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Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it so

Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting democracy. I'm still waiting for AI to figure out self-driving cars, natural language processing, and other "holy grail" problems that researchers have been chipping away at for decades but we are still a long way from having "solved" them. Also in the case of self-driving cars, there is actually a very serious problem of it being difficult to guarantee safety. You can't tie every input to every output to demonstrate how it will behave (it is way, way, wayyyyyyyy too big for that), so you have to show statistically by driving so many miles that it's better. Which is still sound from an engineering perspective! But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times!

p1esk 6y ago
Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and useful, should I care if it happened by accident, or without "understanding"? Can the knowledge encoded in the model parameters be interpreted as some form of understanding? If no, why not? What's missing exactly? What is different from how human scientists operate?
- Judgmentality 6y ago
  Well you can randomly string words together and occasionally get lucky and make a meaningful argument, but that doesn't mean you've created a good method for constructing new ideas. It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.
  - p1esk 6y ago
    can respond to criticisms against its own argument in a coherent way I'm not sure about GPT-3, but let's imagine GPT-4 next year will be able to do this. It just does not strike me as a particularly high bar to clear. Let's go further, and assume GPT-5 in 2022 will pass the Turing Test (you personally will not be able to tell). What would you say then?
    - inimino 6y ago
      Please don't wildly speculate about technologies you clearly don't understand. "Does not strike me as a particularly high bar" means you're unfamiliar with how these systems work at a deep level (by which I mean, you personally cannot sit down at a terminal and build one). So rather than -ahem- making things up and asking "what then?" -- please ask for textbook recommendations on these topics if you'd like to know more.
      - p1esk 6y ago
        I guess we'll just have to wait and see what happens next year :) p.s. I built my first language model (LSTM based) back in 2014. Then I built a VAE based one. Then a GAN based. None of them were especially good, so I switched to music generation (this actually works pretty well). My most recent project is using sparse transformers for raw audio generation. Building novel DL models is literally in my job description. Please don't wildly speculate about strangers you meet on HN.
        inimino 6y ago
        Why just wait when you can bet?
        p1esk 6y ago
        In order to bet, we would have to agree on evaluation criteria. A task like "respond to criticisms against its own argument in a coherent way" is difficult to evaluate. Turing test is also pretty vague, and some people already declared it passed many years ago: https://www.bbc.com/news/technology-27762088 https://www.bbc.com/news/technology-27762088
- joe_the_user 6y ago
  The thing is, if we could write a specific, closed-end, prescriptive definition of "meaningful" or "understanding" or whatever, then we'd be able to program it. And we can't, so we have to settle for something else, usually how a thing fails to be what we (indeed subjectively) consider meaningful. Still, it's not arbitrary. The way that something like GPT-3 tends to fail basically is that you 2-3 paragraphs where paragraph 3 will containment statements that subtly contradict the semantics of paragraph 1. Things like seeing things inside closed drawers and other cues that the thing has no fixed world-model. That's what gives an impression of "meaningless" or "no understanding". (admittedly, I've only played with GPT-2 but this is a description of how texts written by these models at first seem plausible and then implausible as you read more). It's not some philosophical objection akin to "nothing but a human brain can think".
  - deleted 6y ago
    [deleted]
  - p1esk 6y ago
    the thing has no fixed world-model What is "world-model"? What makes you think GPT-3 does not have some kind of a world model? It's clearly not a very good one, but at the same time it does not mean it can't get better. A 3 year old also does not have a very good world model, what's the difference between his world model and one of GPT-3? Again, clearly there's a big difference, I'm just not sure we know enough about what's going on inside 175B model to make any dismissive statements about it. I'm also not sure what will happen if you train a much bigger model on much bigger data. Things might start to emerge at some scale.
    - inimino 6y ago
      We know much more about the world-model of GPT-3 (which is none, but it has a language model) than we do about the three year old (which we can't build or reproduce in silico) but we know plenty well enough to say that no, things are not going to magically "emerge" at some scale. You need something fundamentally different from endless cloze deletion exercises to learn what a drawer is.
    - 6y ago
- wpietri 6y ago
  In communication, meaning is a collaboration between writer and reader. The writer does their best to convey something; the reader does their best to understand. There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data. That's pretty different from communication; it's more a process of internal generation of notions and explanations that could later be conveyed in communication. And then there's the meaning that is even more internal. E.g., reading tarot cards or tea leaves, people generate meaning out of nothing. And then there's Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia If you're saying machine-generated text is meaning in the sense of that last category then sure, it's something we loosely call meaning. But it's fundamentally the same as other sorts of cleromancy [1], just more elaborate. [1] https://en.wikipedia.org/wiki/Cleromancy https://en.wikipedia.org/wiki/Cleromancy
  - p1esk 6y ago
    a process of internal generation of notions How is this "knowledge extraction from data" process different?
    - bregma 6y ago
      Immanuel Kant would call the former "rendering a synthetic judgement" and David Hume termed the latter revealing the a priori knowledge. Plato would call both simply giving substance to the forms. It seems none of this is something new. It's just that you no longer need a good education to learn about old ideas; you can come up with them on your own.
    - wpietri 6y ago
      Different from what? Regular communication? Because the first involves trying to sync up two minds to have the same idea. The latter involves one mind, trying to generate an idea that ends up being useful. Useful in the George Box sense: "All models are wrong, some models are useful."
      - p1esk 6y ago
MiroF 6y ago
There's a ton of problems that a quality language model like GPT-3 can solve, beyond just spamming text generation - stuff like translation quality, automatic post-editing of text, classification, etc.
perl4ever 6y ago
"Sure it's cool, but what problem is it solving?" Isn't it good enough, or very nearly, to generate fake news? And if you can generate something that fools a large percentage of humans, even for a second or two, you can sell ads. It would be solving a problem for anyone who can profit from it. I thought the people who developed it stated that it was too dangerous to release widely? Dangerous = useful to bad people, no?
- thu2111 6y ago
  That was just PR fluff designed to play to the ideological biases of their Valley employee base. They released GPT-2 anyway some months later because other people were going to replicate it anyway, and guess what, the river of fake news we're flooded with daily is still not being generated by AI. It's being generated by journalists with an agenda, same as ever. Don't get me wrong. You can absolutely generate news articles with these text generation models. But if you look at examples of the "fake news" generated by the monster GPT-3 model, it's not really any different to junk churned out by supposedly respectable news organisations i.e. grammatically correct but filled with logical contradictions, false statements that can be checked in 30 seconds with a search engine and so on. Most people already learned which news sources are trustworthy and which are pushing an agenda. If those outlets replace their journalists with GPT-3 it won't make any difference. The readers who don't trust them will still not trust them, and the readers who just want partisan cheerleading will continue to be satisfied.
  - perl4ever 6y ago
    The proportion of people who will spend 30 seconds with a search engine to check on something is practically infinitesimal. Even people who do it frequently don't do it most of the time. And it doesn't matter anyway, because it's too late - you only have to fool people for a second, or a fraction of a second.
    - thu2111 6y ago
      Great, so focus on solving that problem generally instead of worrying about AI. What I see is actually quite different. If the people who could detect fake news was practically infinitesimal then you'd see the vast majority of people having super high trust in the media. In fact most people don't trust the media, lots of polls showing that. Sure they may only fact check something occasionally (often by reading about a topic they happen to understand), but people aren't stupid. After they notice or hear about a few mistakes and observe they're always in the same ideological direction, they get the picture.
jtjbdhsjjdnd 6y ago
In ODS.ai Russian slack-chat a Replika developer has showed the increase in metrics that was provided by GPT-3 in comparison to GPT-2. Here you can see it on Telegram (also in Russian, but the pic is in English) without the access to ODS-slack. https://t.me/dlinnlp/931 https://t.me/dlinnlp/931
imtringued 6y ago
>But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times! This is a completely wrong reading of the situation. Tesla engineers clearly understood it is impossible to determine whether a stationary object is actually on the street or just an overhead sign with the equipment that is available on current Tesla cars and therefore they simply ignore all stationary objects. Because Autopilot is not a self driving technology this is not considered a problem. If a Tesla with enabled Autopilot ever runs into a stationary object it is clearly the driver's fault. It's extremely predictable and since it's easy to blame the driver there is no need to fix the problem by adding the required sensors to Tesla cars.
wharfjumper 6y ago
I don't understand why the goal is to "guarantee safety". It seems to be generally accepted that human error causes approximately 90% of motor vehicle accidents [0]. Surely then the goal of any autonomous or semi-autonomous transport system should be to merely reduce that percentage? If all motor vehicles were "self-driving" and the total number of annual deaths was reduced by one, wouldn't that be a good thing? I would rather reduce my chance of dying early than eliminate the possibility of being killed by a bot. 0. http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-vehicle-crashes http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-...
- joejerryronnie 6y ago
  Because humans are illogical creatures and will ignore objective facts if they believe control (or perceived control) is being taken away from them. The challenges of rolling out true autonomous vehicles are psychological as much as they are technical.

p1esk 6y ago

Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and useful, should I care if it happened by accident, or without "understanding"? Can the knowledge encoded in the model parameters be interpreted as some form of understanding? If no, why not? What's missing exactly? What is different from how human scientists operate?

Judgmentality 6y ago
Well you can randomly string words together and occasionally get lucky and make a meaningful argument, but that doesn't mean you've created a good method for constructing new ideas. It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.
- p1esk 6y ago
  can respond to criticisms against its own argument in a coherent way I'm not sure about GPT-3, but let's imagine GPT-4 next year will be able to do this. It just does not strike me as a particularly high bar to clear. Let's go further, and assume GPT-5 in 2022 will pass the Turing Test (you personally will not be able to tell). What would you say then?
  - inimino 6y ago
    Please don't wildly speculate about technologies you clearly don't understand. "Does not strike me as a particularly high bar" means you're unfamiliar with how these systems work at a deep level (by which I mean, you personally cannot sit down at a terminal and build one). So rather than -ahem- making things up and asking "what then?" -- please ask for textbook recommendations on these topics if you'd like to know more.
    - p1esk 6y ago
      I guess we'll just have to wait and see what happens next year :) p.s. I built my first language model (LSTM based) back in 2014. Then I built a VAE based one. Then a GAN based. None of them were especially good, so I switched to music generation (this actually works pretty well). My most recent project is using sparse transformers for raw audio generation. Building novel DL models is literally in my job description. Please don't wildly speculate about strangers you meet on HN.
      - inimino 6y ago
        Why just wait when you can bet?
        p1esk 6y ago
        In order to bet, we would have to agree on evaluation criteria. A task like "respond to criticisms against its own argument in a coherent way" is difficult to evaluate. Turing test is also pretty vague, and some people already declared it passed many years ago: https://www.bbc.com/news/technology-27762088 https://www.bbc.com/news/technology-27762088
joe_the_user 6y ago
The thing is, if we could write a specific, closed-end, prescriptive definition of "meaningful" or "understanding" or whatever, then we'd be able to program it. And we can't, so we have to settle for something else, usually how a thing fails to be what we (indeed subjectively) consider meaningful. Still, it's not arbitrary. The way that something like GPT-3 tends to fail basically is that you 2-3 paragraphs where paragraph 3 will containment statements that subtly contradict the semantics of paragraph 1. Things like seeing things inside closed drawers and other cues that the thing has no fixed world-model. That's what gives an impression of "meaningless" or "no understanding". (admittedly, I've only played with GPT-2 but this is a description of how texts written by these models at first seem plausible and then implausible as you read more). It's not some philosophical objection akin to "nothing but a human brain can think".
- deleted 6y ago
  [deleted]
- p1esk 6y ago
  the thing has no fixed world-model What is "world-model"? What makes you think GPT-3 does not have some kind of a world model? It's clearly not a very good one, but at the same time it does not mean it can't get better. A 3 year old also does not have a very good world model, what's the difference between his world model and one of GPT-3? Again, clearly there's a big difference, I'm just not sure we know enough about what's going on inside 175B model to make any dismissive statements about it. I'm also not sure what will happen if you train a much bigger model on much bigger data. Things might start to emerge at some scale.
  - inimino 6y ago
    We know much more about the world-model of GPT-3 (which is none, but it has a language model) than we do about the three year old (which we can't build or reproduce in silico) but we know plenty well enough to say that no, things are not going to magically "emerge" at some scale. You need something fundamentally different from endless cloze deletion exercises to learn what a drawer is.
  - 6y ago
wpietri 6y ago
In communication, meaning is a collaboration between writer and reader. The writer does their best to convey something; the reader does their best to understand. There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data. That's pretty different from communication; it's more a process of internal generation of notions and explanations that could later be conveyed in communication. And then there's the meaning that is even more internal. E.g., reading tarot cards or tea leaves, people generate meaning out of nothing. And then there's Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia If you're saying machine-generated text is meaning in the sense of that last category then sure, it's something we loosely call meaning. But it's fundamentally the same as other sorts of cleromancy [1], just more elaborate. [1] https://en.wikipedia.org/wiki/Cleromancy https://en.wikipedia.org/wiki/Cleromancy
- p1esk 6y ago
  a process of internal generation of notions How is this "knowledge extraction from data" process different?
  - bregma 6y ago
    Immanuel Kant would call the former "rendering a synthetic judgement" and David Hume termed the latter revealing the a priori knowledge. Plato would call both simply giving substance to the forms. It seems none of this is something new. It's just that you no longer need a good education to learn about old ideas; you can come up with them on your own.
  - wpietri 6y ago
    Different from what? Regular communication? Because the first involves trying to sync up two minds to have the same idea. The latter involves one mind, trying to generate an idea that ends up being useful. Useful in the George Box sense: "All models are wrong, some models are useful."
    - p1esk 6y ago

MiroF 6y ago

There's a ton of problems that a quality language model like GPT-3 can solve, beyond just spamming text generation - stuff like translation quality, automatic post-editing of text, classification, etc.

perl4ever 6y ago

"Sure it's cool, but what problem is it solving?" Isn't it good enough, or very nearly, to generate fake news? And if you can generate something that fools a large percentage of humans, even for a second or two, you can sell ads. It would be solving a problem for anyone who can profit from it. I thought the people who developed it stated that it was too dangerous to release widely? Dangerous = useful to bad people, no?

thu2111 6y ago
That was just PR fluff designed to play to the ideological biases of their Valley employee base. They released GPT-2 anyway some months later because other people were going to replicate it anyway, and guess what, the river of fake news we're flooded with daily is still not being generated by AI. It's being generated by journalists with an agenda, same as ever. Don't get me wrong. You can absolutely generate news articles with these text generation models. But if you look at examples of the "fake news" generated by the monster GPT-3 model, it's not really any different to junk churned out by supposedly respectable news organisations i.e. grammatically correct but filled with logical contradictions, false statements that can be checked in 30 seconds with a search engine and so on. Most people already learned which news sources are trustworthy and which are pushing an agenda. If those outlets replace their journalists with GPT-3 it won't make any difference. The readers who don't trust them will still not trust them, and the readers who just want partisan cheerleading will continue to be satisfied.
- perl4ever 6y ago
  The proportion of people who will spend 30 seconds with a search engine to check on something is practically infinitesimal. Even people who do it frequently don't do it most of the time. And it doesn't matter anyway, because it's too late - you only have to fool people for a second, or a fraction of a second.
  - thu2111 6y ago
    Great, so focus on solving that problem generally instead of worrying about AI. What I see is actually quite different. If the people who could detect fake news was practically infinitesimal then you'd see the vast majority of people having super high trust in the media. In fact most people don't trust the media, lots of polls showing that. Sure they may only fact check something occasionally (often by reading about a topic they happen to understand), but people aren't stupid. After they notice or hear about a few mistakes and observe they're always in the same ideological direction, they get the picture.

jtjbdhsjjdnd 6y ago

In ODS.ai Russian slack-chat a Replika developer has showed the increase in metrics that was provided by GPT-3 in comparison to GPT-2. Here you can see it on Telegram (also in Russian, but the pic is in English) without the access to ODS-slack. https://t.me/dlinnlp/931 https://t.me/dlinnlp/931

imtringued 6y ago

>But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times! This is a completely wrong reading of the situation. Tesla engineers clearly understood it is impossible to determine whether a stationary object is actually on the street or just an overhead sign with the equipment that is available on current Tesla cars and therefore they simply ignore all stationary objects. Because Autopilot is not a self driving technology this is not considered a problem. If a Tesla with enabled Autopilot ever runs into a stationary object it is clearly the driver's fault. It's extremely predictable and since it's easy to blame the driver there is no need to fix the problem by adding the required sensors to Tesla cars.

wharfjumper 6y ago

I don't understand why the goal is to "guarantee safety". It seems to be generally accepted that human error causes approximately 90% of motor vehicle accidents [0]. Surely then the goal of any autonomous or semi-autonomous transport system should be to merely reduce that percentage? If all motor vehicles were "self-driving" and the total number of annual deaths was reduced by one, wouldn't that be a good thing? I would rather reduce my chance of dying early than eliminate the possibility of being killed by a bot. 0. http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-vehicle-crashes http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-...

joejerryronnie 6y ago
Because humans are illogical creatures and will ignore objective facts if they believe control (or perceived control) is being taken away from them. The challenges of rolling out true autonomous vehicles are psychological as much as they are technical.