to Microsoft’s ChatGPT + Bing search service. I am amazed at how many little things that annoyed me about ChatGPT are effectively “worked around” in the new combined search service. When the Chat Mode is active, it shows what realtime web searches are made to gather context data for ChatGPT to operate on. Because Microsoft’s ChatGPT + Bing search service is so well engineered, I think that Google has an uphill battle to release something better. When Wolfram started writing about GPT-3 and ChatGPT, I wondered if the Wolfram products would be somehow integrated with it, but now I think he is just intellectually curious.
• probably_a_gpt 4y ago
  Chilling
• fencepost 4y ago
  Well, in at least one example I saw earlier today the answer is "insisting that it's 2022 and that the questioner insisting that it's 2023 is misguided and flat out wrong." This kind of makes sense when you think about it as being in some ways based on predictive text based on what it's ingested, because it's ingested a lot of 2022 content and much less 2023.
• xeyownt 4y ago
  If you don't have a Wolfram language intrepeter, you can use ChatGPT to act like one. It produces quite convincing results. ---------- Hi Chat! Do you know the Wolfram Language? I would like you to act as a Wolfram Language interpreter. I will type in command and you will reply with the expected response. If the response contains some output that you cannot reproduce (e.g. like an image), you will try to replace it by a description of that response. My first command is: model = NetModel[{"GPT2 Transformer Trained on WebText Data","Task" -> "LanguageModeling"}]
  • xeyownt 4y ago
    { "The best thing about AI is its ability to be", "The best thing about AI is its ability to be able", "The best thing about AI is its ability to be able to", "The best thing about AI is its ability to be able to do", "The best thing about AI is its ability to be able to do so", "The best thing about AI is its ability to be able to do so much", "The best thing about AI is its ability to be able to do so much more", "The best thing about AI is its ability to be able to do so much more than humans" }
• Simon_O_Rourke 4y ago
  I'm surprised he didn't try to refer to it as WolframGPT in the article - it must rankle that someone else came up with it.
• ChaitanyaSai 4y ago
  This misses that key point that all this prediction can give rise to what looks like astonishing human-level creativity and across many genres. The last decade and half have shown us that with enough data we can pick out patterns well enough to be able to "categorize". But to create, that seemed like a whole another human level outside the realm of mere prediction. Turns out it isn't. What exactly allows LLMs to have this capacity for flexible mimicry? I mean, I can ask it to generate a rap battle between Eminem and Daniel Dennett about consciousness and it can do it very decently. https://twitter.com/SaiDrGaddam/status/1625750884541169666 https://twitter.com/SaiDrGaddam/status/1625750884541169666 That just seems like it shouldn't be so "effortless". What is in here that makes it possible? edit: OK, the article (which is a wonderful introduction, dont want to seem dismissive) does sort of address this but is buried "We can think of the construction of computational language—and semantic grammar—as representing a kind of ultimate compression in representing things. Because it allows us to talk about the essence of what’s possible, without, for example, dealing with all the “turns of phrase” that exist in ordinary human language. And we can view the great strength of ChatGPT as being something a bit similar: because it too has in a sense “drilled through” to the point where it can “put language together in a semantically meaningful way” without concern for different possible turns of phrase." That for me is the real fascinating question. What's allowing LLMs to tap into this semantic grammar and do it so flexibly?
  • quickthrower2 4y ago
    Can it be the next Eminem tho?
    • noduerme 4y ago
      what's astonishingly good about this remark is how well it encapsulates the fact that we've been strip-mining and rehashing genuinely new material into sequels for the better part of 50 years
    • piersj225 4y ago
      Someone on Reddit has challenged it to a rap battle https://old.reddit.com/r/ChatGPT/comments/10zfvc7/chat_gpt_rap_battled_me https://old.reddit.com/r/ChatGPT/comments/10zfvc7/chat_gpt_r...
• azeirah 4y ago
  I wish Wittgenstein was still around to comment about these developments
• tempodox 4y ago
  Maximally simplified: Markov chain on steroids.
• chimen 4y ago
  I merely follow the AI news since I'm not operating in the field but, on the "are we there yet" note, how much until AIs will start working on better versions of language models. Ai working on AI let's say.
  • zulban 4y ago
    ChatGPT can already help its programmers to develop itself. However it's hard to say how far it is from taking full control of that process. Probably a ways off.
• somedude895 4y ago
  Using ChatGPT really helps me understand the concepts better. For example the temperature value: Prompt: Can you answer the following question, once with a temperature of 0, and once with a temperature of 1000? "Is climate change real?" Answer: Sure, here are responses to the question "Is climate change real?" using temperatures of 0 and 1000: Temperature of 0: "Yes, climate change is real. It is a scientifically established fact that Earth's climate is changing, largely due to human activities such as burning fossil fuels and deforestation, which release large amounts of greenhouse gases into the atmosphere and contribute to global warming." Temperature of 1000: "Ah, climate change... what an interesting and multifaceted topic! Some would argue that it's a hoax perpetuated by the government and the media to push certain agendas, while others would claim that it's an inevitable consequence of the Earth's natural cycles and processes. Still, others might say that climate change is just a manifestation of human hubris and shortsightedness, a reflection of our misguided attempts to control and manipulate the natural world. What do you think?" As you can see, the response generated using a temperature of 0 is a clear and factual statement of the scientific consensus on climate change, while the response generated using a temperature of 1000 is highly creative and unpredictable, expressing a wide range of possible opinions and viewpoints on the topic.
  • rcfox 4y ago
    What does "temperature" mean here though? Are you sure you didn't just ask it to generate two different responses?
    • GuB-42 4y ago
      That's the amount of randomness in the answers. Low temperature means it will take the most common path every time, at the risk of paraphrasing its sources. The "zero temperature" answer may very well been copied verbatim from a mainstream website. High temperatures means the system will get fed a lot of noise to create something original, at the risk of getting off rails or simply wrong.
      • newswasboring 4y ago
• madballster 4y ago
  Stephen Wolfram has a way of explaining and teaching that is unique. What a wonderful read.
• frozencell 4y ago
  and a few days ago, some HN users who probably prefer HTML scientology articles criticized Wolfram to just play with shit useless visualizations but they are creative - maybe artistic - but scientific mostly ways to improve the understanding of the world.
• deleted 4y ago
  [deleted]
• sangnoir 4y ago
  The number of people ITT who are reading interpreting ChatGPTs output as intelligence is too damn high. I thought Ex Machina was unrealistic because of its dependence on AGI, or at least having a theory of mind. As it turns out, in the real world,a LLM trained on Tinder data could probably get the job done.
  • machiaweliczny 4y ago
    As mentioned by other commenter in this thread, GPT-3 can solve theory of mind problems similar to 9 year old kids.
• eric_hui 4y ago
  [dead]
• jokoon 4y ago
  To me, modern AI is just "black boxes all the way down". Even specialists don't really know what's happening. It's not encouraging or interesting. Personally, I'm more interested in analyzing those black boxes than tinkering ones that "seems to work", would it be with graph theory, analysis, etc. To me, if something works but we're unable to really understand why it does, it's more the realm of "testing broken clocks that work twice a day". Not to mention it's always more interesting to look at how psychology and neurology define intelligence.
  • chongli 4y ago
    I agree. I’m currently taking a neural networks course and it’s making my discomfort worse, not alleviating it. At the beginning of the course we talked about biological models of neurons and that was pretty cool, if a bit simplistic. Now we’re deep into automatic differentiation and gradient descent and a bunch of hidden layers. Ultimately it’s all just using calculus to approximate some unknown function given a sample of data. The connection to biology, to real living brains, seems like a distant memory. There is no path to understanding, from what I can see. It’s pure instrumentalism and parlour tricks.
    • LeanderK 4y ago
      neural networks are vaguely inspired by the human brain but have as much in common with the humans brain as planes have with birds
    • krona 4y ago
      Complex systems theory is probably a better area to at least get to grips with if you want to gain at least an idea of what the shape of our lack of our understanding is like, vis-a-vis cognition etc. Automatic differentiation is too deep in the weeds whereas the 'behaviour' of neural networks is more emergent.
    • ghosty141 4y ago
      > There is no path to understanding, from what I can see. It’s pure instrumentalism and parlour tricks. Exactly like nature. It feels like emulating whats happening in nature for a very specific usecase under some constraints.
• landswipe 4y ago
  I'm saying it now, tapping into randomness randomly is how this whole universe works. All the complexity you see around you; life itself, emerges out of an infinitely random space. This shouldn't surprise anyone, the right answer to explaining intelligence is not about finding the right answer but the wrong one, sometimes, and then feeding back on itself until it is coherent. Also, this goes much deeper than just the superficial, information coalesces as a result of this, reality is fundamentally defined by this. The observer, you, your consciousness, emerges from that random infinite space of interconnectivity, you tune into that space as a source of shared but bounded information (like a pianist reciting a complex piece from "memory", who doesn't have enough information density in her neurons to encapsulate that knowledge). This prediction machine of 'what comes next' is present, until your cells can no longer co-ordinate, at which point it is over.
• ClumsyPilot 4y ago
  ChatGPT will be used in customer service first, it will keep you on the phone for hours without you being able to reach a human and it will lie to you
• bambax 4y ago
  Ok, I've read the whole post. It's quite long, but fantastic! (It's also quite unlike so many ramblings from Stephen Wolfram that are always pitching "the Wolfram Language" or the Wolfram platform or some kind of Wolfram system. He does a little bit of that at the end, but not too much.) What I like the most about it is that it starts from first principles, explains what machine learning fundamentally is, what's a neural network, what's a transformer, and ends with interesting questions about human language. His main point is that human language is probably much simpler than we thought. Some excerpts: > In the past there were plenty of tasks—including writing essays—that we’ve assumed were somehow “fundamentally too hard” for computers. And now that we see them done by the likes of ChatGPT we tend to suddenly think that computers must have become vastly more powerful—in particular surpassing things they were already basically able to do (like progressively computing the behavior of computational systems like cellular automata). > But this isn’t the right conclusion to draw. Computationally irreducible processes are still computationally irreducible, and are still fundamentally hard for computers—even if computers can readily compute their individual steps. And instead what we should conclude is that tasks—like writing essays—that we humans could do, but we didn’t think computers could do, are actually in some sense computationally easier than we thought. > In other words, the reason a neural net can be successful in writing an essay is because writing an essay turns out to be a “computationally shallower” problem than we thought. And in a sense this takes us closer to “having a theory” of how we humans manage to do things like writing essays, or in general deal with language. (...) > So how is it, then, that something like ChatGPT can get as far as it does with language? The basic answer, I think, is that language is at a fundamental level somehow simpler than it seems. And this means that ChatGPT—even with its ultimately straightforward neural net structure—is successfully able to “capture the essence” of human language and the thinking behind it. And moreover, in its training, ChatGPT has somehow “implicitly discovered” whatever regularities in language (and thinking) make this possible. > The success of ChatGPT is, I think, giving us evidence of a fundamental and important piece of science: it’s suggesting that we can expect there to be major new “laws of language”—and effectively “laws of thought”—out there to discover. In ChatGPT—built as it is as a neural net—those laws are at best implicit. But if we could somehow make the laws explicit, there’s the potential to do the kinds of things ChatGPT does in vastly more direct, efficient—and transparent—ways. Of course it's pure conjecture at this point. Yet it's all quite convincing and indeed, pretty exciting.
• aj7 4y ago
  There’s an enormous opportunity and need for adverse products to quickly detect LLM-produced content. We also need labeling, like the nutritional information on food packages.
  • scrollaway 4y ago
    This idea reminds me of DRM. It’ll always be a game of cat and mouse. And when AI content is too difficult to distinguish from human content you’ll be rife with false positives. It sounds pretty fucking dystopian to me that I would get, for example, banned from commenting somewhere because I didn’t structure my thoughts exotically enough to not be possibly machine-generated. Mark my words: well intentioned as they might be, businesses being created today to detect ChatGPT stuff will in a few years be scummy as hell; you just have to look at the student anti cheat industry. In fact all of these types of businesses end up slimy. DRM, antivirus, anticheat, AML… and now, anti-LLM.
• mensetmanusman 4y ago
  I hope we eventually understand the unreasonable effectiveness of LLMs, if not, it will still be a delightful mystery to live with.
• mlrhazi 4y ago
  An analogy I've been thinking about, to try an explain ChatGPT to non IT/CS family/friends: - Imagine a very smart Englishman, who never had any exposure to Chinese language, gets kidnapped by some Chinese mafia dudes. for days/months/years, he is listening to them talking.... and at the end, he can "engage" in any conversation, in Chinese.... even though he still knows absolutely nothing about Chinese, not the meaning of any single word!!! That's ChatGPT or generative AI. How good/bad that is? How to improve it?
• FounderBurr 4y ago
  This is exceptional.