7 ms·

>What we're seeing is the first instance, still very limited and imperfect, of AGI absolutely not. language-model text generation is actually about as non-gene

>What we're seeing is the first instance, still very limited and imperfect, of AGI absolutely not. language-model text generation is actually about as non-general as it gets -- they are fundamentally incapable of understanding anything at all, ever. they can't do math, work through basic logic problems, or produce any output that isn't just an assumed logical continuation of the input.

throw310822 4y ago
I don't agree. Besides the fact that I can't do much math either, except with a lot of effort and tools, at this point there are masses of examples of GPTs doing basic reasoning and logic and solving problems. Of course you can say that they do it by "finding the most probable continuation"- and you're right. But that doesn't change the fact that it works. Simply because to find the most probable continuation ultimately you need semantic understanding, and somewhere, back-propagating from the training text, the NNs have managed to build a decent model of the world. There's no other way to explain their performance, these are not markov chain text generators.
adastra22 4y ago
Have you actually used any of these products? GPT et al are perfectly capable of taking knowledge from any one domain and applying it towards the solution of any other problem domain, through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. It makes plenty of goofups along the way, just like humans do. But if your requirement is that it performs absolutely perfectly in new domains without making any mistake or needing any supervision... well that is certainly a requirement no human could ever meet, either.
- RhysU 4y ago
  To be completely ridiculous, we'll have AGI when it can convince a human to mate with it over any other human followed by raising its offspring to a successful adulthood. This is the game we're all playing.
  - pixl97 4y ago
    Parasites are also lifeforms. It doesn't have to convince you to breed, it has to convince you to keep it plugged in and feeding ever increasing amounts or computing power. In theory GPT is there with the right prompt.
    - barking_biscuit 4y ago
      >It doesn't have to convince you to breed, it has to convince you to keep it plugged in and feeding ever increasing amounts or computing power. It's already succeeding at that.
- JohnFen 4y ago
  > through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. No, that's exactly not how LLMs work. They are extremely good at predicting what sentences resemble the sentences in their training data and creating those. That's all. People are getting tripped up because they are seeing legitimate intelligence in the output from these systems -- but that intelligence was in the people who wrote the texts that it was trained with, not in the LLM.
  - wokwokwok 4y ago
    I feel like there are two parallel discourses going on here, and it's crazy. On the one hand, we have LLM, and people arguing that they are simply memorizing the internet and what you're getting is a predictive regurgitation from what actual people have said. On the other hand, you have AI Art, and people arguing that it's not just copy-pasting the images it's recognized, and it's actually generating novel outputs by learning 'how to draw'. Do you see a commonality? It's that people are arguing whatever happens to be convenient for them. If a model can generate human-like responses, and it has a large input token size that effectively allows it to maintain a 'memory' by sticking the history in as the input rather than being a one-shot text generator... Really. What is the difference between that and AGI? Does your AGI definition mean you have to have demonstrated understanding of the underlying representations that are put in as text? Does it have to be error free? What fundamental aspect of probabilistic text generation means that it can't be AGI? ...because, it seems to me that it's incredibly convenient to define AGI as something that can't be represented by a LLM, when all you have really is a probabilistic output generator, and a model that currently doesn't do anything interesting. ...and it doesn't. It's not AGI. Right now; but your comment suggests that because of the technical process that the output is generated by that LLMs are fundamentally unable to produce AGI; and I think that's not correct. The technical process is not relevant; it's simply that these models are not sophisticated enough to really be considered AGI. ...but a 5000 billion param model with a billion character token size? I dunno. I think it might start looking pretty hard to argue about.
    - highspeedbus 4y ago
      I have the same sentiment. To me, there's two kinds of groups in most recent discussions about GPT: those who don't understand the underlying functionality at all and those who think they deeply understand it down to its bits. The second group seems to be very stubborn in downplaying GPT et al capabilities. What's curious is that, for the first time in history of AI field, the source of general amazement is coming straight from AI responses, rather than some news or corporate announcement about how the thing works or what it will be able to do for you.
- andrepd 4y ago
  Nope, they are good at prete ding they do. Ask it something that can't be googled in 30s and you will see this. I'm skeptical you can simply scale up this approach to full AGI.
iameli 4y ago
People seem so convinced of this and I just don't get it. I'm seeing this comment through my eyeballs, generating some pertinent text in my brain, and outputting it back out. But so many people seem convinced convinced this process is something radically, fundamentally, irreducibly different than what ChatGPT is doing internally, and I don't get why. Is it because I have a consciousness with an internal narrative and ChatGPT does not? Because that seems like more of a result of how we've wired up ChatGPT to operate than a fundamental structural difference; nothing stopping us from making ChatGPT talk to itself in its brain to generate synthesis.
- JohnFen 4y ago
  It's because whatever you and I are doing, it's not simply statistical analysis. That is all that ChatGPT is doing. It may be possible to create a machine that flirts with actual intelligence, but this is simply not it. There's not even room for doubt about this.
  - iameli 4y ago
    I can't claim to understand everything my brain is doing, but accepting input and filtering it through a bunch of neuron chains to result in some kind of output sounds like "statistical analysis" to me. You seem convinced that ChatGPT will never have "actual intelligence" — care to make a prediction about something that LLMs will never accomplish? We know they can write code, write essays, generate artwork, and play chess. What's a task that requires "actual intelligence"? Parenting a child? Running for President? Making a steak sandwich?
    - JohnFen 4y ago
      An LLM will never be able to produce something that cannot be statistically derived from its training data. So... creating a new language, maybe?
      - iameli 4y ago
        ChatGPT's attempt: https://gist.github.com/iameli/d9a5b715ec9baa5b11063888e054d36a https://gist.github.com/iameli/d9a5b715ec9baa5b11063888e054d... Are those two responses enough to say it's invented a language? Probably not. But it's already farther along than I would have gotten if you asked me to do such a thing. If I kept prompting it for hours, I bet it would start to contradict itself and lose track of the rules it had already established, but so would I. If I were actually inventing a language, I'd take months and keep extensive cross-referenced documentation on grammar and syntax and vocabulary. We don't _let_ ChatGPT do that, it has no mechanism for persisting its ideas like that. But like... neither would I if you took away my notebook and the parts of my brain that persist long-term memory. I guess my interpretation here - I see differences in _capabilities_, but not differences in actual _intelligence_.
lurquer 4y ago
I used to think so. But then I reconsidered. Those poo-pooing LLMs say it is merely ‘a fancier version of autocompletion.’ Or, they make comments (correctly) that ‘it isn’t reasoning… it’s just guessing which word ought come next.’ Such a point-of-view is similar to thinking, in regard to a circle saw, “it doesn’t ‘want’ to cut off your hand! It’s just a circle of spinning serrated steel!” The human race is about to get its hand cut off. We are in a bad place. So much time spent being wasted debating how to make LLMs ‘safe’ by ensuring they don’t inadvertently say something racist! People are utterly missing the point as to the true danger.
barking_biscuit 4y ago
What about Toolformer?
sovande 4y ago
> they can't do math, work through basic logic problems ChatGPT begs to differ: Me: How do I measure 4 liters when I have a 3 liter bottle and a 5 liter bottle? ChatGPT: Here are the steps to measure 4 liters using a 3 liter and a 5 liter bottle: Fill the 5 liter bottle completely with water. Pour 3 liters from the 5 liter bottle into the 3 liter bottle, leaving 2 liters in the 5 liter bottle. Now you have 3 liters of water in the 3 liter bottle, and 2 liters of water in the 5 liter bottle. Empty the 3 liter bottle, and pour the 2 liters from the 5 liter bottle into the 3 liter bottle. Fill the 5 liter bottle again with water, and pour 1 liter from the 5 liter bottle into the 3 liter bottle, which already has 2 liters of water. You will be left with 4 liters of water in the 5 liter bottle, which is the desired amount.
- waisbrot 4y ago
  OTOH, ChatGPT also agrees with kurisufag. I entered your prompt and got the same reply from ChatGPT. Then I changed "4 liters" to "4 gallons" and got this: To measure 4 gallons of liquid using a 3-liter and a 5-liter bottle, you would need to convert gallons to liters first. One gallon is approximately equal to 3.785 liters, so 4 gallons is approximately equal to 15.14 liters. Since the available bottles have capacities of 3 liters and 5 liters, you will need to fill and empty the bottles several times to reach the target of 15.14 liters. Here's one way to do it: Fill the 5-liter bottle with water. Pour the water from the 5-liter bottle into the 3-liter bottle until it's full, leaving 2 liters of water in the 5-liter bottle. Pour the 2 liters of water from the 3-liter bottle into a separate container. Repeat steps 1-3 two more times, which will leave you with a total of 6 liters of water in the separate container. Fill the 5-liter bottle with water again and pour as much as possible into the separate container until it's full. This will add 3 liters of water to the container, for a total of 9 liters. Fill the 3-liter bottle with water and pour it into the separate container until it's full. This will add another 3 liters of water to the container, for a total of 12 liters. Fill the 3-liter bottle with water again and pour as much as possible into the separate container until it's full. This will add 1 liter of water to the container, for a total of 13 liters. Fill the 5-liter bottle with water and pour as much as possible into the separate container until it's full. This will add another 2 liters of water to the container, for a total of 15 liters, which is approximately 4 gallons. Keep in mind that this method is approximate and assumes that 1 gallon is equal to 3.785 liters. If you need a more precise measurement, you may need to use a measuring container that is calibrated in gallons. Starts off OK. Notice that it's eventual goal is to get 15 liters: good! But then it doesn't understand what it's doing so it messes around with the 3 liter bottle instead of knowing 3*5=15. Additionally, one of the times it fills the 3 liter bottle and pours all of it out but somehow only produces 1 liter.
cypress66 4y ago
What you're saying is outright false. If read llm papers you'll see that a lot of benchmarks used are related to math and logic. GPT3 paper as an example https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 I recommend you read that paper as it answers a lot of misconceptions you may have about llms.

throw310822 4y ago

I don't agree. Besides the fact that I can't do much math either, except with a lot of effort and tools, at this point there are masses of examples of GPTs doing basic reasoning and logic and solving problems. Of course you can say that they do it by "finding the most probable continuation"- and you're right. But that doesn't change the fact that it works. Simply because to find the most probable continuation ultimately you need semantic understanding, and somewhere, back-propagating from the training text, the NNs have managed to build a decent model of the world. There's no other way to explain their performance, these are not markov chain text generators.

adastra22 4y ago

Have you actually used any of these products? GPT et al are perfectly capable of taking knowledge from any one domain and applying it towards the solution of any other problem domain, through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. It makes plenty of goofups along the way, just like humans do. But if your requirement is that it performs absolutely perfectly in new domains without making any mistake or needing any supervision... well that is certainly a requirement no human could ever meet, either.

RhysU 4y ago
To be completely ridiculous, we'll have AGI when it can convince a human to mate with it over any other human followed by raising its offspring to a successful adulthood. This is the game we're all playing.
- pixl97 4y ago
  Parasites are also lifeforms. It doesn't have to convince you to breed, it has to convince you to keep it plugged in and feeding ever increasing amounts or computing power. In theory GPT is there with the right prompt.
  - barking_biscuit 4y ago
    >It doesn't have to convince you to breed, it has to convince you to keep it plugged in and feeding ever increasing amounts or computing power. It's already succeeding at that.
JohnFen 4y ago
> through various kinds of data abstraction, reasoning by analogy, and other techniques similar to what humans do. No, that's exactly not how LLMs work. They are extremely good at predicting what sentences resemble the sentences in their training data and creating those. That's all. People are getting tripped up because they are seeing legitimate intelligence in the output from these systems -- but that intelligence was in the people who wrote the texts that it was trained with, not in the LLM.
- wokwokwok 4y ago
  I feel like there are two parallel discourses going on here, and it's crazy. On the one hand, we have LLM, and people arguing that they are simply memorizing the internet and what you're getting is a predictive regurgitation from what actual people have said. On the other hand, you have AI Art, and people arguing that it's not just copy-pasting the images it's recognized, and it's actually generating novel outputs by learning 'how to draw'. Do you see a commonality? It's that people are arguing whatever happens to be convenient for them. If a model can generate human-like responses, and it has a large input token size that effectively allows it to maintain a 'memory' by sticking the history in as the input rather than being a one-shot text generator... Really. What is the difference between that and AGI? Does your AGI definition mean you have to have demonstrated understanding of the underlying representations that are put in as text? Does it have to be error free? What fundamental aspect of probabilistic text generation means that it can't be AGI? ...because, it seems to me that it's incredibly convenient to define AGI as something that can't be represented by a LLM, when all you have really is a probabilistic output generator, and a model that currently doesn't do anything interesting. ...and it doesn't. It's not AGI. Right now; but your comment suggests that because of the technical process that the output is generated by that LLMs are fundamentally unable to produce AGI; and I think that's not correct. The technical process is not relevant; it's simply that these models are not sophisticated enough to really be considered AGI. ...but a 5000 billion param model with a billion character token size? I dunno. I think it might start looking pretty hard to argue about.
  - highspeedbus 4y ago
    I have the same sentiment. To me, there's two kinds of groups in most recent discussions about GPT: those who don't understand the underlying functionality at all and those who think they deeply understand it down to its bits. The second group seems to be very stubborn in downplaying GPT et al capabilities. What's curious is that, for the first time in history of AI field, the source of general amazement is coming straight from AI responses, rather than some news or corporate announcement about how the thing works or what it will be able to do for you.
andrepd 4y ago
Nope, they are good at prete ding they do. Ask it something that can't be googled in 30s and you will see this. I'm skeptical you can simply scale up this approach to full AGI.

iameli 4y ago

People seem so convinced of this and I just don't get it. I'm seeing this comment through my eyeballs, generating some pertinent text in my brain, and outputting it back out. But so many people seem convinced convinced this process is something radically, fundamentally, irreducibly different than what ChatGPT is doing internally, and I don't get why. Is it because I have a consciousness with an internal narrative and ChatGPT does not? Because that seems like more of a result of how we've wired up ChatGPT to operate than a fundamental structural difference; nothing stopping us from making ChatGPT talk to itself in its brain to generate synthesis.

JohnFen 4y ago
It's because whatever you and I are doing, it's not simply statistical analysis. That is all that ChatGPT is doing. It may be possible to create a machine that flirts with actual intelligence, but this is simply not it. There's not even room for doubt about this.
- iameli 4y ago
  I can't claim to understand everything my brain is doing, but accepting input and filtering it through a bunch of neuron chains to result in some kind of output sounds like "statistical analysis" to me. You seem convinced that ChatGPT will never have "actual intelligence" — care to make a prediction about something that LLMs will never accomplish? We know they can write code, write essays, generate artwork, and play chess. What's a task that requires "actual intelligence"? Parenting a child? Running for President? Making a steak sandwich?
  - JohnFen 4y ago
    An LLM will never be able to produce something that cannot be statistically derived from its training data. So... creating a new language, maybe?
    - iameli 4y ago
      ChatGPT's attempt: https://gist.github.com/iameli/d9a5b715ec9baa5b11063888e054d36a https://gist.github.com/iameli/d9a5b715ec9baa5b11063888e054d... Are those two responses enough to say it's invented a language? Probably not. But it's already farther along than I would have gotten if you asked me to do such a thing. If I kept prompting it for hours, I bet it would start to contradict itself and lose track of the rules it had already established, but so would I. If I were actually inventing a language, I'd take months and keep extensive cross-referenced documentation on grammar and syntax and vocabulary. We don't _let_ ChatGPT do that, it has no mechanism for persisting its ideas like that. But like... neither would I if you took away my notebook and the parts of my brain that persist long-term memory. I guess my interpretation here - I see differences in _capabilities_, but not differences in actual _intelligence_.

lurquer 4y ago

I used to think so. But then I reconsidered. Those poo-pooing LLMs say it is merely ‘a fancier version of autocompletion.’ Or, they make comments (correctly) that ‘it isn’t reasoning… it’s just guessing which word ought come next.’ Such a point-of-view is similar to thinking, in regard to a circle saw, “it doesn’t ‘want’ to cut off your hand! It’s just a circle of spinning serrated steel!” The human race is about to get its hand cut off. We are in a bad place. So much time spent being wasted debating how to make LLMs ‘safe’ by ensuring they don’t inadvertently say something racist! People are utterly missing the point as to the true danger.

barking_biscuit 4y ago

What about Toolformer?

sovande 4y ago

> they can't do math, work through basic logic problems ChatGPT begs to differ: Me: How do I measure 4 liters when I have a 3 liter bottle and a 5 liter bottle? ChatGPT: Here are the steps to measure 4 liters using a 3 liter and a 5 liter bottle: Fill the 5 liter bottle completely with water. Pour 3 liters from the 5 liter bottle into the 3 liter bottle, leaving 2 liters in the 5 liter bottle. Now you have 3 liters of water in the 3 liter bottle, and 2 liters of water in the 5 liter bottle. Empty the 3 liter bottle, and pour the 2 liters from the 5 liter bottle into the 3 liter bottle. Fill the 5 liter bottle again with water, and pour 1 liter from the 5 liter bottle into the 3 liter bottle, which already has 2 liters of water. You will be left with 4 liters of water in the 5 liter bottle, which is the desired amount.

waisbrot 4y ago
OTOH, ChatGPT also agrees with kurisufag. I entered your prompt and got the same reply from ChatGPT. Then I changed "4 liters" to "4 gallons" and got this: To measure 4 gallons of liquid using a 3-liter and a 5-liter bottle, you would need to convert gallons to liters first. One gallon is approximately equal to 3.785 liters, so 4 gallons is approximately equal to 15.14 liters. Since the available bottles have capacities of 3 liters and 5 liters, you will need to fill and empty the bottles several times to reach the target of 15.14 liters. Here's one way to do it: Fill the 5-liter bottle with water. Pour the water from the 5-liter bottle into the 3-liter bottle until it's full, leaving 2 liters of water in the 5-liter bottle. Pour the 2 liters of water from the 3-liter bottle into a separate container. Repeat steps 1-3 two more times, which will leave you with a total of 6 liters of water in the separate container. Fill the 5-liter bottle with water again and pour as much as possible into the separate container until it's full. This will add 3 liters of water to the container, for a total of 9 liters. Fill the 3-liter bottle with water and pour it into the separate container until it's full. This will add another 3 liters of water to the container, for a total of 12 liters. Fill the 3-liter bottle with water again and pour as much as possible into the separate container until it's full. This will add 1 liter of water to the container, for a total of 13 liters. Fill the 5-liter bottle with water and pour as much as possible into the separate container until it's full. This will add another 2 liters of water to the container, for a total of 15 liters, which is approximately 4 gallons. Keep in mind that this method is approximate and assumes that 1 gallon is equal to 3.785 liters. If you need a more precise measurement, you may need to use a measuring container that is calibrated in gallons. Starts off OK. Notice that it's eventual goal is to get 15 liters: good! But then it doesn't understand what it's doing so it messes around with the 3 liter bottle instead of knowing 3*5=15. Additionally, one of the times it fills the 3 liter bottle and pours all of it out but somehow only produces 1 liter.

cypress66 4y ago

What you're saying is outright false. If read llm papers you'll see that a lot of benchmarks used are related to math and logic. GPT3 paper as an example https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 I recommend you read that paper as it answers a lot of misconceptions you may have about llms.