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When I read comments like this--and yes I read the article and understand it was generated by an algorithm--I can't help but think the next AI winter is around

When I read comments like this--and yes I read the article and understand it was generated by an algorithm--I can't help but think the next AI winter is around the corner. This does not impress me in the slightest. Taking billions and billions of input corpora and making some of them _sound like_ something a human would say is not impressive. Even if it's at a high school vocabulary level. It may have underlying correlative structure, but there's nothing interesting about the generated artifacts of these algorithms. If we're looking for a cost-effective way to replace content marketing spam... great! We've succeeded! If not, there's nothing interesting or intelligent in these models. I'll be impressed the day I can see a program that can 1) only rely on its own limited experiential inputs and not billions of artifacts (from already mature persons), and 2) come up with the funny insights of a 3-year-old. Little children can say things that sound nonsensical but are intelligent. This sounds intelligent but is nonsensical.

canjobear 6y ago
I think you are underestimating what an advance these models are over previous NLP models in terms of quality. Before GPT-2 we didn't even have models that could reliably generate grammatical sentences. Now we have things that generate coherent (if not beautiful) paragraphs. It seems easy in retrospect, but some of the smartest people around have been working on this for decades.
- abernard1 6y ago
  > I think you are underestimating what an advance these models are over previous NLP models in terms of quality. Yeah I mean, I agree. But in my opinion, it's a case of "doing the wrong thing right" instead of a more useful "doing the right thing wrong." I grant that these automated models are useful for low-value classification/generation tasks at high-frequency scale. I don't think that in any way is related to intelligence though, and the only reason I think they've been pursued is because of immediate economic usefulness _shrug_. When high-value, low-frequency tasks begin to be reproduced by software without intervention, I think we'll be closer to intelligence. This is just mimicry. Change the parameters even in the slightest (e.g. have this algorithm try to "learn" the article it created to actually do something in the world) and it all falls down.
  - natch 6y ago
    This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI. Progress is often made with steps that would have been astonishing a few years ago. And every time the bar is raised higher. Rightly so, but characterizing this as doing the wrong thing is missing the point of what we, and the system, are learning. Yes it's not intelligence. But then, it's not even clear that we ourselves can define intelligence at all… not all philosophers agree on this. Daniel Dennett (philosopher and computer scientist) for example thinks that consciousness may be just a collection of illusions and tricks a mind plays with itself as it models different facets of and lenses into what it stores and perceives.
    - abernard1 6y ago
      > This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI. I think you missed my point. I think we're going in the wrong direction for AI entirely, and these "advances" are fundamentally misguided. OpenAI is explicitly about "intelligence," and so we should question if this is in fact that. It's clear that humans have fundamental intelligence much better than all of this stuff with 6 orders of magnitude less input (at least of the same data sort) on a problem. Perhaps it would be better to say, "I think the ML winter is just around the corner" as opposed to "the AI winter is just around the corner." That said, this really is math, and these algos still don't actually do anything resembling true intelligence.
      - canjobear 6y ago
        > think we're going in the wrong direction for AI entirely This direction has produced results that eluded 30+ years of research. What is the evidence that this is the wrong direction?
      - a9h74j 6y ago
        > 6 orders of magnitude less input IIRC the following is attributable to either Margaret Atwood or Iris Murdoch: "A writer should be able to look into a room [full of people] and understand [in breadth] what is going on."
- enjoylife 6y ago
  I marvel at the jump in BLEU or other measures but I'll second the sentiment, it alone is not showing we are making leaps toward what we need. Yes its a large gradient step minimizing our error, but is it really in the right direction? However I will admit GPT-3 being directed by some yet to be invented causal or counterfactual inference model might be the something which defies my expectations.
- pgt 6y ago
  Is there a term for the casual dismissal of breakthrough technologies and ever-moving goalposts for what is considered impressive?
  - pornel 6y ago
    It's simply calling things "A.I." Seriously, a few years ago recognizing if there's a bird in a photo was an example of a "virtually impossible" task: https://xkcd.com/1425/ https://xkcd.com/1425/
  - EvgeniyZh 6y ago
    https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect
    - pgt 6y ago
      Ehahaha, thank you!
  - maest 6y ago
    "God of the gaps"? The original usage is in theology, but the idea is the same.
exolymph 6y ago
> This does not impress me in the slightest. A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately — should blow your entire mind.
- abernard1 6y ago
  > A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately. Did you never do grammar diagrams in grade school? :-) The "context" and structure of language is a formula. When you have billions of inputs to that formula, it's not surprising you can get a fit or push that fit backwards to generate a data set. This algorithm does not "understand" the things it's saying. If it did, that wouldn't be the end of the chain. It could, without training, make investment decisions on that advice, because it would understand the context of what it had just come up with. Plenty of other examples abound. Humans or animals don't get to have their firmware "upgraded" or software "retrained" every time a new hype paper comes out. They have to use a very limited and basically fixed set of inputs + their own personal history for the rest of their lives. And the outputs they create become internalized and used as inputs to other tasks. We could make 1M models that do little tasks very well, but unless they can be combined in such a way that the models cooperate and have agency over time, this is just a math problem. And I do say "just" in a derogatory way here. Most of this stuff could have been done by the scientific community decades ago if they had the hardware and quantity of ad clicks/emails/events/gifs to do what are basically sophisticated linear algebra tasks.
  - exolymph 6y ago
    I bet you can't guess which parts of this are me versus the AI: https://pastebin.com/FHiRR95F https://pastebin.com/FHiRR95F
colordrops 6y ago
> I'll be impressed the day I can see a program that can 1) only rely on its own limited experiential inputs Hasn't the typical human taken in orders of magnitude more data than this example? And the data has been of both direct sensory experience and texts from other people as well.
- abernard1 6y ago
  > Hasn't the typical human taken in orders of magnitude more data than this example? Have you read GPT-3s 175 billion parameters (words, sentences, papers, I don't care) of anything? Do you know all the words used in that corpus? Nobody has or does. A child of a small age can listen to a very small set of things and not just come up with words to communicate with mama and papa what they learned, but they can reuse it. And this I think is key, because the language part of that is at least partially secondarily. The little kid understands what they're talking about even if they have a hard time communicating it to an adult. The fact they take creative leaps to use their extremely limited vocabulary to communicate their knowledge is amazing.
  - ipnon 6y ago
    I recall a researcher filming their child from the day they were born until they began to speak. They wanted to find how many times a child had to hear a word in order to be able to repeat it back to the parent. The result, I think, was that if the child heard the word 2,000 times at all, they would be able to repeat it. But, if they heard the word 600 times at the same place, for instance the end of the couch, that would be enough to repeat it. The human brain requires less training, but to some extent it is pretrained by our genetic code. The human brain will take on a predictable structure with any sort of training. This post was generated using GPT-3. [;)]
    - sah2ed 6y ago
      > I recall a researcher filming their child from the day they were born until they began to speak. Can’t tell if you are kidding or not, but if you aren’t, mind sharing links about the researcher for the curious?
guhayun 6y ago
1) It DOES rely on its own limited input,meta learning 2) Quite irrelevant,that's a motivation problem
lopmotr 6y ago
No new technology is impressive when it comes incrementally. A camera that automatically records the latitude and longitude of where each photo was taken would have blown my mind as a child. I couldn't have conceived any way it might have worked. But nearly all cameras do that now and at most it's a curiosity or a privacy worry, not a blown mind.
metrokoi 6y ago
The article says more about the state of tech blogging than it does GPT-3. I kept thinking "great, another one of these, when are they actually going to show me any results?" We've been conditioned to accept articles where there's a lot of words and paragraphs and paragraphs of buildup, but nothing actually being said.
- nexuist 6y ago
  Is this meant to be sarcastic? (For context, the vast majority of the article was generated by GPT-3 itself).
user_50123890 6y ago
A new human doesn't come out of thin air, evolution has "trained" them with billions of inputs for billions of years.
- deleted 6y ago
  [deleted]

canjobear 6y ago

I think you are underestimating what an advance these models are over previous NLP models in terms of quality. Before GPT-2 we didn't even have models that could reliably generate grammatical sentences. Now we have things that generate coherent (if not beautiful) paragraphs. It seems easy in retrospect, but some of the smartest people around have been working on this for decades.

abernard1 6y ago
> I think you are underestimating what an advance these models are over previous NLP models in terms of quality. Yeah I mean, I agree. But in my opinion, it's a case of "doing the wrong thing right" instead of a more useful "doing the right thing wrong." I grant that these automated models are useful for low-value classification/generation tasks at high-frequency scale. I don't think that in any way is related to intelligence though, and the only reason I think they've been pursued is because of immediate economic usefulness _shrug_. When high-value, low-frequency tasks begin to be reproduced by software without intervention, I think we'll be closer to intelligence. This is just mimicry. Change the parameters even in the slightest (e.g. have this algorithm try to "learn" the article it created to actually do something in the world) and it all falls down.
- natch 6y ago
  This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI. Progress is often made with steps that would have been astonishing a few years ago. And every time the bar is raised higher. Rightly so, but characterizing this as doing the wrong thing is missing the point of what we, and the system, are learning. Yes it's not intelligence. But then, it's not even clear that we ourselves can define intelligence at all… not all philosophers agree on this. Daniel Dennett (philosopher and computer scientist) for example thinks that consciousness may be just a collection of illusions and tricks a mind plays with itself as it models different facets of and lenses into what it stores and perceives.
  - abernard1 6y ago
    > This kind of facile moving the goalposts is imho a cheap shot and (not imho, fact) is a recurring phenomenon as we make incremental progress toward AI. I think you missed my point. I think we're going in the wrong direction for AI entirely, and these "advances" are fundamentally misguided. OpenAI is explicitly about "intelligence," and so we should question if this is in fact that. It's clear that humans have fundamental intelligence much better than all of this stuff with 6 orders of magnitude less input (at least of the same data sort) on a problem. Perhaps it would be better to say, "I think the ML winter is just around the corner" as opposed to "the AI winter is just around the corner." That said, this really is math, and these algos still don't actually do anything resembling true intelligence.
    - canjobear 6y ago
      > think we're going in the wrong direction for AI entirely This direction has produced results that eluded 30+ years of research. What is the evidence that this is the wrong direction?
    - a9h74j 6y ago
      > 6 orders of magnitude less input IIRC the following is attributable to either Margaret Atwood or Iris Murdoch: "A writer should be able to look into a room [full of people] and understand [in breadth] what is going on."
enjoylife 6y ago
I marvel at the jump in BLEU or other measures but I'll second the sentiment, it alone is not showing we are making leaps toward what we need. Yes its a large gradient step minimizing our error, but is it really in the right direction? However I will admit GPT-3 being directed by some yet to be invented causal or counterfactual inference model might be the something which defies my expectations.
pgt 6y ago
Is there a term for the casual dismissal of breakthrough technologies and ever-moving goalposts for what is considered impressive?
- pornel 6y ago
  It's simply calling things "A.I." Seriously, a few years ago recognizing if there's a bird in a photo was an example of a "virtually impossible" task: https://xkcd.com/1425/ https://xkcd.com/1425/
- EvgeniyZh 6y ago
  https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect
  - pgt 6y ago
    Ehahaha, thank you!
- maest 6y ago
  "God of the gaps"? The original usage is in theology, but the idea is the same.

exolymph 6y ago

> This does not impress me in the slightest. A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately — should blow your entire mind.

abernard1 6y ago
> A computer that is actually fluent in English — as in, understands the language and can use it context-appropriately. Did you never do grammar diagrams in grade school? :-) The "context" and structure of language is a formula. When you have billions of inputs to that formula, it's not surprising you can get a fit or push that fit backwards to generate a data set. This algorithm does not "understand" the things it's saying. If it did, that wouldn't be the end of the chain. It could, without training, make investment decisions on that advice, because it would understand the context of what it had just come up with. Plenty of other examples abound. Humans or animals don't get to have their firmware "upgraded" or software "retrained" every time a new hype paper comes out. They have to use a very limited and basically fixed set of inputs + their own personal history for the rest of their lives. And the outputs they create become internalized and used as inputs to other tasks. We could make 1M models that do little tasks very well, but unless they can be combined in such a way that the models cooperate and have agency over time, this is just a math problem. And I do say "just" in a derogatory way here. Most of this stuff could have been done by the scientific community decades ago if they had the hardware and quantity of ad clicks/emails/events/gifs to do what are basically sophisticated linear algebra tasks.
- exolymph 6y ago
  I bet you can't guess which parts of this are me versus the AI: https://pastebin.com/FHiRR95F https://pastebin.com/FHiRR95F

colordrops 6y ago

> I'll be impressed the day I can see a program that can 1) only rely on its own limited experiential inputs Hasn't the typical human taken in orders of magnitude more data than this example? And the data has been of both direct sensory experience and texts from other people as well.

abernard1 6y ago
> Hasn't the typical human taken in orders of magnitude more data than this example? Have you read GPT-3s 175 billion parameters (words, sentences, papers, I don't care) of anything? Do you know all the words used in that corpus? Nobody has or does. A child of a small age can listen to a very small set of things and not just come up with words to communicate with mama and papa what they learned, but they can reuse it. And this I think is key, because the language part of that is at least partially secondarily. The little kid understands what they're talking about even if they have a hard time communicating it to an adult. The fact they take creative leaps to use their extremely limited vocabulary to communicate their knowledge is amazing.
- ipnon 6y ago
  I recall a researcher filming their child from the day they were born until they began to speak. They wanted to find how many times a child had to hear a word in order to be able to repeat it back to the parent. The result, I think, was that if the child heard the word 2,000 times at all, they would be able to repeat it. But, if they heard the word 600 times at the same place, for instance the end of the couch, that would be enough to repeat it. The human brain requires less training, but to some extent it is pretrained by our genetic code. The human brain will take on a predictable structure with any sort of training. This post was generated using GPT-3. [;)]
  - sah2ed 6y ago
    > I recall a researcher filming their child from the day they were born until they began to speak. Can’t tell if you are kidding or not, but if you aren’t, mind sharing links about the researcher for the curious?

guhayun 6y ago

1) It DOES rely on its own limited input,meta learning 2) Quite irrelevant,that's a motivation problem

lopmotr 6y ago

No new technology is impressive when it comes incrementally. A camera that automatically records the latitude and longitude of where each photo was taken would have blown my mind as a child. I couldn't have conceived any way it might have worked. But nearly all cameras do that now and at most it's a curiosity or a privacy worry, not a blown mind.

metrokoi 6y ago

The article says more about the state of tech blogging than it does GPT-3. I kept thinking "great, another one of these, when are they actually going to show me any results?" We've been conditioned to accept articles where there's a lot of words and paragraphs and paragraphs of buildup, but nothing actually being said.

nexuist 6y ago
Is this meant to be sarcastic? (For context, the vast majority of the article was generated by GPT-3 itself).

user_50123890 6y ago

A new human doesn't come out of thin air, evolution has "trained" them with billions of inputs for billions of years.

deleted 6y ago
[deleted]