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"While I continue to believe that many people are going to collectively lose trillions of dollars ultimately pursuing "AI" at this stage" Can you please explai

"While I continue to believe that many people are going to collectively lose trillions of dollars ultimately pursuing "AI" at this stage" Can you please explain more why you think so ? Thank you.

mschuster91 2y ago
It's a hype cycle with many of the hypers and deciders having zero idea about what AI actually is and how it works. ChatGPT, while amazing, is at its core a token predictor, it cannot ever get to an AGI level that you'd assume to be competitive to a human, even most animals. And just as every other hype cycle, this one will crash down hard. The crypto crashes were bad enough but at least gamers got some very cheap GPUs out of all the failed crypto farms back then, but this time so much more money, particularly institutional money, is flowing around AI that we're looking at a repeat of Lehman's once people wake up and realize they've been scammed.
- KronisLV 2y ago
  > And just as every other hype cycle, this one will crash down hard. Isn't that an inherent problem with pretty much everything nowadays: crypto, blockchain, AI, even the likes of serverless and Kubernetes, or cloud and microservices in general. There's always some hype cycle where the people who are early benefit and a lot of people chasing the hype later lose when the reality of the actual limitations and the real non-inflated utility of each technology hits. And then, a while later, it all settles down. I don't think the current "AI" is special in any way, it's just that everyone tries to get rich (or benefit in other ways, as in the microservices example, where you still very much had a hype cycle) quick without caring about the actual details.
  - anon373839 2y ago
    > I don't think the current "AI" is special in any way As someone who loves to pour ice water on AI hype, I have to say: you can't be serious. The current AI tech has opened up paths to develop applications that were impossible just a few years ago. Even if the tech freezes in place, I think it will yield substantial economic value in the coming years. It's very different from crypto, the main use case for which appears to be money laundering.
    - carlmr 2y ago
      >It's very different from crypto, the main use case for which appears to be money laundering. Which has substantial economic value (for certain groups of people).
      - lazide 2y ago
        According to this random estimate, black market economy alone in just the US is worth ~ $2 trillion/yr. [https://www.investopedia.com/terms/u/underground-economy.asp https://www.investopedia.com/terms/u/underground-economy.asp] Roughly 11-12% of GDP. In many countries, black+grey market is larger than the ‘white’ market. The US is notoriously ‘clean’ compared to most (probably top 10). Even in the US, if you suddenly stopped 10-12% of GDP we’re talking ‘great depression’ levels of economic pain. Honestly, the only reason Crypto isn’t bigger IMO is because there is such a large and established set of folks doing laundering in the ‘normal’ system, and those work well enough there is not nearly as much demand as you’d expect.
        carlmr 2y ago
        >Honestly, the only reason Crypto isn’t bigger IMO is because there is such a large and established set of folks doing laundering in the ‘normal’ system, and those work well enough there is not nearly as much demand as you’d expect. Correct me if I'm wrong. Doesn't the public ledger make it really bad for this purpose when enough is known about some of the actors interacting on the chain here? It's basically an exact recording that everybody can see, of who interacted with whom, based on pseudonyms, right?
        lazide 2y ago
        You are not wrong. Cash is superior in the ability to have intermediate transactions hidden. However it has similar issues in that bills are serialized, and I believe scanned/tracked - or at least there is the capability to do so. So in theory, when moving it out of and back into the system, it can be tied to real identities not just pseudonyms too. Though frankly, it seems like no one really cares most of the time.
  - lillecarl 2y ago
    Both Kubernetes and serverless (FaaS) is here to stay. Microservices is just an excuse to build shit software.
    - KronisLV 2y ago
      > Both Kubernetes and serverless (FaaS) is here to stay. *in environments and projects where they are a good fit > Microservices is just an excuse to build shit software. *in environments and projects where they are a bad fit
- CamperBob2 2y ago
  it cannot ever get to an AGI level that you'd assume to be competitive to a human, even most animals. Suppose you turn out to be wrong. What would convince you?
  - ChuckMcM 2y ago
    It could diagram a sentence it had never seen.
    - karolist 2y ago
      It does that all the time even now. Play with temp, top_k, min_p and nothing is preventing you from getting combinations and order of tokens previously not encountered in history.
    - kragen 2y ago
      I did eventually get an LLM to produce what seems to be a correct diagram of a sentence it had never seen, but it took about ten tries. Grammatical analysis seems to have happened correctly every time, but getting to a usable diagram was difficult. (I know that it's generally rude to include LLM output in HN comments, but in this case I think it's essential supporting material to elevate the discussion of LLM capabilities above "yes it is", "no it isn't".) First, I just asked ChatGPT (GPT-4o mini, what they offer anonymous free users): > Please diagram the sentence, "If my mother's road had been cultural, my uncle's sloth squad residue would have compiled a periodic meadow of night." I just invented that sentence, picking several words with a random number generator, but of course I can't prove to you that I didn't copy it from some book you haven't seen. (For that, you'd have to invent a sentence yourself, or at least provide me with some words to include in it.) I hope you can at least agree that it's a highly unlikely sentence for anyone ever to have uttered before! After talking to itself for a few paragraphs, it arrived at the following grammatical analysis: Step 4: Diagramming the sentence Main Clause: Subject: "my uncle's sloth squad residue" Verb: "would have compiled" Direct Object: "a periodic meadow of night" "a periodic meadow" (noun phrase) "of night" (prepositional phrase modifying "meadow") Conditional Clause: Subject: "my mother's road" Verb: "had been" (linking verb) Predicate Adjective: "cultural" This is exactly correct. From that it produced the following ASCII art "diagram", which looks like nonsense to me, but maybe it's in some kind of sentence-diagramming formalism I haven't seen: If ______/______ | | my mother's had been road | cultural my uncle's sloth squad residue | would have compiled | a periodic meadow of night | of night I told it the diagram looked like nonsense, and it produced this one, which looks worse: If _______/______ | | my mother's road had been | | cultural (Main Clause) | my uncle's sloth squad residue | would have compiled | a periodic meadow | of night I asked GPT-4 (paid) the same question, and it gave me another exactly correct grammatical analysis in words: 1. **Identify the main clauses**: - "If my mother's road had been cultural" is a dependent clause (specifically, a conditional clause). - "my uncle's sloth squad residue would have compiled a periodic meadow of night" is the main clause. 2. **Start with the main clause**: - Subject: "my uncle's sloth squad residue" - "my uncle's" modifies "sloth squad," which in turn modifies "residue." - Predicate: "would have compiled" - This includes the modal auxiliary verb ("would"), perfect auxiliary ("have"), and main verb ("compiled"). - Direct object: "a periodic meadow of night" - “a” is an article modifying “meadow,” - “periodic” modifies “meadow,” - “of night” is a prepositional phrase modifying “meadow.” 3. **Diagram the dependent clause**: - Subject: "road" - Modified by possessive noun phrase “my mother’s” - Predicate: "had been" - Complement/Adjective: "cultural" (I feel like this is definitely "a level that you'd assume to be competitive to a human", in mschuster91's phrase; most native English speakers can't do that.) But then it came up with the following incorrect diagram: +---------------------------------------------------+ | Main Clause | +---------------------------------------------------+ / | \ / | \ / | \ [My uncle's] [sloth squad] [residue] \ / / \ / / \ / / would have compiled ------------------- \ \ \ [a periodic meadow of night] / | \ / | \ a periodic [of night] +---------------------+ | Dependent Clause | +---------------------+ If | road ___|_____ | | my mother's had been cultural Telling it the diagram was nonsense didn't help. But my conclusion from this is not that GPT-4 can't analyze the grammatical structure of sentences it had never seen. It's that GPT-4 gets "confused" when it tries to draw ASCII art, like a person trying to tell you how to draw it over the telephone, but not quite as bad. I've seen similarly abominable results when it tried to draw circuit schematics and guitar tablatures as ASCII art. I tried asking it for a graphviz diagram, which was syntactically correct graphviz code but didn't express the sentence structure properly. I tried simplifying the problem with the prompt, 'Please provide a syntactic tree showing the syntactic derivation of the sentence, "My uncle's sloth squad residue would have compiled a periodic meadow of night," using graphviz syntax and with individual words at the leaf nodes of the tree.' which yielded a slightly incorrect diagram. (I'm still sure nobody has ever uttered this sentence before.) On being told, "This diagram is not quite right.", GPT-4 produced the following diagram, which I believe is correct except for an extra "s" on "uncle" (you can see this in your browser at https://is.gd/Axufjx https://is.gd/Axufjx): digraph G { node [shape=plaintext]; S [label="S"]; NP [label="NP"]; VP [label="VP"]; S -> NP; S -> VP; // Noun Phrase breakdown DP_my [label="DP"]; DP_my -> My; UnclePossessive [label="NP"]; UnclePossessive -> Uncles; UnclePossessive -> ApostropheS; NP -> DP_my; NP -> UncleCompound; UncleCompound [label="NP"]; SlothSquadResidue[label = "N'"]; SlothSquadResidue-> Sloth; SlothSquadResidue-> SquadResidue; SquadResidue[label = "N'"] SquadResidue-> Squad; SquadResidue-> Residue; UncleCompound-> UnclePossessive; UncleCompound-> SlothSquadResidue; // Verb Phrase breakdown ModalWould[label = "Modal"]; ModalWould-> Would; AuxHave[label = "Aux"]; AuxHave-> Have; VCompiled[label = "V"]; VCompiled-> Compiled; ObjectNP[label = "NP"]; // Direct object noun phrase ArticleA[label = "D"]; ArticleA-> A; MeadowPhrase[label = "N'"]; // 'Meadow' modified by 'periodic' AdjPeriodic[label = "Adj"]; AdjPeriodic-> Periodic; MeadowWord[label = N]; MeadowWord -> Meadow; MeadowPhrase -> AdjPeriodic; MeadowPhrase -> MeadowWord; PPofNight[label = PP]; // Prepositional phrase 'of night' PrepOf[label=P] PrepOf-> Of; NightNounPhrase[label=N] NightNounPhrase-> Night; PPofNight -> PrepOf; PPofNight -> NightNounPhrase; ObjectNP -> ArticleA; ObjectNP -> MeadowPhrase; ObjectNP -> PPofNight; VP -> ModalWould; VP -> AuxHave; VP -> VCompiled VP -> ObjectNP } Claude produced a reasonably serviceable diagram on the first try with that prompt: digraph SyntacticTree { node [shape=plaintext]; S -> NP1; S -> VP; NP1 -> DET1 [label="Det"]; NP1 -> NP2 [label="NP"]; NP2 -> NP3 [label="NP"]; NP2 -> N1 [label="N"]; NP3 -> N2 [label="N"]; NP3 -> POS [label="Pos"]; VP -> AUX1 [label="Aux"]; VP -> AUX2 [label="Aux"]; VP -> V [label="V"]; VP -> NP4 [label="NP"]; NP4 -> DET2 [label="Det"]; NP4 -> ADJ [label="Adj"]; NP4 -> N3 [label="N"]; NP4 -> PP [label="PP"]; PP -> P [label="P"]; PP -> NP5 [label="NP"]; NP5 -> N4 [label="N"]; DET1 -> "My"; N2 -> "uncle"; POS -> "'s"; N1 -> "sloth"; N1 -> "squad"; N1 -> "residue"; AUX1 -> "would"; AUX2 -> "have"; V -> "compiled"; DET2 -> "a"; ADJ -> "periodic"; N3 -> "meadow"; P -> "of"; N4 -> "night"; } On being told, I think incorrectly, "This diagram is not quite right.", it produced a worse diagram. So LLMs didn't perform nearly as well on this task as I thought they would, but they also performed much better than you thought they would.
- dsign 2y ago
  Those glorified token predictors are the missing piece in the puzzle of general intelligence. There is a long way to go still in putting all those pieces together, but I don't think any of the steps left are in the same order of "we need a miracle breakthrough". That said, I believe that this is going one of two ways: we use AI to make things materially harder for humans, in a scale from "you don't get this job" to "oops, this is Skynet", with many unpleasant stops in the middle. By the amount of money going into AI right now and most of the applications I'm seeing being hyped, I don't think we have have any scruples with this direction. The other way this can go, and Cerebras is a good example, is that we increase our compute capability and our AI-usefulness to a point where we can fight cancer and stop/revert aging, both being a computational problem at this point. Even if most people don't realize it, or most people have strong moral objections to this outcome and don't even want to talk about it, so it probably won't happen. In simpler words, I think we want to use AI to commit species suicide :-)
  - Shorel 2y ago
    I'm sure there are more missing pieces. We are more than Broca's areas. Our intelligence is much more than linguistic intelligence. However, and this is also an important point, we have built language models far more capable than any language model a single human brain can have. Makes me shudder in awe of what's going to happen when we add the missing pieces.
    - idiotsecant 2y ago
      Yes, I sometimes wonder if what we're witnessing in our lifetimes is the next stage of the 'bootstrapping' of life into a more complex form. If we might be the mitochondria contributing our little piece to the cell that comes after.
- immibis 2y ago
  Why do you think that an AGI can't be a token predictor?
  - mschuster91 2y ago
    Because an LLM _by definition_ cannot even do basic maths (well, except if you're OpenAI and cheat your way around it by detecting if the user asks a simple math question). I'd expect an actually "general" intelligence Thing to be able to be as versatile in intellectual tasks as a human is - and LLMs are reasonably decent at repetition, but cannot infer something completely new from the data it has.
    - versteegen 2y ago
      Define "by definition". Because this statement really makes no sense. Transformers are perfectly capable (and capable of perfectly) learning mathematical functions, given the necessary working-out space, e.g. for long division or for algebraic manipulation. And they can learn to generalise from their training data very well (although very data-inefficiently). That's their entire strength!
    - dogcomplex 2y ago
      Yet they can get silver medal PhD level competition math scores. Perhaps your "definition" should be simply that LLMs have temporarily seen limitations in their ability to natively do math unassisted by an external memory, but are exceptionally good at very advanced math when they can compensate for their lossy short-term attention memory...
  - Shorel 2y ago
    By analogy with human brains: Because our own brains are far more than the Broca's areas in them. Evolution selects for efficiency. If token prediction could work for everything, our brains would also do nothing else but token prediction. Even the brains of fishes and insects would work like that. The human brain has dedicated clusters of neurons for several different cognitive abilities, including face recognition, line detection, body parts self perception, 3D spatial orientation, and so on.
- Shorel 2y ago
  While I basically agree with everything you say, I have to add some caveats: ChatGPT, while being as far from true AGI as the Elisa chatbot written in Lisp, is extraordinarily more useful, and being used for many things that previously required humans to write the bullshit, like lobbying and propaganda. And Crypto... right now BTC is at an historical highest. It could even go higher. And it will eventually crash again. It's the nature of that beast.
- idiotsecant 2y ago
  All the big LLMs are no longer just token predictors. They are beginning to incorporate memory, chain of thought, and other architectural tricks that use the token predictor in novel ways to produce some startlingly useful output. It's certainly the case that an LLM alone cannot achieve AGI. As a component of a larger system though? That remains to be seen. Maybe all we need to do is duct tape a limbic system and memory onto an LLM and the result is something sort of like an AGI. It's a little bit like saying that a ball bearing can't possibly ever be an internal combustion engine. While true, it's sidestepping the point a little bit.
ChuckMcM 2y ago
I would guess you're not asking a serious question here but if you were feel free to contact me, it's why I put my email address in my profile.
- bigdict 2y ago
  Why are you assuming bad faith?
  - ChuckMcM 2y ago
    What gave you the impression I was assuming bad faith? It's off topic to the discussion (which is fine) but can be annoying in the middle of an HN thread.
    - bigdict 2y ago
      > What gave you the impression I was assuming bad faith? You said "I would guess you're not asking a serious question here"
    - ripped_britches 2y ago
      It was a direct quote from your original comment
    - bruce343434 2y ago
      You brought it up...
    - ossopite 2y ago
      Without offering any opinion on its merits, if you think justifying this controversial claim is off topic, then so is the claim and you shouldn't have written it.
    - kragen 2y ago
      You said, "I would guess you're not asking a serious question here," which is to say, you were guessing that the question was asked in bad faith. Or, at any rate, you would, if for some reason the question came up, for example in deciding how to answer it. Which is what you were doing. That is to say, you did guess that it was asked in bad faith. Given the minimal amount of evidence available (12 words and a nickname "__Joker") I think it's reasonable to describe that guess as an assumption. Ergo, you were assuming bad faith.

"While I continue to believe that many people are going to collectively lose trillions of dollars ultimately pursuing "AI" at this stage" Can you please explain more why you think so ? Thank you.

mschuster91 2y ago

It's a hype cycle with many of the hypers and deciders having zero idea about what AI actually is and how it works. ChatGPT, while amazing, is at its core a token predictor, it cannot ever get to an AGI level that you'd assume to be competitive to a human, even most animals. And just as every other hype cycle, this one will crash down hard. The crypto crashes were bad enough but at least gamers got some very cheap GPUs out of all the failed crypto farms back then, but this time so much more money, particularly institutional money, is flowing around AI that we're looking at a repeat of Lehman's once people wake up and realize they've been scammed.

KronisLV 2y ago
> And just as every other hype cycle, this one will crash down hard. Isn't that an inherent problem with pretty much everything nowadays: crypto, blockchain, AI, even the likes of serverless and Kubernetes, or cloud and microservices in general. There's always some hype cycle where the people who are early benefit and a lot of people chasing the hype later lose when the reality of the actual limitations and the real non-inflated utility of each technology hits. And then, a while later, it all settles down. I don't think the current "AI" is special in any way, it's just that everyone tries to get rich (or benefit in other ways, as in the microservices example, where you still very much had a hype cycle) quick without caring about the actual details.
- anon373839 2y ago
  > I don't think the current "AI" is special in any way As someone who loves to pour ice water on AI hype, I have to say: you can't be serious. The current AI tech has opened up paths to develop applications that were impossible just a few years ago. Even if the tech freezes in place, I think it will yield substantial economic value in the coming years. It's very different from crypto, the main use case for which appears to be money laundering.
  - carlmr 2y ago
    >It's very different from crypto, the main use case for which appears to be money laundering. Which has substantial economic value (for certain groups of people).
    - lazide 2y ago
      According to this random estimate, black market economy alone in just the US is worth ~ $2 trillion/yr. [https://www.investopedia.com/terms/u/underground-economy.asp https://www.investopedia.com/terms/u/underground-economy.asp] Roughly 11-12% of GDP. In many countries, black+grey market is larger than the ‘white’ market. The US is notoriously ‘clean’ compared to most (probably top 10). Even in the US, if you suddenly stopped 10-12% of GDP we’re talking ‘great depression’ levels of economic pain. Honestly, the only reason Crypto isn’t bigger IMO is because there is such a large and established set of folks doing laundering in the ‘normal’ system, and those work well enough there is not nearly as much demand as you’d expect.
      - carlmr 2y ago
        >Honestly, the only reason Crypto isn’t bigger IMO is because there is such a large and established set of folks doing laundering in the ‘normal’ system, and those work well enough there is not nearly as much demand as you’d expect. Correct me if I'm wrong. Doesn't the public ledger make it really bad for this purpose when enough is known about some of the actors interacting on the chain here? It's basically an exact recording that everybody can see, of who interacted with whom, based on pseudonyms, right?
        lazide 2y ago
        You are not wrong. Cash is superior in the ability to have intermediate transactions hidden. However it has similar issues in that bills are serialized, and I believe scanned/tracked - or at least there is the capability to do so. So in theory, when moving it out of and back into the system, it can be tied to real identities not just pseudonyms too. Though frankly, it seems like no one really cares most of the time.
- lillecarl 2y ago
  Both Kubernetes and serverless (FaaS) is here to stay. Microservices is just an excuse to build shit software.
  - KronisLV 2y ago
    > Both Kubernetes and serverless (FaaS) is here to stay. *in environments and projects where they are a good fit > Microservices is just an excuse to build shit software. *in environments and projects where they are a bad fit
CamperBob2 2y ago
it cannot ever get to an AGI level that you'd assume to be competitive to a human, even most animals. Suppose you turn out to be wrong. What would convince you?
- ChuckMcM 2y ago
  It could diagram a sentence it had never seen.
  - karolist 2y ago
    It does that all the time even now. Play with temp, top_k, min_p and nothing is preventing you from getting combinations and order of tokens previously not encountered in history.
  - kragen 2y ago
    I did eventually get an LLM to produce what seems to be a correct diagram of a sentence it had never seen, but it took about ten tries. Grammatical analysis seems to have happened correctly every time, but getting to a usable diagram was difficult. (I know that it's generally rude to include LLM output in HN comments, but in this case I think it's essential supporting material to elevate the discussion of LLM capabilities above "yes it is", "no it isn't".) First, I just asked ChatGPT (GPT-4o mini, what they offer anonymous free users): > Please diagram the sentence, "If my mother's road had been cultural, my uncle's sloth squad residue would have compiled a periodic meadow of night." I just invented that sentence, picking several words with a random number generator, but of course I can't prove to you that I didn't copy it from some book you haven't seen. (For that, you'd have to invent a sentence yourself, or at least provide me with some words to include in it.) I hope you can at least agree that it's a highly unlikely sentence for anyone ever to have uttered before! After talking to itself for a few paragraphs, it arrived at the following grammatical analysis: Step 4: Diagramming the sentence Main Clause: Subject: "my uncle's sloth squad residue" Verb: "would have compiled" Direct Object: "a periodic meadow of night" "a periodic meadow" (noun phrase) "of night" (prepositional phrase modifying "meadow") Conditional Clause: Subject: "my mother's road" Verb: "had been" (linking verb) Predicate Adjective: "cultural" This is exactly correct. From that it produced the following ASCII art "diagram", which looks like nonsense to me, but maybe it's in some kind of sentence-diagramming formalism I haven't seen: If ______/______ | | my mother's had been road | cultural my uncle's sloth squad residue | would have compiled | a periodic meadow of night | of night I told it the diagram looked like nonsense, and it produced this one, which looks worse: If _______/______ | | my mother's road had been | | cultural (Main Clause) | my uncle's sloth squad residue | would have compiled | a periodic meadow | of night I asked GPT-4 (paid) the same question, and it gave me another exactly correct grammatical analysis in words: 1. **Identify the main clauses**: - "If my mother's road had been cultural" is a dependent clause (specifically, a conditional clause). - "my uncle's sloth squad residue would have compiled a periodic meadow of night" is the main clause. 2. **Start with the main clause**: - Subject: "my uncle's sloth squad residue" - "my uncle's" modifies "sloth squad," which in turn modifies "residue." - Predicate: "would have compiled" - This includes the modal auxiliary verb ("would"), perfect auxiliary ("have"), and main verb ("compiled"). - Direct object: "a periodic meadow of night" - “a” is an article modifying “meadow,” - “periodic” modifies “meadow,” - “of night” is a prepositional phrase modifying “meadow.” 3. **Diagram the dependent clause**: - Subject: "road" - Modified by possessive noun phrase “my mother’s” - Predicate: "had been" - Complement/Adjective: "cultural" (I feel like this is definitely "a level that you'd assume to be competitive to a human", in mschuster91's phrase; most native English speakers can't do that.) But then it came up with the following incorrect diagram: +---------------------------------------------------+ | Main Clause | +---------------------------------------------------+ / | \ / | \ / | \ [My uncle's] [sloth squad] [residue] \ / / \ / / \ / / would have compiled ------------------- \ \ \ [a periodic meadow of night] / | \ / | \ a periodic [of night] +---------------------+ | Dependent Clause | +---------------------+ If | road ___|_____ | | my mother's had been cultural Telling it the diagram was nonsense didn't help. But my conclusion from this is not that GPT-4 can't analyze the grammatical structure of sentences it had never seen. It's that GPT-4 gets "confused" when it tries to draw ASCII art, like a person trying to tell you how to draw it over the telephone, but not quite as bad. I've seen similarly abominable results when it tried to draw circuit schematics and guitar tablatures as ASCII art. I tried asking it for a graphviz diagram, which was syntactically correct graphviz code but didn't express the sentence structure properly. I tried simplifying the problem with the prompt, 'Please provide a syntactic tree showing the syntactic derivation of the sentence, "My uncle's sloth squad residue would have compiled a periodic meadow of night," using graphviz syntax and with individual words at the leaf nodes of the tree.' which yielded a slightly incorrect diagram. (I'm still sure nobody has ever uttered this sentence before.) On being told, "This diagram is not quite right.", GPT-4 produced the following diagram, which I believe is correct except for an extra "s" on "uncle" (you can see this in your browser at https://is.gd/Axufjx https://is.gd/Axufjx): digraph G { node [shape=plaintext]; S [label="S"]; NP [label="NP"]; VP [label="VP"]; S -> NP; S -> VP; // Noun Phrase breakdown DP_my [label="DP"]; DP_my -> My; UnclePossessive [label="NP"]; UnclePossessive -> Uncles; UnclePossessive -> ApostropheS; NP -> DP_my; NP -> UncleCompound; UncleCompound [label="NP"]; SlothSquadResidue[label = "N'"]; SlothSquadResidue-> Sloth; SlothSquadResidue-> SquadResidue; SquadResidue[label = "N'"] SquadResidue-> Squad; SquadResidue-> Residue; UncleCompound-> UnclePossessive; UncleCompound-> SlothSquadResidue; // Verb Phrase breakdown ModalWould[label = "Modal"]; ModalWould-> Would; AuxHave[label = "Aux"]; AuxHave-> Have; VCompiled[label = "V"]; VCompiled-> Compiled; ObjectNP[label = "NP"]; // Direct object noun phrase ArticleA[label = "D"]; ArticleA-> A; MeadowPhrase[label = "N'"]; // 'Meadow' modified by 'periodic' AdjPeriodic[label = "Adj"]; AdjPeriodic-> Periodic; MeadowWord[label = N]; MeadowWord -> Meadow; MeadowPhrase -> AdjPeriodic; MeadowPhrase -> MeadowWord; PPofNight[label = PP]; // Prepositional phrase 'of night' PrepOf[label=P] PrepOf-> Of; NightNounPhrase[label=N] NightNounPhrase-> Night; PPofNight -> PrepOf; PPofNight -> NightNounPhrase; ObjectNP -> ArticleA; ObjectNP -> MeadowPhrase; ObjectNP -> PPofNight; VP -> ModalWould; VP -> AuxHave; VP -> VCompiled VP -> ObjectNP } Claude produced a reasonably serviceable diagram on the first try with that prompt: digraph SyntacticTree { node [shape=plaintext]; S -> NP1; S -> VP; NP1 -> DET1 [label="Det"]; NP1 -> NP2 [label="NP"]; NP2 -> NP3 [label="NP"]; NP2 -> N1 [label="N"]; NP3 -> N2 [label="N"]; NP3 -> POS [label="Pos"]; VP -> AUX1 [label="Aux"]; VP -> AUX2 [label="Aux"]; VP -> V [label="V"]; VP -> NP4 [label="NP"]; NP4 -> DET2 [label="Det"]; NP4 -> ADJ [label="Adj"]; NP4 -> N3 [label="N"]; NP4 -> PP [label="PP"]; PP -> P [label="P"]; PP -> NP5 [label="NP"]; NP5 -> N4 [label="N"]; DET1 -> "My"; N2 -> "uncle"; POS -> "'s"; N1 -> "sloth"; N1 -> "squad"; N1 -> "residue"; AUX1 -> "would"; AUX2 -> "have"; V -> "compiled"; DET2 -> "a"; ADJ -> "periodic"; N3 -> "meadow"; P -> "of"; N4 -> "night"; } On being told, I think incorrectly, "This diagram is not quite right.", it produced a worse diagram. So LLMs didn't perform nearly as well on this task as I thought they would, but they also performed much better than you thought they would.
dsign 2y ago
Those glorified token predictors are the missing piece in the puzzle of general intelligence. There is a long way to go still in putting all those pieces together, but I don't think any of the steps left are in the same order of "we need a miracle breakthrough". That said, I believe that this is going one of two ways: we use AI to make things materially harder for humans, in a scale from "you don't get this job" to "oops, this is Skynet", with many unpleasant stops in the middle. By the amount of money going into AI right now and most of the applications I'm seeing being hyped, I don't think we have have any scruples with this direction. The other way this can go, and Cerebras is a good example, is that we increase our compute capability and our AI-usefulness to a point where we can fight cancer and stop/revert aging, both being a computational problem at this point. Even if most people don't realize it, or most people have strong moral objections to this outcome and don't even want to talk about it, so it probably won't happen. In simpler words, I think we want to use AI to commit species suicide :-)
- Shorel 2y ago
  I'm sure there are more missing pieces. We are more than Broca's areas. Our intelligence is much more than linguistic intelligence. However, and this is also an important point, we have built language models far more capable than any language model a single human brain can have. Makes me shudder in awe of what's going to happen when we add the missing pieces.
  - idiotsecant 2y ago
    Yes, I sometimes wonder if what we're witnessing in our lifetimes is the next stage of the 'bootstrapping' of life into a more complex form. If we might be the mitochondria contributing our little piece to the cell that comes after.
immibis 2y ago
Why do you think that an AGI can't be a token predictor?
- mschuster91 2y ago
  Because an LLM _by definition_ cannot even do basic maths (well, except if you're OpenAI and cheat your way around it by detecting if the user asks a simple math question). I'd expect an actually "general" intelligence Thing to be able to be as versatile in intellectual tasks as a human is - and LLMs are reasonably decent at repetition, but cannot infer something completely new from the data it has.
  - versteegen 2y ago
    Define "by definition". Because this statement really makes no sense. Transformers are perfectly capable (and capable of perfectly) learning mathematical functions, given the necessary working-out space, e.g. for long division or for algebraic manipulation. And they can learn to generalise from their training data very well (although very data-inefficiently). That's their entire strength!
  - dogcomplex 2y ago
    Yet they can get silver medal PhD level competition math scores. Perhaps your "definition" should be simply that LLMs have temporarily seen limitations in their ability to natively do math unassisted by an external memory, but are exceptionally good at very advanced math when they can compensate for their lossy short-term attention memory...
- Shorel 2y ago
  By analogy with human brains: Because our own brains are far more than the Broca's areas in them. Evolution selects for efficiency. If token prediction could work for everything, our brains would also do nothing else but token prediction. Even the brains of fishes and insects would work like that. The human brain has dedicated clusters of neurons for several different cognitive abilities, including face recognition, line detection, body parts self perception, 3D spatial orientation, and so on.
Shorel 2y ago
While I basically agree with everything you say, I have to add some caveats: ChatGPT, while being as far from true AGI as the Elisa chatbot written in Lisp, is extraordinarily more useful, and being used for many things that previously required humans to write the bullshit, like lobbying and propaganda. And Crypto... right now BTC is at an historical highest. It could even go higher. And it will eventually crash again. It's the nature of that beast.
idiotsecant 2y ago
All the big LLMs are no longer just token predictors. They are beginning to incorporate memory, chain of thought, and other architectural tricks that use the token predictor in novel ways to produce some startlingly useful output. It's certainly the case that an LLM alone cannot achieve AGI. As a component of a larger system though? That remains to be seen. Maybe all we need to do is duct tape a limbic system and memory onto an LLM and the result is something sort of like an AGI. It's a little bit like saying that a ball bearing can't possibly ever be an internal combustion engine. While true, it's sidestepping the point a little bit.

ChuckMcM 2y ago

I would guess you're not asking a serious question here but if you were feel free to contact me, it's why I put my email address in my profile.

bigdict 2y ago
Why are you assuming bad faith?
- ChuckMcM 2y ago
  What gave you the impression I was assuming bad faith? It's off topic to the discussion (which is fine) but can be annoying in the middle of an HN thread.
  - bigdict 2y ago
    > What gave you the impression I was assuming bad faith? You said "I would guess you're not asking a serious question here"
  - ripped_britches 2y ago
    It was a direct quote from your original comment
  - bruce343434 2y ago
    You brought it up...
  - ossopite 2y ago
    Without offering any opinion on its merits, if you think justifying this controversial claim is off topic, then so is the claim and you shouldn't have written it.
  - kragen 2y ago
    You said, "I would guess you're not asking a serious question here," which is to say, you were guessing that the question was asked in bad faith. Or, at any rate, you would, if for some reason the question came up, for example in deciding how to answer it. Which is what you were doing. That is to say, you did guess that it was asked in bad faith. Given the minimal amount of evidence available (12 words and a nickname "__Joker") I think it's reasonable to describe that guess as an assumption. Ergo, you were assuming bad faith.