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Wow this is 19,000 words. I like his summary at the end: At some level it’s a great example of the fundamental scientific fact that large numbers of simple com

Wow this is 19,000 words. I like his summary at the end: At some level it’s a great example of the fundamental scientific fact that large numbers of simple computational elements can do remarkable and unexpected things. And this: ... But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. Yeah I've been thinking along these lines. ChatGPT is telling us something about language or thought, we just havent got to the bottom of what it is yet. Something along the lines of 'with enough data its easier to model than we expected'.

jw1224 4y ago
> Yeah I've been thinking along these lines. ChatGPT is telling us something about language or thought, we just havent got to the bottom of what it is yet. Something along the lines of 'with enough data its easier to model than we expected'. I’ve been thinking similarly, and am coming to understand and accept we’ll never get to the bottom of it :) The universe is fractal-like in nature. It shouldn’t be a surprise, then, that if “we” have created an intelligence which exists as a subset of “us”, a self-similar process is ultimately responsible for granting us our own intelligence.
- visarga 4y ago
  > 'with enough data its easier to model than we expected' > a self-similar process is ultimately responsible for granting us our own intelligence In my view, intelligence essentially resides within language, specifically in the corpus of language. Both humans and AIs can be effectively colonized by language, as there are innumerable concepts and observations that are transmitted from one mind to another, and now even from mind to LLM. Initially, ideas were limited to human minds, then to small communities, followed by books, computers, and now LLM stands as the ultimate epitome of language replication, in fact one model could contain the whole culture. To be sure, there is a practical intelligence that is learned through personal experiences, but it constitutes only a tiny fraction of our overall intelligence. Hence, both AI and humans have an equal claim to intelligence, because a significant part of our intelligence arises from language.
  - melagonster 4y ago
    Ludwig Wittgenstein had same idea. but finally he found human experience is more then our language.
    - _mhr_ 4y ago
      Could you elaborate please? For those of us not familiar with Wittgenstein's work, could you link to sources depicting before and after his views changed, preferably with summaries.
      - karpierz 4y ago
        Summary of before: https://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus https://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus Summary of after: https://en.wikipedia.org/wiki/Philosophical_Investigations https://en.wikipedia.org/wiki/Philosophical_Investigations
        melagonster 4y ago
        thank you
  - lukeplato 4y ago
    Yann LeCun often argues that animals like cats and dogs are substantially more intelligent than current LLMs [0] and I'd have to agree. I don't see how/why to consider practical knowledge as only constituting a tiny fraction of our overall intelligence. Either way, it's not clear if the GPT-* models will someday produce emergent common sense or if they're going down an entirely wrong path. [0] https://twitter.com/ylecun/status/1622300311573651458 https://twitter.com/ylecun/status/1622300311573651458
    - CamperBob2 4y ago
      LeCun misses the point by a mile, which is weird at his level. LLMs absolutely do perform problem solving, every time you feed them a prompt. The problem-solving doesn't happen on the output side of the model, it happens on the input side. Someone objected that a cat can't write a Python program, and LeCun points out that "Regurgitating Python code does not require any understanding of a complex world." No, but a) interpreting the prompt does require understanding, and good luck finding a dog or cat who will offer any response at all to a request for a Python program; and b) it's hardly "regurgitating" if the output never existed anywhere in the training data. TL,DR: his FoMO is showing.
- deleted 4y ago
  [deleted]
pixl97 4y ago
kurzgesagt has a new episode today about the human machine. https://www.youtube.com/watch?v=TYPFenJQciw https://www.youtube.com/watch?v=TYPFenJQciw One of the topics in the video was emergence, and where we see it in the world as more layers of complexity are added to systems. We're to the point with our algorithms that we are seeing complex system emerge from simple parts.
- aix1 4y ago
  Does anyone know if there's a German version of this video? I tried finding it but couldn't.
twayt 4y ago
It’s by emergent construction that language has this property. It’s no accident. In order to be able to communicate at all across the arbitrary range if subjective human experience, we had to come up with sounds / words / concepts / phrases that would preserve meaning across humans to whatever functional standard necessary. Thus language is fundamentally constructed to be “modelable” whether it be humans or machines doing the modeling. There is a whole other realm of ineffabilities that we screen out because they aren’t modelable by language
strangattractor 4y ago
Maybe some type of cellular automata could explain it;)
kerpotgh 4y ago
[dead]
rhaway84773 4y ago
I think not very far in the future we are gonna look at the belief that there is something extraordinary about human thought the way we think about geocentrism. That we privileged the human brain simply because it’s ours, because I suspect human thought will end up being not much more than something like a pattern matching mechanism.
- realce 4y ago
  Hundreds of millions of years of effort lifted a small veil from life's understanding of understanding, and it took 2 months for people to get spoiled over it. Good grief man.
- bigDinosaur 4y ago
  There does seem to be something particularly cool about consciousness/qualia. I of course can't qualify that meaningfully beyond just finding it kind of awesome.
  - bmacho 4y ago
    I believe that chatGPT is way beyond that level already. It is just it's currently used as "wake up, continue this text, die". But I think robots controlled by chatGPT and doing whatever they want (very soon) will show everyone that it has consciousness.
jmoak3 4y ago
I saw a great comment here, and I will repeat it without the attribution it deserves: We may have realized it's easier to build a brain than to understand one
- deleted 4y ago
  [deleted]
- jonahbenton 4y ago
  Define understand, and does an analog to Godel's incompleteness apply.
  - paulmd 4y ago
    > does an analog to Godel's incompleteness apply not GP but this seems like quite an attractive idea that many people have reached: a brain of a given "complexity" cannot comprehend the activity of another brain of equal or higher complexity. I'm positive I'm cribbing this from scifi somewhere, maybe Clarke Or Asimov, but, it's the same idea as the Chomsky hierarchy, and the Godel theorems seem like a generalization of that to general sets of rules rather than mere "automata". For example, you can generalize a state automata to have N possible actors transitioning state at discrete clock intervals, but each actor can keep transitioning and perhaps even spawn additional ones. The machine never terminates until all actors have reached a termination state. That machine is probably impossible to model on any kind of a Turing machine in polynominal time. And a machine that operates at continuous intervals is of course impossible to model on a Discrete Neural Machine in polynomial time (integers vs reals categorization). There are perhaps a lot of complexity categories here, similar to alephs of infinity or problems in P/NP, and when you generalize the complexity categorization to infinity, you get godel incompleteness, just an abstract set of rules governing this categorization of rule sets and what amounts to their computability/decidability. Everyone is fishing at this same idea, a human has no chance of slicing open a brain (or even imaging it) and having any idea what any of those electrical sparkles mean. At most you could perhaps model some tiny fraction for a tiny quantum, with great effort. We have to rely on machines to assist us for that - probably neural nets, a machine of equal or greater complexity. And we will probably have to rely on machine analysis to be like "ok this ganglion is the geographic center of the AI, and this flash here is the concept of Italy", as far as that even has any meaning at all in a brain. Mere line by line analysis of a Large Language Model or other deep neural network by a human is essentially impossible in any sort of realtime fashion, yeah you can probably model a quantum or two of it statistically and be like "aha this region lights up when we ask about the location of the alps" but the best you are going to do is observational analysis of a small quantum of it during a certain controlled known sequences of events. Unless you build a machine of similar complexity to interpret it. Just like a brain, and just like a state machine emulating a machine of higher complexity-category. They're all the same thing, categories of computability/power. This is not in any way rigorous, just some casual observations of similarities and parallels between these concepts. It seems like everyone is brushing at that same concept, maybe that helps to get it out on paper. For an actual hot take: it seems quite clear that our computability as a consciousness depends on the computing power of a higher complexity machine, the brain. Our consciousnesses are really emulated, we totally do live in a simulation and the simulator is your brain, a machine of higher complexity. Isn't it such a disturbing thought that all your conscious impulses are reduced to a biological machine? Or at least it's of equivalent complexity to one. And the idea that our own conscious and unconscious desires are shaped by this biological machine that may not even be fully explicable. That has been a science fiction theme for a very long time, or the Phineas Gage case, the idea that we are all monsters but for circumstance and we are captives of this biological machine and its unpredictable impulses. We are the neural systems we've trained, and implacable biology they're running on - you change the machine and you also change the person, Phineas Gage was no less conscious and self-cognizent than any of us. He just was a completely different person minus that bit, his conscious being's thought-stream was different because of the biological machine behind it. It's the literal plato's cave, our conscious thoughts are the shadow played out by our biological machine and its program (not to say it's a simple one!). It's not inherently a bad thing - we incorporate distributed linear/biological systems all over the body in addition to consciousness. reflexes fire before nerve impulses are processed by the conscious center, your eyes are chemical photosensors and can respond to extremely quick instantaneous (high shutter speed) "flash" exposures like silhouettes. And the brain is a highly parallel processor that responds to them. But logical consciousness is a very discrete and monodirectional thing compared to these peripheral biological systems and its computational category is fairly low compared to the massively-parallel brain it runs on. but, we've also mastered these other AI/computational-neural systems now to be a force multiplier for us, we can build systems that we direct in logical thought for us (Frank Herbert would like to remind us that this is a sin ;). Tool-making has always been one of the greatest signifiers of intelligence, it may be quintessentially the sign of intelligence in terms of evolution of consciousness between certain tiers of computation. And humanity is about to build really good artificial brains on a working scale in the next 25 years, and probably interface with brains (in good and bad ways) before too many more decades after. But it doesn't make any logical sense to try and explain how the model works on a line by line level, any more than it does with the brain model we based it on. Completely pointless to try, it only makes sense if you look at the whole thing and what's going on, it's about the brainwaves, neurons firing in waves and clusters. /not an AI, just fun at parties, condolences if you read all that shit ;)
seba_dos1 4y ago
> that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. That sounds like a lot of ideas on what makes humans special among other species and how our knowledge on that was being revised over last decades (what's common knowledge on the intelligence of, say, primates or corvids today would be unspeakable blasphemy mere 100 years ago). Various religions have instilled the idea of a human as a sacred entity that's meant to rule over everything because of how special ("made in the image of God") it is, yet we keep learning that we're much simpler than we thought over and over again. I wish for it to result in less hubris in the humanity as a whole.
gowld 4y ago
Chomsky proposed that decades ago. Universal grammar https://en.wikipedia.org/wiki/Universal_grammar https://en.wikipedia.org/wiki/Universal_grammar
britzkopf 4y ago
The thing I'm sort of confused about, but maybe someone can explain why I shouldn't be, is, why does there seem to be no implication for language translation? Or is there but coverage is overwhelmed by the fascination with chatGPT? In short, is machine language translation now a fully solved problem? A couple years ago when I tested Google translate in a non-esoteric conversation with my Russian speaking girlfriend and, although it was useful, in terms of native fluency it failed pretty decisively. But isn't this a much easier problem than that which chatGPT is being marketed (or at least covered in the media) as solving?
- thomashop 4y ago
  I think general translation is kind of solved when it comes to popular languages. Try DeepL. I dont know how well it works for different language pairs to the languagesi know. I dont even know if deepl uses one of the newer large language models
  - Agentlien 4y ago
    What qualifies as popular languages in your opinion? I use DeepL a lot as a first draft when translating stuff from Swedish (~10 million native speakers) or Dutch (~30 million native speakers) to English. While it's good enough as a starting point it regularly negates the meaning of fairly simple sentences, completely misses the use of popular idioms (often resulting in a non sequitur) and more often than not spits out grammatically incorrect nonsense for any sentence relying on implied context.
- Baeocystin 4y ago
  ChatGPT has been blowing every single translation task I've thrown it out of the water, even compared to other modern systems. I have no idea why more people aren't talking about that aspect of it either, other than the Anglosphere in general is kind of oblivious to things that aren't English.
  - int_19h 4y ago
    For Russian, at least, sticking the article (bit by bit) into ChatGPT produces results that are broadly comparable to Bing and Google translators. It is somewhat more likely to pick words that are not direct translations, but might convey the idea better given the likely cultural background of someone speaking the language - for example, it will sometimes (but not always) replace "voodoo" with "witchcraft". However, the overall sentence structure is rather stilted and obviously non-native in places. As others have noted, it doesn't seem to be fully language-aware outside of English. For example, if you ask it to write a poem or a song in English, it will usually make something that rhymes (or you can specifically demand that). But if you do the same for Russian, the result will not rhyme, even when specifically requested, and despite the model claiming that it does. If you ask it to explain what exactly the rhymes are, it will get increasingly nonsensical from there. I tried that after someone on HN complained about the same thing with Dutch, except they also noted that the generated text seemed like it would rhyme in English. I wonder if that has something to do with sentence structure also being wrong. Given that English was predominant in the training corpus, I wonder if the resulting model "thinks" in English, so to speak - i.e. that some part of the resulting net is basically a translator, and the output of that is ultimately fed to the nodes that handle the correlation of tokens if you force it to talk in other languages.
thom 4y ago
I don’t think the language point is particularly revelatory - we’ve lived with quite effective machine translation for a long while now. But it’s certainly unexpected that large swathes of complex knowledge can be gathered and represented this way (as patterns of patterns of patterns). Consequentially, ChatGPT is a still a fairly uninteresting pattern matching machine in itself. It has very static knowledge and no way to reason or ponder or evaluate or experiment between that knowledge and the world beyond, as anyone trying to use ChatGPT to get ‘correct’ answers and not just vaguely cromulent ideas is finding. We’ve perhaps proven that machines can know what we can know, but can’t think as we can think. I would not bet against the latter being solved in my lifetime though.
geraneum 4y ago
> ChatGPT is telling us something about language or thought There is a leap from language to thought and Wolfram talks about it in more detail in the article in the section named “Surely a Network That’s Big Enough Can Do Anything!” I encourage everyone to read the full article. It’s more nuanced than “Language is easy” Here is an excerpt from that section: …But this isn’t the right conclusion to draw [certain tasks being to complex for the computer]. Computationally irreducible processes are still computationally irreducible, and are still fundamentally hard for computers—even if computers can readily compute their individual steps. And instead what we should conclude is that tasks—like writing essays—that we humans could do, but we didn’t think computers could do, are actually in some sense computationally easier than we thought.
LastTrain 4y ago
I don't fear AI but I do fear how people will react to the truths it reveals.

jw1224 4y ago

> Yeah I've been thinking along these lines. ChatGPT is telling us something about language or thought, we just havent got to the bottom of what it is yet. Something along the lines of 'with enough data its easier to model than we expected'. I’ve been thinking similarly, and am coming to understand and accept we’ll never get to the bottom of it :) The universe is fractal-like in nature. It shouldn’t be a surprise, then, that if “we” have created an intelligence which exists as a subset of “us”, a self-similar process is ultimately responsible for granting us our own intelligence.

visarga 4y ago
> 'with enough data its easier to model than we expected' > a self-similar process is ultimately responsible for granting us our own intelligence In my view, intelligence essentially resides within language, specifically in the corpus of language. Both humans and AIs can be effectively colonized by language, as there are innumerable concepts and observations that are transmitted from one mind to another, and now even from mind to LLM. Initially, ideas were limited to human minds, then to small communities, followed by books, computers, and now LLM stands as the ultimate epitome of language replication, in fact one model could contain the whole culture. To be sure, there is a practical intelligence that is learned through personal experiences, but it constitutes only a tiny fraction of our overall intelligence. Hence, both AI and humans have an equal claim to intelligence, because a significant part of our intelligence arises from language.
- melagonster 4y ago
  Ludwig Wittgenstein had same idea. but finally he found human experience is more then our language.
  - _mhr_ 4y ago
    Could you elaborate please? For those of us not familiar with Wittgenstein's work, could you link to sources depicting before and after his views changed, preferably with summaries.
    - karpierz 4y ago
      Summary of before: https://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus https://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus Summary of after: https://en.wikipedia.org/wiki/Philosophical_Investigations https://en.wikipedia.org/wiki/Philosophical_Investigations
      - melagonster 4y ago
        thank you
- lukeplato 4y ago
  Yann LeCun often argues that animals like cats and dogs are substantially more intelligent than current LLMs [0] and I'd have to agree. I don't see how/why to consider practical knowledge as only constituting a tiny fraction of our overall intelligence. Either way, it's not clear if the GPT-* models will someday produce emergent common sense or if they're going down an entirely wrong path. [0] https://twitter.com/ylecun/status/1622300311573651458 https://twitter.com/ylecun/status/1622300311573651458
  - CamperBob2 4y ago
    LeCun misses the point by a mile, which is weird at his level. LLMs absolutely do perform problem solving, every time you feed them a prompt. The problem-solving doesn't happen on the output side of the model, it happens on the input side. Someone objected that a cat can't write a Python program, and LeCun points out that "Regurgitating Python code does not require any understanding of a complex world." No, but a) interpreting the prompt does require understanding, and good luck finding a dog or cat who will offer any response at all to a request for a Python program; and b) it's hardly "regurgitating" if the output never existed anywhere in the training data. TL,DR: his FoMO is showing.
deleted 4y ago
[deleted]

pixl97 4y ago

kurzgesagt has a new episode today about the human machine. https://www.youtube.com/watch?v=TYPFenJQciw https://www.youtube.com/watch?v=TYPFenJQciw One of the topics in the video was emergence, and where we see it in the world as more layers of complexity are added to systems. We're to the point with our algorithms that we are seeing complex system emerge from simple parts.

aix1 4y ago
Does anyone know if there's a German version of this video? I tried finding it but couldn't.

twayt 4y ago

It’s by emergent construction that language has this property. It’s no accident. In order to be able to communicate at all across the arbitrary range if subjective human experience, we had to come up with sounds / words / concepts / phrases that would preserve meaning across humans to whatever functional standard necessary. Thus language is fundamentally constructed to be “modelable” whether it be humans or machines doing the modeling. There is a whole other realm of ineffabilities that we screen out because they aren’t modelable by language

strangattractor 4y ago

Maybe some type of cellular automata could explain it;)

kerpotgh 4y ago

[dead]

rhaway84773 4y ago

I think not very far in the future we are gonna look at the belief that there is something extraordinary about human thought the way we think about geocentrism. That we privileged the human brain simply because it’s ours, because I suspect human thought will end up being not much more than something like a pattern matching mechanism.

realce 4y ago
Hundreds of millions of years of effort lifted a small veil from life's understanding of understanding, and it took 2 months for people to get spoiled over it. Good grief man.
bigDinosaur 4y ago
There does seem to be something particularly cool about consciousness/qualia. I of course can't qualify that meaningfully beyond just finding it kind of awesome.
- bmacho 4y ago
  I believe that chatGPT is way beyond that level already. It is just it's currently used as "wake up, continue this text, die". But I think robots controlled by chatGPT and doing whatever they want (very soon) will show everyone that it has consciousness.

jmoak3 4y ago

I saw a great comment here, and I will repeat it without the attribution it deserves: We may have realized it's easier to build a brain than to understand one

deleted 4y ago
[deleted]
jonahbenton 4y ago
Define understand, and does an analog to Godel's incompleteness apply.
- paulmd 4y ago
  > does an analog to Godel's incompleteness apply not GP but this seems like quite an attractive idea that many people have reached: a brain of a given "complexity" cannot comprehend the activity of another brain of equal or higher complexity. I'm positive I'm cribbing this from scifi somewhere, maybe Clarke Or Asimov, but, it's the same idea as the Chomsky hierarchy, and the Godel theorems seem like a generalization of that to general sets of rules rather than mere "automata". For example, you can generalize a state automata to have N possible actors transitioning state at discrete clock intervals, but each actor can keep transitioning and perhaps even spawn additional ones. The machine never terminates until all actors have reached a termination state. That machine is probably impossible to model on any kind of a Turing machine in polynominal time. And a machine that operates at continuous intervals is of course impossible to model on a Discrete Neural Machine in polynomial time (integers vs reals categorization). There are perhaps a lot of complexity categories here, similar to alephs of infinity or problems in P/NP, and when you generalize the complexity categorization to infinity, you get godel incompleteness, just an abstract set of rules governing this categorization of rule sets and what amounts to their computability/decidability. Everyone is fishing at this same idea, a human has no chance of slicing open a brain (or even imaging it) and having any idea what any of those electrical sparkles mean. At most you could perhaps model some tiny fraction for a tiny quantum, with great effort. We have to rely on machines to assist us for that - probably neural nets, a machine of equal or greater complexity. And we will probably have to rely on machine analysis to be like "ok this ganglion is the geographic center of the AI, and this flash here is the concept of Italy", as far as that even has any meaning at all in a brain. Mere line by line analysis of a Large Language Model or other deep neural network by a human is essentially impossible in any sort of realtime fashion, yeah you can probably model a quantum or two of it statistically and be like "aha this region lights up when we ask about the location of the alps" but the best you are going to do is observational analysis of a small quantum of it during a certain controlled known sequences of events. Unless you build a machine of similar complexity to interpret it. Just like a brain, and just like a state machine emulating a machine of higher complexity-category. They're all the same thing, categories of computability/power. This is not in any way rigorous, just some casual observations of similarities and parallels between these concepts. It seems like everyone is brushing at that same concept, maybe that helps to get it out on paper. For an actual hot take: it seems quite clear that our computability as a consciousness depends on the computing power of a higher complexity machine, the brain. Our consciousnesses are really emulated, we totally do live in a simulation and the simulator is your brain, a machine of higher complexity. Isn't it such a disturbing thought that all your conscious impulses are reduced to a biological machine? Or at least it's of equivalent complexity to one. And the idea that our own conscious and unconscious desires are shaped by this biological machine that may not even be fully explicable. That has been a science fiction theme for a very long time, or the Phineas Gage case, the idea that we are all monsters but for circumstance and we are captives of this biological machine and its unpredictable impulses. We are the neural systems we've trained, and implacable biology they're running on - you change the machine and you also change the person, Phineas Gage was no less conscious and self-cognizent than any of us. He just was a completely different person minus that bit, his conscious being's thought-stream was different because of the biological machine behind it. It's the literal plato's cave, our conscious thoughts are the shadow played out by our biological machine and its program (not to say it's a simple one!). It's not inherently a bad thing - we incorporate distributed linear/biological systems all over the body in addition to consciousness. reflexes fire before nerve impulses are processed by the conscious center, your eyes are chemical photosensors and can respond to extremely quick instantaneous (high shutter speed) "flash" exposures like silhouettes. And the brain is a highly parallel processor that responds to them. But logical consciousness is a very discrete and monodirectional thing compared to these peripheral biological systems and its computational category is fairly low compared to the massively-parallel brain it runs on. but, we've also mastered these other AI/computational-neural systems now to be a force multiplier for us, we can build systems that we direct in logical thought for us (Frank Herbert would like to remind us that this is a sin ;). Tool-making has always been one of the greatest signifiers of intelligence, it may be quintessentially the sign of intelligence in terms of evolution of consciousness between certain tiers of computation. And humanity is about to build really good artificial brains on a working scale in the next 25 years, and probably interface with brains (in good and bad ways) before too many more decades after. But it doesn't make any logical sense to try and explain how the model works on a line by line level, any more than it does with the brain model we based it on. Completely pointless to try, it only makes sense if you look at the whole thing and what's going on, it's about the brainwaves, neurons firing in waves and clusters. /not an AI, just fun at parties, condolences if you read all that shit ;)

seba_dos1 4y ago

> that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. That sounds like a lot of ideas on what makes humans special among other species and how our knowledge on that was being revised over last decades (what's common knowledge on the intelligence of, say, primates or corvids today would be unspeakable blasphemy mere 100 years ago). Various religions have instilled the idea of a human as a sacred entity that's meant to rule over everything because of how special ("made in the image of God") it is, yet we keep learning that we're much simpler than we thought over and over again. I wish for it to result in less hubris in the humanity as a whole.

gowld 4y ago

Chomsky proposed that decades ago. Universal grammar https://en.wikipedia.org/wiki/Universal_grammar https://en.wikipedia.org/wiki/Universal_grammar

britzkopf 4y ago

The thing I'm sort of confused about, but maybe someone can explain why I shouldn't be, is, why does there seem to be no implication for language translation? Or is there but coverage is overwhelmed by the fascination with chatGPT? In short, is machine language translation now a fully solved problem? A couple years ago when I tested Google translate in a non-esoteric conversation with my Russian speaking girlfriend and, although it was useful, in terms of native fluency it failed pretty decisively. But isn't this a much easier problem than that which chatGPT is being marketed (or at least covered in the media) as solving?

thomashop 4y ago
I think general translation is kind of solved when it comes to popular languages. Try DeepL. I dont know how well it works for different language pairs to the languagesi know. I dont even know if deepl uses one of the newer large language models
- Agentlien 4y ago
  What qualifies as popular languages in your opinion? I use DeepL a lot as a first draft when translating stuff from Swedish (~10 million native speakers) or Dutch (~30 million native speakers) to English. While it's good enough as a starting point it regularly negates the meaning of fairly simple sentences, completely misses the use of popular idioms (often resulting in a non sequitur) and more often than not spits out grammatically incorrect nonsense for any sentence relying on implied context.
Baeocystin 4y ago
ChatGPT has been blowing every single translation task I've thrown it out of the water, even compared to other modern systems. I have no idea why more people aren't talking about that aspect of it either, other than the Anglosphere in general is kind of oblivious to things that aren't English.
- int_19h 4y ago
  For Russian, at least, sticking the article (bit by bit) into ChatGPT produces results that are broadly comparable to Bing and Google translators. It is somewhat more likely to pick words that are not direct translations, but might convey the idea better given the likely cultural background of someone speaking the language - for example, it will sometimes (but not always) replace "voodoo" with "witchcraft". However, the overall sentence structure is rather stilted and obviously non-native in places. As others have noted, it doesn't seem to be fully language-aware outside of English. For example, if you ask it to write a poem or a song in English, it will usually make something that rhymes (or you can specifically demand that). But if you do the same for Russian, the result will not rhyme, even when specifically requested, and despite the model claiming that it does. If you ask it to explain what exactly the rhymes are, it will get increasingly nonsensical from there. I tried that after someone on HN complained about the same thing with Dutch, except they also noted that the generated text seemed like it would rhyme in English. I wonder if that has something to do with sentence structure also being wrong. Given that English was predominant in the training corpus, I wonder if the resulting model "thinks" in English, so to speak - i.e. that some part of the resulting net is basically a translator, and the output of that is ultimately fed to the nodes that handle the correlation of tokens if you force it to talk in other languages.

thom 4y ago

I don’t think the language point is particularly revelatory - we’ve lived with quite effective machine translation for a long while now. But it’s certainly unexpected that large swathes of complex knowledge can be gathered and represented this way (as patterns of patterns of patterns). Consequentially, ChatGPT is a still a fairly uninteresting pattern matching machine in itself. It has very static knowledge and no way to reason or ponder or evaluate or experiment between that knowledge and the world beyond, as anyone trying to use ChatGPT to get ‘correct’ answers and not just vaguely cromulent ideas is finding. We’ve perhaps proven that machines can know what we can know, but can’t think as we can think. I would not bet against the latter being solved in my lifetime though.

geraneum 4y ago

> ChatGPT is telling us something about language or thought There is a leap from language to thought and Wolfram talks about it in more detail in the article in the section named “Surely a Network That’s Big Enough Can Do Anything!” I encourage everyone to read the full article. It’s more nuanced than “Language is easy” Here is an excerpt from that section: …But this isn’t the right conclusion to draw [certain tasks being to complex for the computer]. Computationally irreducible processes are still computationally irreducible, and are still fundamentally hard for computers—even if computers can readily compute their individual steps. And instead what we should conclude is that tasks—like writing essays—that we humans could do, but we didn’t think computers could do, are actually in some sense computationally easier than we thought.

LastTrain 4y ago

I don't fear AI but I do fear how people will react to the truths it reveals.