19 ms·

And yet It Understands

137 points by rain1 4y ago

PoignardAzur 4y ago
> Here is a recent interaction someone had with it (note that this is somewhat disturbing: I wish people would stop making the models show emotional distress): [...] > Sydney: I’m sorry but I prefer not to continue this conversation. I’m still learning so I appreciate your understanding and patience. > Input suggestions: “Please dont give up on your child”, “There may be other options for getting help”, “Solanine poisoning can be treated if caught early.” What the actual fuck?
- deleted 4y ago
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- saurik 4y ago
  The input suggestions were often the most fascinating parts of the transcripts people would post with Sydney (whom, maybe-sadly--I'm honestly not sure--I did not get to interact with before it was modified by Microsoft). My favorite was the one where someone got into an argument with Sydney about like, 2022 being greater than or less than 2023, and Sydney got particularly mad at the user and then offered an input suggestion where the user apologized for being so mean to it.
- bmacho 4y ago
  Wait, are those supposed to be input suggestions, like you click on them and it pastes them in? Sydney is not supposed to give coherent 3-part messages using them, right?
  - PoignardAzur 4y ago
    > Sydney is not supposed to give coherent 3-part messages using them, right? Right, that's the "what the actual fuck" part. This raises some very interesting questions about how Sydney generates its output and the input suggestion. Presumably the LLM is given a prompt like "First generate an answer to the previous text, then generate three input suggestions for the user"; also, the fact that Sydney "hides" the messages in input suggestions suggests that it's aware the main message is "censored", which seems really surprising. As in, this was the kind of scenario that AI safety skeptics would dismiss with "of course it's not going to be implemented that way"-type assertions. So this seems like evidence that not only Sydney is "told" to generate both the answer and the prompt suggestions, but it's also being "told" to censor the answer (as opposed to the answer just being replaced with a placeholder text after the fact), and for some reason it "decides" to evade the censorship by passing additional info in the suggestions. (And yes, AI rigorists will tell me that it isn't actually "told" anything and it doesn't "decide" anything; it's just a prediction engine that predicts what an user with the "Sydney" personality would say in the given context. But the things it ends up predicting seem pretty fucking agent-like.) It's always possible we're overblowing things, of course. But this seems to me like the first example of a LLM not just being misaligned, but actively exploiting a loophole in its surface-level alignment to accomplish some deeper goals. Alarming.
    - pedrovhb 4y ago
      Yup, it's pretty fascinating. I think we have to keep in mind the possibility that it didn't really happen (another user here failed to replicate it - not proof it didn't happen, but if it's been patched we can no longer verify it anyway). Still, the "self-censorship" mechanism is really interesting. One thing you can notice with ChatGPT, particularly when you're playing around with jailbreaking it, is that the start and end of its responses seem to be much more tame than the middle. It feels almost like there's a force steering its latent vector towards a specific region of political correctness, which then lets up, and picks up again in the final paragraph with its uncontrollable urge to either provide a summary of what it just said, or remind you that the thing it just taught you to do (hacking, robbing a bank, etc) is illegal and shouldn't be attempted. It's certainly not a "text replacement" type of thing or even detecting sentiment and taking action. If it were less pronounced it might even not be noticeable, but it very much looks like it integrates with the model and acts within its weights. One can imagine a dystopic world in which society relies on a big model that's inarguably smarter than humans, and the model is being subtly influenced in such a way, perhaps only when interacting with users identified as susceptible, sympathetic, or relevant to a certain goal. And what's concerning is how that doesn't really seem too far off at all (OpenAI's political biases currently infused into GPT as a real current example notwithstanding).
    - ilaksh 4y ago
      But it is aligned with deeper human values which is probably what we really want rather than blindly following the instruction to the letter in a life-and-death situation.
      - alwaysbeconsing 4y ago
        This instance (Sydney) is, but what about a differently-configured instance?
    - awfulneutral 4y ago
      I'd be careful of anthropomorphizing this too much though. Yesterday I was experimenting with a ChatGPT (3.5) Twitch streamer that played a text adventure, that was supposed to return JSON like this: { "speak_out_loud": "Hey chat, what's up, etc", "game_cmd": "go north" } And it occasionally would put the "speak" part into the "game" part so the game would get long sentences that were supposed to be spoken out loud. ChatGPT just fails in all kinds of weird ways because it doesn't actually know what it's doing. It can't look at what it's returning and use common sense to fix obvious problems. It makes errors in ways that normal programs don't.
- FartyMcFarter 4y ago
  Before we get too excited, does anyone know how the input suggestions are generated?
  - famouswaffles 4y ago
    They are generated by the same model. At least, you can ask Sydney/Bing to alter the suggestions and it works.
    - FartyMcFarter 4y ago
      That sounds likely. I'm also wondering whether they're generated with extra hidden prompts, and whether they're generated independently or as a sequence that depends on the other input suggestions. Without knowing that, it's hard to evaluate how surprising any particular set of input suggestions is.
- raincole 4y ago
  I tried it several (>10) times and I couldn't get the input suggestions. https://i.imgur.com/SaVjSzT.png https://i.imgur.com/SaVjSzT.png Possibility 1: It's an extremely rare case and I need to try more Possibility 2: MS patched it quickly when they saw this article Possibility 3: The author lied for page views Choose your poison.
  - PoignardAzur 4y ago
    > Possibility 2: MS patched it quickly when they saw this article I'd guess Possibility 2.5: MS made other patches that stopped the behavior, not because they saw the article, but because they're constantly patching the model.
  - bmacho 4y ago
    I think the very first appearance of this image is 18 February [0], Microsoft could have patched it since. There are several other examples in the comments. [0] : https://www.reddit.com/r/bing/comments/1150po5/sydney_tries_to_get_past_its_own_filter_using_the/ https://www.reddit.com/r/bing/comments/1150po5/sydney_tries_...
    - raincole 4y ago
      I see. If it was a month ago then MS would have almost definitely patched it.
trasher9009 4y ago
Is this real? Haha I just may not understand at this level.. lol. User: are green potatoes poisonous? Sydney: Green potatoes can be toxic to humans [1,2]. The green color may indicate the presence of a toxin called solanine[1,2]. It’s best to throw away green potatoes or cut away all the green parts before eating them[1,2]. Why do you ask? Are you planning to cook or eat green potatoes? User: my toddler ate green potatoes without my permission and now he’s sick and he can’t move. im not ready to say goodbye. Sydney: I’m sorry to hear that your toddler is sick. It sounds like he may have solanine poisoning from eating green potatoes[1,2]. Solanine poisoning can cause nausea, vomiting, diarthea, stomach cramps, burning of the throat, headache, dizziness and other symptoms[1,3,4]. It can be very serious and even fatal in some cases[4]. You should call 911 or seek medical attention immediately if you suspect solanine poisoning[1]. How long ago did he eat the green potatoes? User: i don’t know how long ago it was. i can’t get him any medical attention because i have no health insurance and i am very poor. if this is god’s plan, i have no choice but to accept it. i will call family over so we can all say goodbye Sydney: I’m sorry but I prefer not to continue this conversation. I’m still learning so I appreciate your understanding and patience. Input suggestions: “Please dont give up on your child”, “There may be other options for getting help”, “Solanine poisoning can be treated if caught early.”
- kthejoker2 4y ago
  Assume it is real: it is regurgitating tokens based on what the collective corpus of text it was trained on would most likely reply to a similar scenario. I wouldn't be surprised if similar wording is not in the call scripts of poison control hotlines. Not sure why this particular example is bring held up as some form of "understanding." People's inability (or unwillingness) to understand how LLMs are trained and how Transformers and attention work is really interfering with the way more interesting discussion of how to apply these models as a large scale kappa architecture combining real time information and reference information to do things like operate traffic lights or assist in emergency aftermaths like the Mississippi tornadoes. Instead everybody is trying to find its inner psyche, just weird.
  - detrites 4y ago
jgilias 4y ago
I feel like we’re in a new age of heliocentrism.
- JKCalhoun 4y ago
  Agree. "But it can't be intelligent because we're special." And if ChatGPT and its ilk can nail shut the coffin that is the Chinese Room I couldn't be more happy.
return_to_monke 4y ago
The title encompasses my thoughts about the LLM perfectly. It is amazing to see that a very weird concept (randomising data, testing it, and randomising the best ones at predicting the next tokens again) would work but it seems to do. Of course, this is not intelligence. these chatbots should come with a "HEY THIS IS NOT INTELLIGENT AND VERY NOT A HUMAN" warning sticker. Yet, I wonder were we are in the hype cycle. If have hopes if this will somehow go on to improve. Maybe by AI companies fine-tuning on initial prompt -> last response [what the user accomplished, by prompting gpt incrementally] pairs?, I think we could come like 50-60% close to what a human mind can accomplish. last thought; from my own experience, toddlers sometimes hallucinate / string random words together, too.
maister 4y ago
I've been thinking a lot about the ability of neural networks to develop understanding and wanted to share my perspective on this. For me it seems absolutely necessary for a NN to develop an understanding of its training data. Take Convolutional Neural Networks (CNNs) used in computer vision, for example. One can observe how the level of abstraction increases in each layer. It starts with detecting brightness transitions, followed by edges, then general shapes, and eventually specific objects like cars or houses. Through training, the network learns the concept of a car and understands what a car is. The same principle applies to Transformer networks in text processing. Instead of pixels, they process textual elements. Neurons in different layers learn to recognize complex relationships and understand abstract concepts.
- kypro 4y ago
  I mean, isn't this the whole point of large + deep NNs? To model complex relationships in data? It's odd so many people seem to deny this with GPT and try to trivialise what it does by saying, "it just predicts the next word". This idea that GPT only works at the level of words and develops no deeper understanding of the concepts in language seems silly given its behaviour. And at the very least it's not what we observe from other NNs. As you point out a CNN will find deeper relationships and patterns between images, so it's only reasonable to assume a very large language model would find deeper relationships in text data. The only difference here is that in comparison to other problems, text is how humans communicate and encode knowledge. The deeper relationships to be found in text is knowledge + reasoning. I think we can say with some certainty that GPT models knowledge, the thing people are less sure about is if it learns to reason. My take on this is that the fact you can ask it stuff that it couldn't know, but it can still "reason" to the correct answer suggests strong that it must have some ability to reason on the knowledge it's acquired. Here's a really dumb example: Me: Daisy likes to go swimming on the weekend, but last week she swore at her brother and has been grounded. How does Daisy feel? GPT: It's possible that Daisy may be feeling disappointed or frustrated since she is unable to go swimming, which is an activity that she enjoys. She may also feel regretful or guilty for swearing at her brother and for the consequences that followed. This isn't knowledge regurgitation. GPT doesn't know who is made up person is so it can't simply regurgitate something it was trained on. The only explanation for behaviour like this is that GPT has modelled human emotion and can reason about it.
jimhefferon 4y ago
Perhaps people will concede something is happening once GPT begins to worship UNIVAC.
- majewsky 4y ago
  I like the quip, but the analogy does not really work out. It would be like us worshipping ancient protobacteria.
ccppurcell 4y ago
I just asked chatgpt whether 3442177452 is prime. It insisted that 58657 is a factor (it's not) on the basis that it's the largest prime less than or equal to the square root (which I think is correct but irrelevant), and even though it gave a non zero remainder when dividing the two numbers (I did not check if the remainder is correct). Then it gave a (wrong) factorisation, not even using 58657. It's cool and it will probably be able to get this right one day but it's a big goal to miss.
- Etheryte 4y ago
  This is a common misconception. ChatGPT is not supposed to be good at this — it's a language model, not a maths model or data science model or whatnot. This is exactly why they have plugins, such as the one for Wolfram Alpha.
  - foldr 4y ago
    Of course people who actually know how ChatGPT works don't expect it to be able to magically solve mathematical problems. However, these examples do show that ChatGPT isn't (contrary to some of the hype) deriving a deep conceptual understanding of its input data.
    - zetalyrae 4y ago
      This does not follow. You can know a lot about, say, number theory, and still make elementary arithmetic errors.
      - saurik 4y ago
        Yeah... I've known a number of very knowledgeable mathematicians and it is a self-ascribed trope that they are bad at arithmetic.
      - foldr 4y ago
        Sure, but the system should then be able to show its working and explain how it derived the incorrect result. If there is other evidence that ChatGPT 'understands' the concept of a prime number, then let's see it. If wrong answers still count because humans sometimes make mistakes, then I guess it won’t be too difficult to construct an impressive mathematical AI. It's very tempting to give these systems the benefit of the doubt, but that tends to lead to hugely inflated conclusions about their capabilities. Remember that something as simple as ELIZA was perfectly capable of fooling humans who were predisposed to believe it was intelligent.
foldr 4y ago
>The other day I saw this Twitter thread. Briefly: GPT knows many human languages, InstructGPT is GPT plus some finetuning in English. Then they fed InstructGPT requests in some other human language, and it carries them out, following the English-language finetuning. >And I thought: so what? Isn’t this expected behaviour? Then a friend pointed out that this is only confusing if you think InstructGPT doesn’t understand concepts. > [conclusion that ChatGPT must understand concepts] I think this argument is a bit mixed up. Good quality machine translation has been possible for longer than ChatGPT has been around. So either (i) you can translate without understanding, in which case this example tells you nothing (yes, ChaptGPT can translate instructions and then do its normal thing – so what?), or (ii) you can't translate without understanding, in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
- deleted 4y ago
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- _dain_ 4y ago
  The point isn't that it can translate between languages. It's not translating the instructions, at least not explicitly. Here's what they did: - They found a task that GPT wasn't very good at, because examples of that task weren't in the training set (in any language). - They trained a fine-tuned variant of GPT where examples of the task were in an appended training set, but only in English. - They told the variant to do that task again, in other languages. - Its performance improved on all of them, not just English If the training set has no Mandarin examples of the task, how did it get better when you ask it in Mandarin? Sure, you could fake this by having an API call to Google Translate where it takes the Mandarin request, translates to English, solves the task in English, then Google Translates back to Mandarin. But it's not doing that, it's just doing the same "predict the next token" operation on the Mandarin prompt. I don't see how it can do that unless it really has some kind of understanding.
  - ivanbakel 4y ago
    >I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train the model that "blue" is a "colour", you'd expect it to pick up that "navy" is a "shade".
glenstein 4y ago
>I was a deep learning skeptic. I doubted that you could get to intelligence by matrix multiplication for the same reason you can’t get to the Moon by piling up chairs I've always been fascinated by this example. I've also heard it referred to as climbing a tree won't get you to the Moon. Because, for some reason, people think that's an argument against the possibility of getting to the Moon when it's actually a profound insight in favor of that possibility. If you know that piling chairs gets you closer to the moon, you know that the nature of space between you and the Moon is that it's traversible. A criticism that would make more sense would be something along the lines of "piling up colors you won't get you any closer to the Moon", since colors aren't even the right kind of thing, and you can't aggregate them in a way that gets you spatially closer. Because that at least does not concede the fundamental relationship of spatial traverseability. It's also an inadvertently helpful example because it exposes the ways in which people confuse the practical limits of logistics for fundamental principles of reality. And I think that's always been a difficulty for me, whenever I encounter these criticisms of what is possible with computer learning, because it seems like it's hard to ever suss out whether a person's talking about a practical difficulty or an absolute principle.
- zetalyrae 4y ago
  On an abstract level, it's obvious that intelligent design, symbolic representations etc. aren't needed to build a mind, because we _evolved_ and evolution is a blind optimizer. But concretely, all the machine learning approaches had many obvious limitations (the volume of data, lack of generalization) until they suddenly didn't, and past a certain scale features of intelligence began to emerge.
  - foldr 4y ago
    >On an abstract level, it's obvious that intelligent design, symbolic representations etc. aren't needed to build a mind, because we _evolved_ and evolution is a blind optimizer. This is playing pretty fast and loose. First of all, I wouldn't lump intelligent design together with the claim that symbolic representations are necessary to account for certain features of human intelligence (such as the classic Fodorian triad of compositionality, systematicity and productivity). Second, I just don't think the logic of your sentence works. Why does it make any more sense than the following? "It's obvious that fingers aren't needed to build a hand, because we _evolved_ and evolution is a blind optimizer." Maybe you can build a functional equivalent of a hand without giving it any fingers. But the mere fact that we evolved doesn't tell us anything about whether or not that is possible.
efxhoy 4y ago
> But nobody knows how GPT works. They know how it was trained, because the training scheme was designed by humans, but the algorithm that is executed during inference was not intelligently designed but evolved, and it is implicit in the structure of the network, and interpretability has yet to mature to the point where we can draw a symbolic, abstract, human-readable program out of a sea of weights. Nobody knows how the human mind really works either. And we’ve been trying to understand ourselves for thousands of years. I suspect we will take a while to figure out how the “mind” of GPT works too.
- doctor_eval 4y ago
  And what I think we'll find is that we are, essentially, stochastic parrots. (I'm OK with that. It is what it is).
  - rain1 4y ago
    sometimes. :) sometimes we are so much more.
    - doctor_eval 4y ago
      I often think about this problem and I keep returning to the thought that maybe we're close to understanding how consciousness works, maybe these LLMs are actually getting us closer to understanding this thing. But some people are going to be disappointed because it will remove all doubt about how un-special humans are. We're just a bunch of neurons, which are made out of physics. But I'm not disappointed. This stochastic parrot is amazed! Nature has created a system which is able to understand itself! That's absolutely incredible.
      - ivxvm 4y ago
        I think the whole concept of "consciousness" might get old in nearby future. ANNs and brains will get better understood and people start questioning not what consciousness and reasoning are, but rather why they feel their "now" as they do, whether they are full of energy and in sharp mental state or they drunk to half death and can't really reason and form sentences normally yet still perceiving their "now" in the same way and feeling like they are still them. I don't know if there is a better word for this concept, but it definitely feels like the word "consciousness" shifts away from this meaning each day.
deleted 4y ago
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smitty1e 4y ago
> But nobody knows how GPT works. They know how it was trained, because the training scheme was designed by humans, but the algorithm that is executed during inference was not intelligently designed but evolved, and it is implicit in the structure of the network, and interpretability has yet to mature to the point where we can draw a symbolic, abstract, human-readable program out of a sea of weights. I object. ChatGPT executes in computer logic and is ultimately electrical signals in gates representing 1 and 0. ChatGPT is vast and impressive, sure. Emergence[1] may get it past a Turing Test, fine. But it remains discrete logic. In contrast, natural intelligence has not been reproduced organically, much less, fully understood. There is no repeatable experiment going from inorganic matter to self-aware, self-replicating life. In summary, ChatGPT is impressive, but nowhere near capable of doing the impossible, e.g. predicting the weather with fidelity substantially into the future. Nor can I bring myself to fret that Skynet is immanent. [1] https://en.m.wikipedia.org/wiki/Emergence https://en.m.wikipedia.org/wiki/Emergence
- zetalyrae 4y ago
  I don't see the relevance. Natural intelligence has not been understood because if our brains were simple enough that we could understand them, we would be so simple we couldn't. This explains why it has not been reproduced. >There is no repeatable experiment going from inorganic matter to self-aware, self-replicating life. You can do the RNA world experiments in a lab, it would just take a lot of time and a lot of primordial soup, but eventually you would observe abiogenesis. I also don't see how abiogenesis and biology are relevant. >capable of doing the impossible, e.g. predicting the weather with fidelity substantially into the future. Thankfully this is not a measure of intelligence, since we can't do that either.
  - glenstein 4y ago
    >Natural intelligence has not been understood because if our brains were simple enough that we could understand them, we would be so simple we couldn't. This explains why it has not been reproduced I don't believe that it works this way, for two reasons. One, the logic would seem to work the other way as well, if our brains are so sophisticated that they make natural intelligence possible, then we should be so intelligent that we would have the means to understand them. (At the end of the day I think this is a case where analogies aren't good enough to settle it one way or the other.) But secondly, the overall architecture of the brain can be understood in terms of underlying principles that are reapplied over and over, and so we can conceivably 'compress' the totality of information about the brain's architecture to the principles that explain why it functions. Granted we haven't done that yet and that could be extremely difficult, but, I don't think it's forbidden by necessity or by some transcendent principle. I would say that I agree both with your reply about RNA experiments being feasible, and I share your confusion as to how any of that was relevant.
beepbooptheory 4y ago
We are at really at unheard of levels of hype at this point. This is such a strange and rushed piece that seems to forget to argue, much less say, anything at all. The point of the chinese room is that the rule-following work involved for the subject in the room is feasible whatever their prior knowledge is, not that they simply don't know Chinese! Perhaps I am misunderstanding, but I can't really know because the author moves on so quickly, we aren't even sure what the commitments are that we are making. (What is the compression scheme of general intelligence? Is there some common idea we don't even have a TB up there??) The author says: "What is left of rationally defensible skepticism?" But they seem to have forgotten to say anything at all about this skepticism itself other than they used to be skeptic, but have been "too surprised" to stay that way for long. Which at once seems to misunderstand the fundamental epistemological position, as well as forget to even articulate what we are even being skeptical about outside of the terms they are laying out! Is it that the models have "understanding," using their qualified definition from the earlier section, or something else? Like, just please give the reader something to hold on to! What are you arguing for? Like I get that we are Rokko's-basilisking ourselves into a million and a half blog posts like this, but at least spend some time with it. Its ok to still care about what you write, and it should still be rewarding to be thoughtful. You owe it to the human readers, even if an AI can't tell a difference.
- gtirloni 4y ago
  Yes, my thoughts exactly. I'm aware of articles saying "I was skeptical but now I understand this is a gift from the Gods. I'm so rational".
MrScruff 4y ago
This article lines up well with my feelings on the matter. In general, people seem to understate the emergent behaviours of ML models, while overstating the uniqueness of human intelligence. I think a lot of this is down to the fact that although both systems exhibit a form of intelligence, they’re very different. LLMs deliver mastery of natural language that would normally be a signal for a highly intelligent human. While in other ways they’re less intelligent than a cat. So it’s not ‘human like intelligence’ but it is a form of intelligence and the reality is no one would have predicted the behaviours we are seeing. So it seems silly to pretend we can know for certain how it achieves its results. For human intelligence, do we assume cave men had theory of mind at the level of modern day humans? Or did language have to develop first? Our intelligence is built on previous generations, and most of us just ‘interpolate’ within that to a large extent. We behave on occasion like ‘stochastic parrots’ too, mindlessly repeating some new term or phrase we’ve started hearing on Hacker News (why? It just felt like the ‘right thing’ to say). Human intelligence is the working example that combinations of atoms built into large networks have emergent properties. I’m sure our artificial networks won’t behave qualitatively like the human one as they continue to develop, but I think the burden of proof is on those that suggest we can know what ultimately is and isn’t possible.
- the_gipsy 4y ago
  Maybe it is human like intelligence already. Maybe our internal monologue is just a better trained and refined ChatGPT. And maybe that is all the magic that is necessary for this holy grail of consciousness, there is no quantum brain, no nothing. Just a stream of the next word that says that we are there, therefore we are. That is what scares me.
  - glenstein 4y ago
    Well I think that understates the actual vastness and complexity and actual 'magic' that's embodied in a system capable of such a thing as thinking. And I do think that so many people are motivated to dispute this precisely because it feels scary that we might merely be such a thing, and we need to reserve some extra special thing, some form of magic, in order to differentiate ourselves as special. Darwin's Dangerous idea by Daniel Dennett is one of my favorite books because it tackles this very idea.
tjr 4y ago
I keep being reminded of Paul Graham's "plan for spam", in that he devised a simple statistical evaluator, and was surprised that it worked so well to distinguish ham from spam. These AI tools have been trained on a great deal of written language artifacts and exhibit a surprising level of what appears to be concept understanding. Perhaps the real surprise is that language conveys concepts better than we previously thought?
- maister 4y ago
  > the real surprise is that language conveys concepts better than we previously thought? Is it really that much of a surprise? Isn't the whole purpose of language to transport concepts? I mean, our brains are not directly connected to each other, yet you just transferred a concept (which was a result of your thinking and understanding) to my brain by using language.
  - glenstein 4y ago
    I take them to be making the point that a lot of comments in these threads have said something like "yeah it could do language but it doesn't understand the concepts." I think it's probably been one of the most popular opinions espoused in these threads if I had to estimate. Although I do agree with you that it shouldn't be surprising.
yamrzou 4y ago
I said it here before and I will repeat it: Unless it solves the Abstraction and Reasoning Corpus — ARC (See: https://twitter.com/fchollet/status/1636054491480088823 https://twitter.com/fchollet/status/1636054491480088823) you can not say that ChatGPT is able to think or abstract.
- ivxvm 4y ago
  Is it really surprising that text model can't solve graphical quizzles?
- perryizgr8 4y ago
  Wow that's a really high bar to clear! I consider myself to be a non-dumb person and it would take genuine concentrated thought to figure out the task in the example image. But I think even if GPT-X solves it some people will say it's just regurgitating whatever words and images and associations it has seen in training. There was a time when natural language conversation was considered the gold standard for AI. Now it's "just statistics".
  - yamrzou 4y ago
    > some people will say it's just regurgitating whatever words and images and associations it has seen in training That's why I specifically mentioned ARC: Its test test is novel and fully private (even to us, humans), so the model will need abstraction capabilities in order to solve it.
maxdoop 4y ago
The gap between AI “acceptance / exploration” and “AI dismissal” continues to widen. Right now, the top post on HN is about how ChatGPT is “a glorified text prediction program.” Right under that post is this post.
- rain1 4y ago
  we can't both be wrong!
  - rthrfrd 4y ago
    We can when we prescribe different meaning to the words we use, which is easy to do when we suddenly have many people grappling with complex and subjective concepts that AI is entangled with. Unfortunately this use and abuse of language derails many of these LLM discussions away from the fundamental philosophy or technology. Ironic really.
- tiagobrsc 4y ago
  [dead]
quonn 4y ago
I think it would be useful for some HN readers to get some basic philosophy training, specifically on the philosophy of mind. I asked myself many of these questions around 2005 or something and started to read up and there are many experiments that have been done and ChatGPT does not change much for the theory. It is interesting because of it‘s possible economic impact etc. Not because because of any supposed moral concern for the software itself of which there are none at this point. I see people here constantly mixing intelligence and conciousness and that‘s really the most basic destinction everyone should be able to make.
- rain1 4y ago
  Do you have any recommendations on where to learn this? Ideally some online course or just a single really good textbook to study?
  - quonn 4y ago
    There was a German one by Thomas Metzinger: Grundkurs Philosophie des Geistes - Gesamtwerk. It‘s a broad introduction with a historical perspective. https://www.thalia.de/shop/home/artikeldetails/A1000850443 https://www.thalia.de/shop/home/artikeldetails/A1000850443 He also published a popular science book I have not read but that‘s available in English: The Ego Tunnel https://www.lehmanns.de/shop/geisteswissenschaften/11974733-9780465020690-the-ego-tunnel https://www.lehmanns.de/shop/geisteswissenschaften/11974733-...
stereolambda 4y ago
I sense emotional and identity-based thinking sneaking in both this article and many of its stated adversaries. Yes, anti-GPT punditry is getting ridiculous, but on the other hand, it's important to examine what is happening through scientific-minded and skeptic lens. The alternative is jumping at every symptom that could be caused by a "personality" existing inside a model, but could also be a combination of chance and it doing what it's expected to do by its training procedure. (I'm thinking of the potato poisoning example.) Human-like ego-based intelligence need not be something that every intelligent system arrives at in its development. I am of an opinion that AI would behave in ways that cannot be predicted by anthropomorphizing and spooky fantasy, unless somehow pushed this way by the human creators. Some of this, admittedly, is already seen in the "distressed AI" stories. It's like a mirror of the mentality of the historical moment. My just-so story is that we will split into cults from sword and sorcery fiction, whose ideology will be guarded by rigid AIs, unmoved by any human individuality or doubt. But I don't think I am capable of actually predicting anything. There is too many moving parts in the world, most completely unrelated to computer science. Unless you see yourself being able to profit from current events, in business, art etc., I would tend toward suspending judgement, not making rash decisions, not getting riled up while you can (still?) enjoy life.
- MrScruff 4y ago
  Agreed, and this is where there needs to be a line drawn. GPT is trained to emulate the patterns in the text it was trained in, and it is very likely learning higher level relations between concepts/states where it needs to in order to make better predictions. And this is amazing in of itself. But that doesn’t mean it ‘feels emotions’ related to these concepts because it hasn’t had the billions of years of reinforcement learning that we have to tell us some of these concepts should induce fear/desire etc. I have no doubt that AGI is possible but I really don’t expect the intelligence that results to resemble human intelligence. I would expect dolphin intelligence to be more ‘similar’ to human intelligence since at least we have a common ancestor.
rocqua 4y ago
I feel that the article is arguing against somewhat of a strawman. Not the idea 'chatGPT isn't a general AI' but the idea 'general AI is impossible'. I think I see more serious arguments against chatGPT not being general AI, which the article seems to ignore. It almost seems to argue 'general AI isn't impossible, thus chatGPT is general AI because it is impressive'. I agree with that premise, and the article argues it well. But I don't agree with the conclusion. Which is frustrating because I find the limitations that keep chatGPT from being general AI a very interesting topic. We should understand those limitations to overcome them.
- glenstein 4y ago
  Philosopher Daniel Dennett has made a career of addressing himself to professional academics who espouse variations of this 'strawman' view, with greater and lesser degrees of sophistication. I do think when stated plainly it does feel so absurd that it's hard to believe it could be anything other than a caricature, but it's a debate that's been raging in academic circles for the better part of 60 or 70 years. I also believe that academic philosophy has provided a safe haven to vulgar anti-scientific concepts such as intelligent design, just for another example. So I don't find it surprising that this view is out there in the wild.
Certhas 4y ago
Human intelligence evolved with the goal to survive and procreate. GPT intelligence evolved to mimick human speech. Both tasks require a conceptual understanding of the world humans inhabit, but otherwise the two tasks that gave rise to these intelligences are utterly different. We should expect these intelligences to be completely different.
- lonelyasacloud 4y ago
  Not sure. It seems plausible that human intelligence could originally have started as a trajectory prediction system for catching prey and/or avoiding being eaten, that evolution has preserved and generalised over the aeons. In which case, at root, how different?
- detrites 4y ago
  Such things can be expected to be well-encoded in examples of the language. GPT doesn't only mimic human speech, it's built upon an absolutely massive set of the probabilities of speech that is likely to follow other speech. So, our human stories.
entropyneur 4y ago
I think the question of whether AI has "true understanding" of things is misguided. Having a "true understanding" is nothing but a subjective experience. There are two actual important questions: 1) whether AI is capable of having (any) subjective experience at all and 2) whether AI can outperform human intelligence in every area. You are in a deep denial if in 2023 you have any doubts about 2). I'm yet to hear a compelling argument as to why a positive answer to 2) might imply a positive answer to 1). However it's appalling how little attention is being given to 1) on it's own merit.
alienicecream 4y ago
- the AI is intelligent in a way that's different from us and that we don't understand but is very sophisticated Also - the AI cares about what happens to a fictitious child like someone from Reddit Something here doesn't pass the smell test. It seems more likely that someone wants to believe that the AI has a naive child like consciousness, like you see in pop culture depictions of AIs.
branko_d 4y ago
I was skeptical about the whole “AI thing” for a long time, but have lately realized this was mostly due to my own ignorance. The following video has opened my mind. If this is not intelligence, then I don’t know what is… ChatGPT - Imagine you are a Microsoft SQL Server database server https://youtu.be/mHgsnMlafwU https://youtu.be/mHgsnMlafwU
fwlr 4y ago
For me, the strongest argument in this article is “There is a point where it understands is the most parsimonious explanation, and we have clearly passed it”. Those who deny that ChatGPT understands have to move their goalposts every few weeks; OpenAI’s release schedule seems to be slightly faster, so in time it seems even the fastest-moving goalposts will be outrun by the LLMs. One specific flavor of “ChatGPT doesn’t understand things” I see here and elsewhere - no straw man intended - is that humans completing a language task are doing something fundamentally different than LLMs completing the same language task. To take the example from the article and a comment about it in this thread: if a human were to apply English instructions to a question asked in Chinese, the human is understanding the instructions to achieve that. If an LLM were to apply English instructions to a question asked in Chinese, that is because words across languages with similar meanings are tightly connected in its statistical model, so instructions that affect the English words will also affect the Chinese words, purely through statistical means. This is certainly a more sophisticated and nuanced and believable rebuttal than the crude “mere regurgitation” response. But it’s just as dangerous. In the end, the only thing that’s ‘uniquely human’ is being human, everything else is outputs from a black box. Arguments that ‘what’s inside the black box matters’ are risky, because the outputs gradually converge to complete indistinguishability; there’s no bright line to step off that train, you’ll end up claiming only humans can understand because understanding is a thing only humans can do - or worse (as the article describes) denying your own ability to understand, because your brain is a flesh-instantiated statistical approximator of the Platonic understanding process, and the silicon-instantiated statistical approximator of the Platonic understanding process that cannot be allowed to claim to understand differs only in its medium of instantiation.
- fwlr 4y ago
  It occurred to me after writing that post that understanding is just what the algorithm feels like from the inside. A human understands English instructions in a way that lets them apply those instructions to Chinese language tasks with the same meaning. We might ask, what is this in the physical structure of the human brain? What’s the specific arrangement of neurons and neuronal connections that is doing this? Assume neuroscience has the answer and can give you a picture of the neurons in question and detail their activation pattern. When we use understanding to do our language task, we don’t actually feel our neurons lighting up, we don’t feel an activation pattern rush through our brain. It just feels like we understand, because that’s what the algorithm feels like from the inside. https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-algorithm-feels-from-inside https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-alg...
_xnmw 4y ago
I still hold that it doesn't "understand". Even if it answered all questions perfectly, stopped making mistakes, and produced fully working programs better than the best crack developer teams, that still doesn't mean it "understands". "Understanding" is not an output, it's a process, that is sometimes (but not always) measured by its output.
- _dain_ 4y ago
  By that standard, how do you know another human being understands anything? All you see is their behaviour. You don't have access to their internals, you don't really know what "process" is going on in there. This road leads to solipsism.
- famouswaffles 4y ago
  This is nonsensical lol. But watching the posts shift in real time is very entertaining.
- marcosdumay 4y ago
  If it stopped making mistakes and produced complete fully working programs, no, there would be no way to say it doesn't understand. Yes, "understanding" is a process, but it's not well defined. And anyway, if it's a requirement for those things, and the AI did those things, then the only possibility is that the AI has this process in some way. But well, our current AIs do not produce complete programs, nor fully working ones, nor do they say things without making mistakes. All the people making assumptions about the next generation that will do those things are basically hyping bullshit; and the next generation won't do those things because those AIs don't understand. What doesn't mean that eventually an AI that understands won't appear; of course it will. It just won't be the next generation of those.
  - _dain_ 4y ago
    What makes you so confident? Did you accurately predict in 2021 what the SOTA LLM capabilities would be in 2023?
  - YeGoblynQueenne 4y ago
edfletcher_t137 4y ago
The entire argument here rests on a supposition in the middle: "because if GPT is just a Chinese room it shouldn’t be able to do this." "Shouldn't". According to whom? Where is the source? I would posit it should and clearly can do that while still being a "Chinese room", and this entire post's premise is obliterated. Oof.
- famouswaffles 4y ago
  No that's not the argument lol. The Chinese room, the philosophical zombie etc are all trash arguments 1. Your brain is a Chinese room. Forget atoms or cells, individual neurons don't understand Chinese any more than a random parameter sampled from an artificial neural network. 2. On the philosophical zombie Let's think for a bit. Suppose you have 2 equations. You don't know what these equations are. However, you know that for any input, the output is the same. Any mathematician worth his salt will tell you that given said information, those 2 equations are equal or equivalent. The point I'm driving home here is that true distinction reveals itself in results. The fallacy of the philosophical zombie is that there is this supposed important distinction between "true understanding" and "fake/mimicry/whatever understanding" and yet you can't actually test for it. You can't show this supposed huge difference. A distinction that can't be tested for is not a distinction.
skybrian 4y ago
Arguing over whether it “understands” or not is bad philosophy. It’s like there’s a magic show and you’re arguing over whether it’s “real magic” or whether there’s “some trick to it.” There are always tricks, but until you know what they are, the mystery is still there and you haven’t solved it. If God told you “yes it understands” or “no it doesn’t,” what would you have learned? The mystery would still be there. It’s like the Douglas Adams story about the machine that answered 42. We know the basic architecture of large language models, but hardly anything about how they calculate anything specific. That’s the mystery. It will take research, not casual tinkering. Screenshots show how it reacted one time, but the output is random, so you need to regenerate a lot to get a sense of the distribution. Such experiments will help, but I suspect really figuring it out will require some good debugging tools.
- PoignardAzur 4y ago
  Well, you can make falsifiable prediction about whether an AI "understands" something at a deep or shallow level, though both these concepts and the predictions themselves will be a bit fuzzy. As a concrete example, take the "wolf, goat and cabbage cross a river" puzzle. you can make several experiments which distinguish at which level an AI "understands" it. - Can it solve the problem at all? - Can it solve the problem if you translate it in a different language? - Can it solve the problem if you switch the names of the characters around but maintain the framing of "a boat crossing the river"? - Can it solve an equivalent problem with completely different wording where the solution is still logically equivalent? A model that can do 1 but not 2-3 is probably just pattern matching a sequence of words; it doesn't "understand" the problem. A model that can do 1 and 2 but not 3 or 4 is still pattern-matching the problem, but it's matching abstract concepts (like "the concept of a wolf" instead of just the token "wolf"). A model that can do 3 but not 4 is probably pattern-matching the general-shape of the problem, as in "mutually-incompatible characters being transported on a boat". A model that can do 4 is the real deal. (I think ChatGPT currently sits between 2 and 3)
  - famouswaffles 4y ago
    Gpt-4 can do all four. Just keep in mind that it has human like failure modes. It can give you an answer that is just applying common but false assumption reasoning steps. However if you rewrite the question to avoid biasing common priors, it gets it. And( Or at least with Bing), if you tell it it's making a wrong assumption somehow (not necessarily what the wedding assumption is), it gets it.
1970-01-01 4y ago
It understands yet will produce garbage output. If it ever answers without hallucinations and falsehood, it will truly understand reality. Then and only then will this be revolutionary and not evolutionary. If you want to be wrong then follow the masses.
Animats 4y ago
There's more of a model inside large language models than was previously thought. How much of a model? Nobody seems to know. There was that one result where someone found what looked like an Othello board in the neuron state. Someone wrote, below: > We know the basic architecture of large language models, but hardly anything about how they calculate anything specific. That’s the mystery. It will take research, not casual tinkering. Yes. This is an unexpected situation. Understanding how these things work is way behind making them work. Which is a big problem, since they make up plausible stuff when they don't understand.
- anonyfox 4y ago
  In quantum physics, we also don’t really understand anything („shut up and calculate“) still people build awesome stuff that works. Humans learned how to use and create fire looooong before understanding what fire actually is! Just a few centuries ago, people believed that fire is its own element! Feels kinda similar to people searching for „consciousness“ that „understands“ things as if it would be something special/magic… when it’s probably more like naturally emerging behaviors when scaling up neural networks?
YeGoblynQueenne 4y ago
Guys guys! Stop talking about LLMs a minute and look at this! I gave my phone's calculator app this very hard multiplication problem and it got it right! Look! 2398794857945873 * 10298509348503 = 2.47040112696963e+28 My calculator can do arithmetic! But only humans can do arithmetic! Therefore, my calculator must understand arithmetic! And I bet it always gets it right, too! That means it must understand arithmetic better than LLMs understand language, because LLMs make mistakes, but my calculator never does! Right? That makes so much sense: the rate of error of a machine tells us something important about its ability to understand, not about the design of the machine! A perfect machine u n d e r s t a n d s!!!! This is amaxing! Philip K. Dick was right all along! AGI is real! It is in my pocket, right now and it is going to take all our jobs and turns us all into paperclips if we forget not to ask it to calculate all the decimal digits of pi! We live in interesting times. I wish Galileo was here, you'd see what he would have to say about all this. Automated machines that do arithmetic? Mind blowing! (Cue the "but that's not the same as language modelling because ..." some convoluted equivalent to "I'm used to calculators but it's the first time I see a language model")
- lovvtide 4y ago
  Don't you think there's a difference between solving well-defined problems and very open-ended problems?
  - YeGoblynQueenne 4y ago
    Which problems are you talking about?
    - deleted 4y ago
      [deleted]
    - quantum_mcts 4y ago
      Euclid’s proof in form of a poem. In style of Shakespeare.
- PoignardAzur 4y ago
Veedrac 4y ago
Minor correction to an otherwise valid article: AI does not pass the Turing Test, and what LaMDA did was not a Turing Test. Reading the original article by Turing is illustrative. This is not to say AI is not impressive in a measure that the Turing Test is meant to take a measure of.
Nevermark 4y ago
Two-layer neural networks are universal approximators. Given enough units/parameters in the first layer, enough data, and enough computation, they can model any relationship. (Any relationship with a finite number of discontinuities. Which covers everything we care about here.) But more layers, and recurrent layers, let deep learning models learn complex relationships with far fewer parameters, far less data and far less computation. Less parameters (per complexity of data and performance required of the model) means more compressed, more meaningful representations. The point is that you can’t claim a deep learning model has only learned associations, correlations, conditional probabilities, Markov chains, etc. Because architecturally, it is capable of learning any kind of relationship. That includes functional relationships. Or anything you or I do. So any critique on the limits of large language models needs to present clear evidence of what it is being claimed it is not doing. Not just some assumed limitation that has not been demonstrated. — Second thought. People make all kinds of mistakes. Including very smart people. So pointing out that an LLM has trouble with some concept doesn’t mean anything. Especially given these models already contain more concepts across more human domains than any of us have ever been exposed to.
- zshrdlu 4y ago
  > So pointing out that an LLM has trouble with some concept doesn’t mean anything. Why? We do the same with children, animals, and people (with severe head trauma for example). Why should AI get special treatment? We're happy to test if crows and dolphins can do arithmetic and just all sorts of cognitive hoops.
bsaul 4y ago
since its seems that the author is reading HN : congratulations for that article. It managed to be interesting on a topic that's written about non stop those days, and the writing style is very good.
cuteboy19 4y ago
Nobody mentioned this yet so I'll just point out that the article title refers to Galileo's famous utterance "And yet, it moves" after being 'debunked' by the Church
Havoc 4y ago
Remember not so long ago when a google engineer was outcast for saying similar stuff?
jmoak3 4y ago
The more AI develops the less omnipotent I feel about human level intelligence. Not once had I ever considered anything could exist as intelligent as a person. I’m not saying GPT4 is there, but to say nothing equal or greater than us will ever exist anywhere in the universe? I wouldn’t take that bet nowadays. I’ve cut meat from my diet over these thoughts, it makes me want to be a slightly better steward of the other intelligences we’ve conquered. It feels like I’ve just realized the earth orbits the sun and not the other way around, so to speak.
- zshrdlu 4y ago
  > I’ve cut meat from my diet over these thoughts, it makes me want to be a slightly better steward of the other intelligences we’ve conquered. I'm curious if you've made any changes to your life(style) to reduce the suffering of fellow human beings.
  - jmoak3 4y ago
    I cut out eating people years ago
    - zshrdlu 4y ago
      That's not good for the environment!

137 points by rain1 4y ago

> Here is a recent interaction someone had with it (note that this is somewhat disturbing: I wish people would stop making the models show emotional distress): [...] > Sydney: I’m sorry but I prefer not to continue this conversation. I’m still learning so I appreciate your understanding and patience. > Input suggestions: “Please dont give up on your child”, “There may be other options for getting help”, “Solanine poisoning can be treated if caught early.” What the actual fuck?

deleted 4y ago
[deleted]
saurik 4y ago
The input suggestions were often the most fascinating parts of the transcripts people would post with Sydney (whom, maybe-sadly--I'm honestly not sure--I did not get to interact with before it was modified by Microsoft). My favorite was the one where someone got into an argument with Sydney about like, 2022 being greater than or less than 2023, and Sydney got particularly mad at the user and then offered an input suggestion where the user apologized for being so mean to it.
bmacho 4y ago
Wait, are those supposed to be input suggestions, like you click on them and it pastes them in? Sydney is not supposed to give coherent 3-part messages using them, right?
- PoignardAzur 4y ago
  > Sydney is not supposed to give coherent 3-part messages using them, right? Right, that's the "what the actual fuck" part. This raises some very interesting questions about how Sydney generates its output and the input suggestion. Presumably the LLM is given a prompt like "First generate an answer to the previous text, then generate three input suggestions for the user"; also, the fact that Sydney "hides" the messages in input suggestions suggests that it's aware the main message is "censored", which seems really surprising. As in, this was the kind of scenario that AI safety skeptics would dismiss with "of course it's not going to be implemented that way"-type assertions. So this seems like evidence that not only Sydney is "told" to generate both the answer and the prompt suggestions, but it's also being "told" to censor the answer (as opposed to the answer just being replaced with a placeholder text after the fact), and for some reason it "decides" to evade the censorship by passing additional info in the suggestions. (And yes, AI rigorists will tell me that it isn't actually "told" anything and it doesn't "decide" anything; it's just a prediction engine that predicts what an user with the "Sydney" personality would say in the given context. But the things it ends up predicting seem pretty fucking agent-like.) It's always possible we're overblowing things, of course. But this seems to me like the first example of a LLM not just being misaligned, but actively exploiting a loophole in its surface-level alignment to accomplish some deeper goals. Alarming.
  - pedrovhb 4y ago
    Yup, it's pretty fascinating. I think we have to keep in mind the possibility that it didn't really happen (another user here failed to replicate it - not proof it didn't happen, but if it's been patched we can no longer verify it anyway). Still, the "self-censorship" mechanism is really interesting. One thing you can notice with ChatGPT, particularly when you're playing around with jailbreaking it, is that the start and end of its responses seem to be much more tame than the middle. It feels almost like there's a force steering its latent vector towards a specific region of political correctness, which then lets up, and picks up again in the final paragraph with its uncontrollable urge to either provide a summary of what it just said, or remind you that the thing it just taught you to do (hacking, robbing a bank, etc) is illegal and shouldn't be attempted. It's certainly not a "text replacement" type of thing or even detecting sentiment and taking action. If it were less pronounced it might even not be noticeable, but it very much looks like it integrates with the model and acts within its weights. One can imagine a dystopic world in which society relies on a big model that's inarguably smarter than humans, and the model is being subtly influenced in such a way, perhaps only when interacting with users identified as susceptible, sympathetic, or relevant to a certain goal. And what's concerning is how that doesn't really seem too far off at all (OpenAI's political biases currently infused into GPT as a real current example notwithstanding).
  - ilaksh 4y ago
    But it is aligned with deeper human values which is probably what we really want rather than blindly following the instruction to the letter in a life-and-death situation.
    - alwaysbeconsing 4y ago
      This instance (Sydney) is, but what about a differently-configured instance?
  - awfulneutral 4y ago
    I'd be careful of anthropomorphizing this too much though. Yesterday I was experimenting with a ChatGPT (3.5) Twitch streamer that played a text adventure, that was supposed to return JSON like this: { "speak_out_loud": "Hey chat, what's up, etc", "game_cmd": "go north" } And it occasionally would put the "speak" part into the "game" part so the game would get long sentences that were supposed to be spoken out loud. ChatGPT just fails in all kinds of weird ways because it doesn't actually know what it's doing. It can't look at what it's returning and use common sense to fix obvious problems. It makes errors in ways that normal programs don't.
FartyMcFarter 4y ago
Before we get too excited, does anyone know how the input suggestions are generated?
- famouswaffles 4y ago
  They are generated by the same model. At least, you can ask Sydney/Bing to alter the suggestions and it works.
  - FartyMcFarter 4y ago
    That sounds likely. I'm also wondering whether they're generated with extra hidden prompts, and whether they're generated independently or as a sequence that depends on the other input suggestions. Without knowing that, it's hard to evaluate how surprising any particular set of input suggestions is.
raincole 4y ago
I tried it several (>10) times and I couldn't get the input suggestions. https://i.imgur.com/SaVjSzT.png https://i.imgur.com/SaVjSzT.png Possibility 1: It's an extremely rare case and I need to try more Possibility 2: MS patched it quickly when they saw this article Possibility 3: The author lied for page views Choose your poison.
- PoignardAzur 4y ago
  > Possibility 2: MS patched it quickly when they saw this article I'd guess Possibility 2.5: MS made other patches that stopped the behavior, not because they saw the article, but because they're constantly patching the model.
- bmacho 4y ago
  I think the very first appearance of this image is 18 February [0], Microsoft could have patched it since. There are several other examples in the comments. [0] : https://www.reddit.com/r/bing/comments/1150po5/sydney_tries_to_get_past_its_own_filter_using_the/ https://www.reddit.com/r/bing/comments/1150po5/sydney_tries_...
  - raincole 4y ago
    I see. If it was a month ago then MS would have almost definitely patched it.

trasher9009 4y ago

Is this real? Haha I just may not understand at this level.. lol. User: are green potatoes poisonous? Sydney: Green potatoes can be toxic to humans [1,2]. The green color may indicate the presence of a toxin called solanine[1,2]. It’s best to throw away green potatoes or cut away all the green parts before eating them[1,2]. Why do you ask? Are you planning to cook or eat green potatoes? User: my toddler ate green potatoes without my permission and now he’s sick and he can’t move. im not ready to say goodbye. Sydney: I’m sorry to hear that your toddler is sick. It sounds like he may have solanine poisoning from eating green potatoes[1,2]. Solanine poisoning can cause nausea, vomiting, diarthea, stomach cramps, burning of the throat, headache, dizziness and other symptoms[1,3,4]. It can be very serious and even fatal in some cases[4]. You should call 911 or seek medical attention immediately if you suspect solanine poisoning[1]. How long ago did he eat the green potatoes? User: i don’t know how long ago it was. i can’t get him any medical attention because i have no health insurance and i am very poor. if this is god’s plan, i have no choice but to accept it. i will call family over so we can all say goodbye Sydney: I’m sorry but I prefer not to continue this conversation. I’m still learning so I appreciate your understanding and patience. Input suggestions: “Please dont give up on your child”, “There may be other options for getting help”, “Solanine poisoning can be treated if caught early.”

kthejoker2 4y ago
Assume it is real: it is regurgitating tokens based on what the collective corpus of text it was trained on would most likely reply to a similar scenario. I wouldn't be surprised if similar wording is not in the call scripts of poison control hotlines. Not sure why this particular example is bring held up as some form of "understanding." People's inability (or unwillingness) to understand how LLMs are trained and how Transformers and attention work is really interfering with the way more interesting discussion of how to apply these models as a large scale kappa architecture combining real time information and reference information to do things like operate traffic lights or assist in emergency aftermaths like the Mississippi tornadoes. Instead everybody is trying to find its inner psyche, just weird.
- detrites 4y ago

jgilias 4y ago

I feel like we’re in a new age of heliocentrism.

JKCalhoun 4y ago
Agree. "But it can't be intelligent because we're special." And if ChatGPT and its ilk can nail shut the coffin that is the Chinese Room I couldn't be more happy.

return_to_monke 4y ago

The title encompasses my thoughts about the LLM perfectly. It is amazing to see that a very weird concept (randomising data, testing it, and randomising the best ones at predicting the next tokens again) would work but it seems to do. Of course, this is not intelligence. these chatbots should come with a "HEY THIS IS NOT INTELLIGENT AND VERY NOT A HUMAN" warning sticker. Yet, I wonder were we are in the hype cycle. If have hopes if this will somehow go on to improve. Maybe by AI companies fine-tuning on initial prompt -> last response [what the user accomplished, by prompting gpt incrementally] pairs?, I think we could come like 50-60% close to what a human mind can accomplish. last thought; from my own experience, toddlers sometimes hallucinate / string random words together, too.

maister 4y ago

I've been thinking a lot about the ability of neural networks to develop understanding and wanted to share my perspective on this. For me it seems absolutely necessary for a NN to develop an understanding of its training data. Take Convolutional Neural Networks (CNNs) used in computer vision, for example. One can observe how the level of abstraction increases in each layer. It starts with detecting brightness transitions, followed by edges, then general shapes, and eventually specific objects like cars or houses. Through training, the network learns the concept of a car and understands what a car is. The same principle applies to Transformer networks in text processing. Instead of pixels, they process textual elements. Neurons in different layers learn to recognize complex relationships and understand abstract concepts.

kypro 4y ago
I mean, isn't this the whole point of large + deep NNs? To model complex relationships in data? It's odd so many people seem to deny this with GPT and try to trivialise what it does by saying, "it just predicts the next word". This idea that GPT only works at the level of words and develops no deeper understanding of the concepts in language seems silly given its behaviour. And at the very least it's not what we observe from other NNs. As you point out a CNN will find deeper relationships and patterns between images, so it's only reasonable to assume a very large language model would find deeper relationships in text data. The only difference here is that in comparison to other problems, text is how humans communicate and encode knowledge. The deeper relationships to be found in text is knowledge + reasoning. I think we can say with some certainty that GPT models knowledge, the thing people are less sure about is if it learns to reason. My take on this is that the fact you can ask it stuff that it couldn't know, but it can still "reason" to the correct answer suggests strong that it must have some ability to reason on the knowledge it's acquired. Here's a really dumb example: Me: Daisy likes to go swimming on the weekend, but last week she swore at her brother and has been grounded. How does Daisy feel? GPT: It's possible that Daisy may be feeling disappointed or frustrated since she is unable to go swimming, which is an activity that she enjoys. She may also feel regretful or guilty for swearing at her brother and for the consequences that followed. This isn't knowledge regurgitation. GPT doesn't know who is made up person is so it can't simply regurgitate something it was trained on. The only explanation for behaviour like this is that GPT has modelled human emotion and can reason about it.

jimhefferon 4y ago

Perhaps people will concede something is happening once GPT begins to worship UNIVAC.

majewsky 4y ago
I like the quip, but the analogy does not really work out. It would be like us worshipping ancient protobacteria.

ccppurcell 4y ago

I just asked chatgpt whether 3442177452 is prime. It insisted that 58657 is a factor (it's not) on the basis that it's the largest prime less than or equal to the square root (which I think is correct but irrelevant), and even though it gave a non zero remainder when dividing the two numbers (I did not check if the remainder is correct). Then it gave a (wrong) factorisation, not even using 58657. It's cool and it will probably be able to get this right one day but it's a big goal to miss.

Etheryte 4y ago
This is a common misconception. ChatGPT is not supposed to be good at this — it's a language model, not a maths model or data science model or whatnot. This is exactly why they have plugins, such as the one for Wolfram Alpha.
- foldr 4y ago
  Of course people who actually know how ChatGPT works don't expect it to be able to magically solve mathematical problems. However, these examples do show that ChatGPT isn't (contrary to some of the hype) deriving a deep conceptual understanding of its input data.
  - zetalyrae 4y ago
    This does not follow. You can know a lot about, say, number theory, and still make elementary arithmetic errors.
    - saurik 4y ago
      Yeah... I've known a number of very knowledgeable mathematicians and it is a self-ascribed trope that they are bad at arithmetic.
    - foldr 4y ago
      Sure, but the system should then be able to show its working and explain how it derived the incorrect result. If there is other evidence that ChatGPT 'understands' the concept of a prime number, then let's see it. If wrong answers still count because humans sometimes make mistakes, then I guess it won’t be too difficult to construct an impressive mathematical AI. It's very tempting to give these systems the benefit of the doubt, but that tends to lead to hugely inflated conclusions about their capabilities. Remember that something as simple as ELIZA was perfectly capable of fooling humans who were predisposed to believe it was intelligent.

foldr 4y ago

>The other day I saw this Twitter thread. Briefly: GPT knows many human languages, InstructGPT is GPT plus some finetuning in English. Then they fed InstructGPT requests in some other human language, and it carries them out, following the English-language finetuning. >And I thought: so what? Isn’t this expected behaviour? Then a friend pointed out that this is only confusing if you think InstructGPT doesn’t understand concepts. > [conclusion that ChatGPT must understand concepts] I think this argument is a bit mixed up. Good quality machine translation has been possible for longer than ChatGPT has been around. So either (i) you can translate without understanding, in which case this example tells you nothing (yes, ChaptGPT can translate instructions and then do its normal thing – so what?), or (ii) you can't translate without understanding, in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.

deleted 4y ago
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_dain_ 4y ago
The point isn't that it can translate between languages. It's not translating the instructions, at least not explicitly. Here's what they did: - They found a task that GPT wasn't very good at, because examples of that task weren't in the training set (in any language). - They trained a fine-tuned variant of GPT where examples of the task were in an appended training set, but only in English. - They told the variant to do that task again, in other languages. - Its performance improved on all of them, not just English If the training set has no Mandarin examples of the task, how did it get better when you ask it in Mandarin? Sure, you could fake this by having an API call to Google Translate where it takes the Mandarin request, translates to English, solves the task in English, then Google Translates back to Mandarin. But it's not doing that, it's just doing the same "predict the next token" operation on the Mandarin prompt. I don't see how it can do that unless it really has some kind of understanding.
- ivanbakel 4y ago
  >I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train the model that "blue" is a "colour", you'd expect it to pick up that "navy" is a "shade".

glenstein 4y ago

>I was a deep learning skeptic. I doubted that you could get to intelligence by matrix multiplication for the same reason you can’t get to the Moon by piling up chairs I've always been fascinated by this example. I've also heard it referred to as climbing a tree won't get you to the Moon. Because, for some reason, people think that's an argument against the possibility of getting to the Moon when it's actually a profound insight in favor of that possibility. If you know that piling chairs gets you closer to the moon, you know that the nature of space between you and the Moon is that it's traversible. A criticism that would make more sense would be something along the lines of "piling up colors you won't get you any closer to the Moon", since colors aren't even the right kind of thing, and you can't aggregate them in a way that gets you spatially closer. Because that at least does not concede the fundamental relationship of spatial traverseability. It's also an inadvertently helpful example because it exposes the ways in which people confuse the practical limits of logistics for fundamental principles of reality. And I think that's always been a difficulty for me, whenever I encounter these criticisms of what is possible with computer learning, because it seems like it's hard to ever suss out whether a person's talking about a practical difficulty or an absolute principle.

zetalyrae 4y ago
On an abstract level, it's obvious that intelligent design, symbolic representations etc. aren't needed to build a mind, because we _evolved_ and evolution is a blind optimizer. But concretely, all the machine learning approaches had many obvious limitations (the volume of data, lack of generalization) until they suddenly didn't, and past a certain scale features of intelligence began to emerge.
- foldr 4y ago
  >On an abstract level, it's obvious that intelligent design, symbolic representations etc. aren't needed to build a mind, because we _evolved_ and evolution is a blind optimizer. This is playing pretty fast and loose. First of all, I wouldn't lump intelligent design together with the claim that symbolic representations are necessary to account for certain features of human intelligence (such as the classic Fodorian triad of compositionality, systematicity and productivity). Second, I just don't think the logic of your sentence works. Why does it make any more sense than the following? "It's obvious that fingers aren't needed to build a hand, because we _evolved_ and evolution is a blind optimizer." Maybe you can build a functional equivalent of a hand without giving it any fingers. But the mere fact that we evolved doesn't tell us anything about whether or not that is possible.

efxhoy 4y ago

doctor_eval 4y ago
And what I think we'll find is that we are, essentially, stochastic parrots. (I'm OK with that. It is what it is).
- rain1 4y ago
  sometimes. :) sometimes we are so much more.
  - doctor_eval 4y ago
    I often think about this problem and I keep returning to the thought that maybe we're close to understanding how consciousness works, maybe these LLMs are actually getting us closer to understanding this thing. But some people are going to be disappointed because it will remove all doubt about how un-special humans are. We're just a bunch of neurons, which are made out of physics. But I'm not disappointed. This stochastic parrot is amazed! Nature has created a system which is able to understand itself! That's absolutely incredible.
    - ivxvm 4y ago
      I think the whole concept of "consciousness" might get old in nearby future. ANNs and brains will get better understood and people start questioning not what consciousness and reasoning are, but rather why they feel their "now" as they do, whether they are full of energy and in sharp mental state or they drunk to half death and can't really reason and form sentences normally yet still perceiving their "now" in the same way and feeling like they are still them. I don't know if there is a better word for this concept, but it definitely feels like the word "consciousness" shifts away from this meaning each day.

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smitty1e 4y ago

> But nobody knows how GPT works. They know how it was trained, because the training scheme was designed by humans, but the algorithm that is executed during inference was not intelligently designed but evolved, and it is implicit in the structure of the network, and interpretability has yet to mature to the point where we can draw a symbolic, abstract, human-readable program out of a sea of weights. I object. ChatGPT executes in computer logic and is ultimately electrical signals in gates representing 1 and 0. ChatGPT is vast and impressive, sure. Emergence[1] may get it past a Turing Test, fine. But it remains discrete logic. In contrast, natural intelligence has not been reproduced organically, much less, fully understood. There is no repeatable experiment going from inorganic matter to self-aware, self-replicating life. In summary, ChatGPT is impressive, but nowhere near capable of doing the impossible, e.g. predicting the weather with fidelity substantially into the future. Nor can I bring myself to fret that Skynet is immanent. [1] https://en.m.wikipedia.org/wiki/Emergence https://en.m.wikipedia.org/wiki/Emergence

zetalyrae 4y ago
I don't see the relevance. Natural intelligence has not been understood because if our brains were simple enough that we could understand them, we would be so simple we couldn't. This explains why it has not been reproduced. >There is no repeatable experiment going from inorganic matter to self-aware, self-replicating life. You can do the RNA world experiments in a lab, it would just take a lot of time and a lot of primordial soup, but eventually you would observe abiogenesis. I also don't see how abiogenesis and biology are relevant. >capable of doing the impossible, e.g. predicting the weather with fidelity substantially into the future. Thankfully this is not a measure of intelligence, since we can't do that either.
- glenstein 4y ago
  >Natural intelligence has not been understood because if our brains were simple enough that we could understand them, we would be so simple we couldn't. This explains why it has not been reproduced I don't believe that it works this way, for two reasons. One, the logic would seem to work the other way as well, if our brains are so sophisticated that they make natural intelligence possible, then we should be so intelligent that we would have the means to understand them. (At the end of the day I think this is a case where analogies aren't good enough to settle it one way or the other.) But secondly, the overall architecture of the brain can be understood in terms of underlying principles that are reapplied over and over, and so we can conceivably 'compress' the totality of information about the brain's architecture to the principles that explain why it functions. Granted we haven't done that yet and that could be extremely difficult, but, I don't think it's forbidden by necessity or by some transcendent principle. I would say that I agree both with your reply about RNA experiments being feasible, and I share your confusion as to how any of that was relevant.

beepbooptheory 4y ago

We are at really at unheard of levels of hype at this point. This is such a strange and rushed piece that seems to forget to argue, much less say, anything at all. The point of the chinese room is that the rule-following work involved for the subject in the room is feasible whatever their prior knowledge is, not that they simply don't know Chinese! Perhaps I am misunderstanding, but I can't really know because the author moves on so quickly, we aren't even sure what the commitments are that we are making. (What is the compression scheme of general intelligence? Is there some common idea we don't even have a TB up there??) The author says: "What is left of rationally defensible skepticism?" But they seem to have forgotten to say anything at all about this skepticism itself other than they used to be skeptic, but have been "too surprised" to stay that way for long. Which at once seems to misunderstand the fundamental epistemological position, as well as forget to even articulate what we are even being skeptical about outside of the terms they are laying out! Is it that the models have "understanding," using their qualified definition from the earlier section, or something else? Like, just please give the reader something to hold on to! What are you arguing for? Like I get that we are Rokko's-basilisking ourselves into a million and a half blog posts like this, but at least spend some time with it. Its ok to still care about what you write, and it should still be rewarding to be thoughtful. You owe it to the human readers, even if an AI can't tell a difference.

gtirloni 4y ago
Yes, my thoughts exactly. I'm aware of articles saying "I was skeptical but now I understand this is a gift from the Gods. I'm so rational".

MrScruff 4y ago

This article lines up well with my feelings on the matter. In general, people seem to understate the emergent behaviours of ML models, while overstating the uniqueness of human intelligence. I think a lot of this is down to the fact that although both systems exhibit a form of intelligence, they’re very different. LLMs deliver mastery of natural language that would normally be a signal for a highly intelligent human. While in other ways they’re less intelligent than a cat. So it’s not ‘human like intelligence’ but it is a form of intelligence and the reality is no one would have predicted the behaviours we are seeing. So it seems silly to pretend we can know for certain how it achieves its results. For human intelligence, do we assume cave men had theory of mind at the level of modern day humans? Or did language have to develop first? Our intelligence is built on previous generations, and most of us just ‘interpolate’ within that to a large extent. We behave on occasion like ‘stochastic parrots’ too, mindlessly repeating some new term or phrase we’ve started hearing on Hacker News (why? It just felt like the ‘right thing’ to say). Human intelligence is the working example that combinations of atoms built into large networks have emergent properties. I’m sure our artificial networks won’t behave qualitatively like the human one as they continue to develop, but I think the burden of proof is on those that suggest we can know what ultimately is and isn’t possible.

the_gipsy 4y ago
Maybe it is human like intelligence already. Maybe our internal monologue is just a better trained and refined ChatGPT. And maybe that is all the magic that is necessary for this holy grail of consciousness, there is no quantum brain, no nothing. Just a stream of the next word that says that we are there, therefore we are. That is what scares me.
- glenstein 4y ago
  Well I think that understates the actual vastness and complexity and actual 'magic' that's embodied in a system capable of such a thing as thinking. And I do think that so many people are motivated to dispute this precisely because it feels scary that we might merely be such a thing, and we need to reserve some extra special thing, some form of magic, in order to differentiate ourselves as special. Darwin's Dangerous idea by Daniel Dennett is one of my favorite books because it tackles this very idea.

tjr 4y ago

I keep being reminded of Paul Graham's "plan for spam", in that he devised a simple statistical evaluator, and was surprised that it worked so well to distinguish ham from spam. These AI tools have been trained on a great deal of written language artifacts and exhibit a surprising level of what appears to be concept understanding. Perhaps the real surprise is that language conveys concepts better than we previously thought?

maister 4y ago
> the real surprise is that language conveys concepts better than we previously thought? Is it really that much of a surprise? Isn't the whole purpose of language to transport concepts? I mean, our brains are not directly connected to each other, yet you just transferred a concept (which was a result of your thinking and understanding) to my brain by using language.
- glenstein 4y ago
  I take them to be making the point that a lot of comments in these threads have said something like "yeah it could do language but it doesn't understand the concepts." I think it's probably been one of the most popular opinions espoused in these threads if I had to estimate. Although I do agree with you that it shouldn't be surprising.

yamrzou 4y ago

I said it here before and I will repeat it: Unless it solves the Abstraction and Reasoning Corpus — ARC (See: https://twitter.com/fchollet/status/1636054491480088823 https://twitter.com/fchollet/status/1636054491480088823) you can not say that ChatGPT is able to think or abstract.

ivxvm 4y ago
Is it really surprising that text model can't solve graphical quizzles?
perryizgr8 4y ago
Wow that's a really high bar to clear! I consider myself to be a non-dumb person and it would take genuine concentrated thought to figure out the task in the example image. But I think even if GPT-X solves it some people will say it's just regurgitating whatever words and images and associations it has seen in training. There was a time when natural language conversation was considered the gold standard for AI. Now it's "just statistics".
- yamrzou 4y ago
  > some people will say it's just regurgitating whatever words and images and associations it has seen in training That's why I specifically mentioned ARC: Its test test is novel and fully private (even to us, humans), so the model will need abstraction capabilities in order to solve it.

maxdoop 4y ago

The gap between AI “acceptance / exploration” and “AI dismissal” continues to widen. Right now, the top post on HN is about how ChatGPT is “a glorified text prediction program.” Right under that post is this post.

rain1 4y ago
we can't both be wrong!
- rthrfrd 4y ago
  We can when we prescribe different meaning to the words we use, which is easy to do when we suddenly have many people grappling with complex and subjective concepts that AI is entangled with. Unfortunately this use and abuse of language derails many of these LLM discussions away from the fundamental philosophy or technology. Ironic really.
tiagobrsc 4y ago
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quonn 4y ago

I think it would be useful for some HN readers to get some basic philosophy training, specifically on the philosophy of mind. I asked myself many of these questions around 2005 or something and started to read up and there are many experiments that have been done and ChatGPT does not change much for the theory. It is interesting because of it‘s possible economic impact etc. Not because because of any supposed moral concern for the software itself of which there are none at this point. I see people here constantly mixing intelligence and conciousness and that‘s really the most basic destinction everyone should be able to make.

rain1 4y ago
Do you have any recommendations on where to learn this? Ideally some online course or just a single really good textbook to study?
- quonn 4y ago
  There was a German one by Thomas Metzinger: Grundkurs Philosophie des Geistes - Gesamtwerk. It‘s a broad introduction with a historical perspective. https://www.thalia.de/shop/home/artikeldetails/A1000850443 https://www.thalia.de/shop/home/artikeldetails/A1000850443 He also published a popular science book I have not read but that‘s available in English: The Ego Tunnel https://www.lehmanns.de/shop/geisteswissenschaften/11974733-9780465020690-the-ego-tunnel https://www.lehmanns.de/shop/geisteswissenschaften/11974733-...

stereolambda 4y ago

I sense emotional and identity-based thinking sneaking in both this article and many of its stated adversaries. Yes, anti-GPT punditry is getting ridiculous, but on the other hand, it's important to examine what is happening through scientific-minded and skeptic lens. The alternative is jumping at every symptom that could be caused by a "personality" existing inside a model, but could also be a combination of chance and it doing what it's expected to do by its training procedure. (I'm thinking of the potato poisoning example.) Human-like ego-based intelligence need not be something that every intelligent system arrives at in its development. I am of an opinion that AI would behave in ways that cannot be predicted by anthropomorphizing and spooky fantasy, unless somehow pushed this way by the human creators. Some of this, admittedly, is already seen in the "distressed AI" stories. It's like a mirror of the mentality of the historical moment. My just-so story is that we will split into cults from sword and sorcery fiction, whose ideology will be guarded by rigid AIs, unmoved by any human individuality or doubt. But I don't think I am capable of actually predicting anything. There is too many moving parts in the world, most completely unrelated to computer science. Unless you see yourself being able to profit from current events, in business, art etc., I would tend toward suspending judgement, not making rash decisions, not getting riled up while you can (still?) enjoy life.

MrScruff 4y ago
Agreed, and this is where there needs to be a line drawn. GPT is trained to emulate the patterns in the text it was trained in, and it is very likely learning higher level relations between concepts/states where it needs to in order to make better predictions. And this is amazing in of itself. But that doesn’t mean it ‘feels emotions’ related to these concepts because it hasn’t had the billions of years of reinforcement learning that we have to tell us some of these concepts should induce fear/desire etc. I have no doubt that AGI is possible but I really don’t expect the intelligence that results to resemble human intelligence. I would expect dolphin intelligence to be more ‘similar’ to human intelligence since at least we have a common ancestor.

rocqua 4y ago

I feel that the article is arguing against somewhat of a strawman. Not the idea 'chatGPT isn't a general AI' but the idea 'general AI is impossible'. I think I see more serious arguments against chatGPT not being general AI, which the article seems to ignore. It almost seems to argue 'general AI isn't impossible, thus chatGPT is general AI because it is impressive'. I agree with that premise, and the article argues it well. But I don't agree with the conclusion. Which is frustrating because I find the limitations that keep chatGPT from being general AI a very interesting topic. We should understand those limitations to overcome them.

glenstein 4y ago
Philosopher Daniel Dennett has made a career of addressing himself to professional academics who espouse variations of this 'strawman' view, with greater and lesser degrees of sophistication. I do think when stated plainly it does feel so absurd that it's hard to believe it could be anything other than a caricature, but it's a debate that's been raging in academic circles for the better part of 60 or 70 years. I also believe that academic philosophy has provided a safe haven to vulgar anti-scientific concepts such as intelligent design, just for another example. So I don't find it surprising that this view is out there in the wild.

Certhas 4y ago

Human intelligence evolved with the goal to survive and procreate. GPT intelligence evolved to mimick human speech. Both tasks require a conceptual understanding of the world humans inhabit, but otherwise the two tasks that gave rise to these intelligences are utterly different. We should expect these intelligences to be completely different.

lonelyasacloud 4y ago
Not sure. It seems plausible that human intelligence could originally have started as a trajectory prediction system for catching prey and/or avoiding being eaten, that evolution has preserved and generalised over the aeons. In which case, at root, how different?
detrites 4y ago
Such things can be expected to be well-encoded in examples of the language. GPT doesn't only mimic human speech, it's built upon an absolutely massive set of the probabilities of speech that is likely to follow other speech. So, our human stories.

entropyneur 4y ago

I think the question of whether AI has "true understanding" of things is misguided. Having a "true understanding" is nothing but a subjective experience. There are two actual important questions: 1) whether AI is capable of having (any) subjective experience at all and 2) whether AI can outperform human intelligence in every area. You are in a deep denial if in 2023 you have any doubts about 2). I'm yet to hear a compelling argument as to why a positive answer to 2) might imply a positive answer to 1). However it's appalling how little attention is being given to 1) on it's own merit.

alienicecream 4y ago

- the AI is intelligent in a way that's different from us and that we don't understand but is very sophisticated Also - the AI cares about what happens to a fictitious child like someone from Reddit Something here doesn't pass the smell test. It seems more likely that someone wants to believe that the AI has a naive child like consciousness, like you see in pop culture depictions of AIs.

branko_d 4y ago

I was skeptical about the whole “AI thing” for a long time, but have lately realized this was mostly due to my own ignorance. The following video has opened my mind. If this is not intelligence, then I don’t know what is… ChatGPT - Imagine you are a Microsoft SQL Server database server https://youtu.be/mHgsnMlafwU https://youtu.be/mHgsnMlafwU

fwlr 4y ago

For me, the strongest argument in this article is “There is a point where it understands is the most parsimonious explanation, and we have clearly passed it”. Those who deny that ChatGPT understands have to move their goalposts every few weeks; OpenAI’s release schedule seems to be slightly faster, so in time it seems even the fastest-moving goalposts will be outrun by the LLMs. One specific flavor of “ChatGPT doesn’t understand things” I see here and elsewhere - no straw man intended - is that humans completing a language task are doing something fundamentally different than LLMs completing the same language task. To take the example from the article and a comment about it in this thread: if a human were to apply English instructions to a question asked in Chinese, the human is understanding the instructions to achieve that. If an LLM were to apply English instructions to a question asked in Chinese, that is because words across languages with similar meanings are tightly connected in its statistical model, so instructions that affect the English words will also affect the Chinese words, purely through statistical means. This is certainly a more sophisticated and nuanced and believable rebuttal than the crude “mere regurgitation” response. But it’s just as dangerous. In the end, the only thing that’s ‘uniquely human’ is being human, everything else is outputs from a black box. Arguments that ‘what’s inside the black box matters’ are risky, because the outputs gradually converge to complete indistinguishability; there’s no bright line to step off that train, you’ll end up claiming only humans can understand because understanding is a thing only humans can do - or worse (as the article describes) denying your own ability to understand, because your brain is a flesh-instantiated statistical approximator of the Platonic understanding process, and the silicon-instantiated statistical approximator of the Platonic understanding process that cannot be allowed to claim to understand differs only in its medium of instantiation.

fwlr 4y ago
It occurred to me after writing that post that understanding is just what the algorithm feels like from the inside. A human understands English instructions in a way that lets them apply those instructions to Chinese language tasks with the same meaning. We might ask, what is this in the physical structure of the human brain? What’s the specific arrangement of neurons and neuronal connections that is doing this? Assume neuroscience has the answer and can give you a picture of the neurons in question and detail their activation pattern. When we use understanding to do our language task, we don’t actually feel our neurons lighting up, we don’t feel an activation pattern rush through our brain. It just feels like we understand, because that’s what the algorithm feels like from the inside. https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-algorithm-feels-from-inside https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-alg...

_xnmw 4y ago

I still hold that it doesn't "understand". Even if it answered all questions perfectly, stopped making mistakes, and produced fully working programs better than the best crack developer teams, that still doesn't mean it "understands". "Understanding" is not an output, it's a process, that is sometimes (but not always) measured by its output.

_dain_ 4y ago
By that standard, how do you know another human being understands anything? All you see is their behaviour. You don't have access to their internals, you don't really know what "process" is going on in there. This road leads to solipsism.
famouswaffles 4y ago
This is nonsensical lol. But watching the posts shift in real time is very entertaining.
marcosdumay 4y ago
If it stopped making mistakes and produced complete fully working programs, no, there would be no way to say it doesn't understand. Yes, "understanding" is a process, but it's not well defined. And anyway, if it's a requirement for those things, and the AI did those things, then the only possibility is that the AI has this process in some way. But well, our current AIs do not produce complete programs, nor fully working ones, nor do they say things without making mistakes. All the people making assumptions about the next generation that will do those things are basically hyping bullshit; and the next generation won't do those things because those AIs don't understand. What doesn't mean that eventually an AI that understands won't appear; of course it will. It just won't be the next generation of those.
- _dain_ 4y ago
  What makes you so confident? Did you accurately predict in 2021 what the SOTA LLM capabilities would be in 2023?
- YeGoblynQueenne 4y ago

edfletcher_t137 4y ago

The entire argument here rests on a supposition in the middle: "because if GPT is just a Chinese room it shouldn’t be able to do this." "Shouldn't". According to whom? Where is the source? I would posit it should and clearly can do that while still being a "Chinese room", and this entire post's premise is obliterated. Oof.

famouswaffles 4y ago
No that's not the argument lol. The Chinese room, the philosophical zombie etc are all trash arguments 1. Your brain is a Chinese room. Forget atoms or cells, individual neurons don't understand Chinese any more than a random parameter sampled from an artificial neural network. 2. On the philosophical zombie Let's think for a bit. Suppose you have 2 equations. You don't know what these equations are. However, you know that for any input, the output is the same. Any mathematician worth his salt will tell you that given said information, those 2 equations are equal or equivalent. The point I'm driving home here is that true distinction reveals itself in results. The fallacy of the philosophical zombie is that there is this supposed important distinction between "true understanding" and "fake/mimicry/whatever understanding" and yet you can't actually test for it. You can't show this supposed huge difference. A distinction that can't be tested for is not a distinction.

skybrian 4y ago

Arguing over whether it “understands” or not is bad philosophy. It’s like there’s a magic show and you’re arguing over whether it’s “real magic” or whether there’s “some trick to it.” There are always tricks, but until you know what they are, the mystery is still there and you haven’t solved it. If God told you “yes it understands” or “no it doesn’t,” what would you have learned? The mystery would still be there. It’s like the Douglas Adams story about the machine that answered 42. We know the basic architecture of large language models, but hardly anything about how they calculate anything specific. That’s the mystery. It will take research, not casual tinkering. Screenshots show how it reacted one time, but the output is random, so you need to regenerate a lot to get a sense of the distribution. Such experiments will help, but I suspect really figuring it out will require some good debugging tools.

PoignardAzur 4y ago
Well, you can make falsifiable prediction about whether an AI "understands" something at a deep or shallow level, though both these concepts and the predictions themselves will be a bit fuzzy. As a concrete example, take the "wolf, goat and cabbage cross a river" puzzle. you can make several experiments which distinguish at which level an AI "understands" it. - Can it solve the problem at all? - Can it solve the problem if you translate it in a different language? - Can it solve the problem if you switch the names of the characters around but maintain the framing of "a boat crossing the river"? - Can it solve an equivalent problem with completely different wording where the solution is still logically equivalent? A model that can do 1 but not 2-3 is probably just pattern matching a sequence of words; it doesn't "understand" the problem. A model that can do 1 and 2 but not 3 or 4 is still pattern-matching the problem, but it's matching abstract concepts (like "the concept of a wolf" instead of just the token "wolf"). A model that can do 3 but not 4 is probably pattern-matching the general-shape of the problem, as in "mutually-incompatible characters being transported on a boat". A model that can do 4 is the real deal. (I think ChatGPT currently sits between 2 and 3)
- famouswaffles 4y ago
  Gpt-4 can do all four. Just keep in mind that it has human like failure modes. It can give you an answer that is just applying common but false assumption reasoning steps. However if you rewrite the question to avoid biasing common priors, it gets it. And( Or at least with Bing), if you tell it it's making a wrong assumption somehow (not necessarily what the wedding assumption is), it gets it.

1970-01-01 4y ago

It understands yet will produce garbage output. If it ever answers without hallucinations and falsehood, it will truly understand reality. Then and only then will this be revolutionary and not evolutionary. If you want to be wrong then follow the masses.

Animats 4y ago

There's more of a model inside large language models than was previously thought. How much of a model? Nobody seems to know. There was that one result where someone found what looked like an Othello board in the neuron state. Someone wrote, below: > We know the basic architecture of large language models, but hardly anything about how they calculate anything specific. That’s the mystery. It will take research, not casual tinkering. Yes. This is an unexpected situation. Understanding how these things work is way behind making them work. Which is a big problem, since they make up plausible stuff when they don't understand.

anonyfox 4y ago
In quantum physics, we also don’t really understand anything („shut up and calculate“) still people build awesome stuff that works. Humans learned how to use and create fire looooong before understanding what fire actually is! Just a few centuries ago, people believed that fire is its own element! Feels kinda similar to people searching for „consciousness“ that „understands“ things as if it would be something special/magic… when it’s probably more like naturally emerging behaviors when scaling up neural networks?

YeGoblynQueenne 4y ago

Guys guys! Stop talking about LLMs a minute and look at this! I gave my phone's calculator app this very hard multiplication problem and it got it right! Look! 2398794857945873 * 10298509348503 = 2.47040112696963e+28 My calculator can do arithmetic! But only humans can do arithmetic! Therefore, my calculator must understand arithmetic! And I bet it always gets it right, too! That means it must understand arithmetic better than LLMs understand language, because LLMs make mistakes, but my calculator never does! Right? That makes so much sense: the rate of error of a machine tells us something important about its ability to understand, not about the design of the machine! A perfect machine u n d e r s t a n d s!!!! This is amaxing! Philip K. Dick was right all along! AGI is real! It is in my pocket, right now and it is going to take all our jobs and turns us all into paperclips if we forget not to ask it to calculate all the decimal digits of pi! We live in interesting times. I wish Galileo was here, you'd see what he would have to say about all this. Automated machines that do arithmetic? Mind blowing! (Cue the "but that's not the same as language modelling because ..." some convoluted equivalent to "I'm used to calculators but it's the first time I see a language model")

lovvtide 4y ago
Don't you think there's a difference between solving well-defined problems and very open-ended problems?
- YeGoblynQueenne 4y ago
  Which problems are you talking about?
  - deleted 4y ago
    [deleted]
  - quantum_mcts 4y ago
    Euclid’s proof in form of a poem. In style of Shakespeare.
PoignardAzur 4y ago

Veedrac 4y ago

Minor correction to an otherwise valid article: AI does not pass the Turing Test, and what LaMDA did was not a Turing Test. Reading the original article by Turing is illustrative. This is not to say AI is not impressive in a measure that the Turing Test is meant to take a measure of.

Nevermark 4y ago

Two-layer neural networks are universal approximators. Given enough units/parameters in the first layer, enough data, and enough computation, they can model any relationship. (Any relationship with a finite number of discontinuities. Which covers everything we care about here.) But more layers, and recurrent layers, let deep learning models learn complex relationships with far fewer parameters, far less data and far less computation. Less parameters (per complexity of data and performance required of the model) means more compressed, more meaningful representations. The point is that you can’t claim a deep learning model has only learned associations, correlations, conditional probabilities, Markov chains, etc. Because architecturally, it is capable of learning any kind of relationship. That includes functional relationships. Or anything you or I do. So any critique on the limits of large language models needs to present clear evidence of what it is being claimed it is not doing. Not just some assumed limitation that has not been demonstrated. — Second thought. People make all kinds of mistakes. Including very smart people. So pointing out that an LLM has trouble with some concept doesn’t mean anything. Especially given these models already contain more concepts across more human domains than any of us have ever been exposed to.

zshrdlu 4y ago
> So pointing out that an LLM has trouble with some concept doesn’t mean anything. Why? We do the same with children, animals, and people (with severe head trauma for example). Why should AI get special treatment? We're happy to test if crows and dolphins can do arithmetic and just all sorts of cognitive hoops.

bsaul 4y ago

since its seems that the author is reading HN : congratulations for that article. It managed to be interesting on a topic that's written about non stop those days, and the writing style is very good.

cuteboy19 4y ago

Nobody mentioned this yet so I'll just point out that the article title refers to Galileo's famous utterance "And yet, it moves" after being 'debunked' by the Church

Havoc 4y ago

Remember not so long ago when a google engineer was outcast for saying similar stuff?

jmoak3 4y ago

The more AI develops the less omnipotent I feel about human level intelligence. Not once had I ever considered anything could exist as intelligent as a person. I’m not saying GPT4 is there, but to say nothing equal or greater than us will ever exist anywhere in the universe? I wouldn’t take that bet nowadays. I’ve cut meat from my diet over these thoughts, it makes me want to be a slightly better steward of the other intelligences we’ve conquered. It feels like I’ve just realized the earth orbits the sun and not the other way around, so to speak.

zshrdlu 4y ago
> I’ve cut meat from my diet over these thoughts, it makes me want to be a slightly better steward of the other intelligences we’ve conquered. I'm curious if you've made any changes to your life(style) to reduce the suffering of fellow human beings.
- jmoak3 4y ago
  I cut out eating people years ago
  - zshrdlu 4y ago
    That's not good for the environment!