5 ms·

Obviously not. If we had a general solution to language intelligence we would have artificial intelligence at the level of at least human intelligence – which w

by ninjin 4y ago

Obviously not. If we had a general solution to language intelligence we would have artificial intelligence at the level of at least human intelligence – which we do not. Rather, the right question to ask is which language intelligence tasks currently have acceptable performance and under which conditions (text domain, etc.). Clearly this is a much more difficult question and with a lot more nuance to it, even if it is undeniably that things have moved very quickly over the last few years. Skimming the abstract as a senior academic in the area. This looks like preliminary work and a limited investigation for a single (non-standard) task. Thus far from a strong result published at say a top-tier conference or journal. Still, interesting direction and if expanded upon could absolutely be impactful. I should also mention that I am not familiar with the related literature, so it could very much be that there is similar (better?) work out there exploring the same question.

sebzim4500 4y ago
>If we had a general solution to language intelligence we would have artificial intelligence at the level of at least human intelligence I don't see why this follows. We do loads of stuff other than language, it is entirely possible for an AI to be better than us at language but worse at everything else.
famouswaffles 4y ago
We have artificial intelligence that is general and above average human intelligence for the majority of tasks it can perform. Near expert level for some. NLP is a solved problem. Bespoke models are out the door. Large enough LLMs crush anything else for any NLP task. Honestly, this whole "they are not intelligent" argument is becoming ridiculous. might as well argue that a plane isn’t a real bird or a car isn’t a real horse.
- mistrial9 4y ago
  no, a short answer to this is .. these models are probabilistic, therefore they will always have errors along with whatever else. Secondly "intelligence" is not one thing; no one has all of it or none of it, including computers.
  - nico 4y ago
    > these models are probabilistic, therefore they will always have errors There's nothing perfect. Even computers and computer networks need to have error-correcting code because information gets randomly corrupted. Our whole reality is probabilistic. And us humans are way worse than AI at consistency. We even overwrite our own memories all the time, so we can't even be sure what we remember is actually what happened! (btw, this is currently being used in therapy to re-write traumatic memories and help people overcome PTSD). https://www.npr.org/sections/health-shots/2014/02/04/271527934/our-brains-rewrite-our-memories-putting-present-in-the-past https://www.npr.org/sections/health-shots/2014/02/04/2715279...
- ninjin 4y ago
  > We have artificial intelligence that is general and above average human intelligence for the vast majority of tasks it can perform. Even when I give it the benefit of the doubt, this sentence makes no sense to me. Do we have a list of tasks a language model can perform? To the best of my knowledge, they can arguably perform any language task. > Large enough LLMs crush anything else for any NLP task. and evidently they beat top humans too. Yes, they are certainly (rightfully) the go-to model for most tasks at this point if your concern is outright performance. Have I indicated otherwise? As for beating “top” humans, I am sure that can be investigated, but it is a fairly nuanced research question. It is inarguable that they are amazingly good though, especially relative to what we had just a few years ago. > Honestly, this whole "they are not intelligent" argument is becoming ridiculously obtuse. > > might as well argue that a plane isn’t a real bird or a car isn’t a real horse. Which is a claim and argument that I never made – hallucinating? How about you calm down a little and get back on the ground? You are talking to someone that has argued in favour of these kinds of models for about a decade. But that does not mean that I am willing to spout nonsense or lose track of what we know and what we do not yet know.
  - famouswaffles 4y ago
    You said NLP is unsolved because we don't have human level artificial intelligence. We absolutely do. at least by any evaluations we can carry out. no-one wants to call a spade a spade yet but the sentiment is obvious in recent research. directly being called General purpose technologies from the jobs paper, general artificial intelligence from the creativity paper. That last one is particularly funny, they just switched the two words.
- ummonk 4y ago
  > might as well argue that a plane isn’t a real bird or a car isn’t a real horse. They aren’t though… They are far superior at specific things birds and horses are known for, but they can’t do everything that birds and horses can, so they aren’t even artificial birds and horses.
  - famouswaffles 4y ago
    Of course they aren't. The point is that it's irrelevant. what matters is that the plane still flies, the car still drives and the boat still sails. For the people who are now salivating at their potential, or dreading the possibility of being made redundant by them, these large language models are already intelligent enough to matter. Handwringing bout some non-existent difference between "true understanding" and "fake understanding" which by the way nobody seems to be able to actually distinguish (I mean wow such a supposed huge difference and you can't even show me what that is. a distinction you can't test for is not a distinction ) is so far beyond the point, it's increasingly maddening to read.
    - ummonk 4y ago
      Okay I agree with you on that. The technology will be disruptive regardless of whether we attribute true understanding to it, and as we start adding long term memory and planning to these AIs, we will start seeing significant alignment risk as well. This is true regardless of whether we decide to cope by saying they have "fake understanding" and are "stochastic parrots".
- shock-value 4y ago
  The debate over what kind of intelligence these models possess is rightly lively and ongoing. It’s clear that at the least, they can decipher very numerous patterns across a wide range of conceptual depths — it’s an architectural advance easily on the the level of the convolutional neural network, if not even more profound. The idea that NLP is “solved” isn’t a crazy notion, though I won’t take a side on that. That said, it’s equally obvious that they are not AGI unless you have a really uninspired and self-limiting definition of AGI. They are purely feedforward aside from the single generated token that becomes part of the input to the next iteration. Multimodality has not been incorporated (aside from possibly a limited form in GPT-4). Real-world decision-making and agency is entirely outside the bounds of what these models can conceive or act towards. Effectively and by design these models are computational behemoths trained to do one singular task only — wring a large textual input though an enormous interconnected web of calculations purely in service of distilling everything down to a single word as output, a hopefully plausible guess at what’s next given what’s been seen.
  - famouswaffles 4y ago
    AGI is Artificial General Intelligence. We have absolutely passed the bar of artificial and generally intelligent. It's not my fault goal post shifting is rampant in this field. And you want to know the crazier thing? Evidently a lot of researchers feel similarly too. General Purpose Technologies ( from the Jobs Paper), General Artificial Intelligence (from the creativity paper). Want to know the original title of the recent Microsoft paper ? "First contact with an AGI system". The skirting around the word that is now happening is insanely funny. Look at the last one. Fuck, they just switched the word order. Nobody wants to call a spade a spade yet but it's obvious people are figuring it out. I can you show you output that clearly demonstrates understanding and reasoning. That's not the problem. The problem is that when I do, the argument Quickly shifts to "it's not true understanding!" What a bizzare argument. This is the fallacy of the philosophical zombie. Somehow there is this extra special distinction between two things and yet you can't actually show it. You can't test for so called huge distinction. A distinction that can't be tested for is not a distinction. The intelligence arguments are also stupid because they miss the point entirely. What matters is that the plane still flies, the car still drives and the boat still sails. For the people who are now salivating at their potential, or dreading the possibility of being made redundant by them, these large language models are already intelligent enough to matter.
- Mawr 4y ago
  The market disagrees with you. How come there are billions of dollars spent on all these knowledge workers around the world every day when they could be replaced by this expert-level AI? I'm not sure where this idea of LLMs being intelligent even comes from. It took me a whopping 9 prompts (genuine questions, no clever prompt engineering) of interacting with ChatGPT to conclude it does not understand anything. It doesn't understand addition, what length is, doesn't remember what it said a second ago, etc. The output of ChatGPT is clearly just a reflection of its inner workings - predicting the next word based on training data. It's clever and undoubtely useful for a certain set of repetitive problems like generating boilerplate but it's not intelligence, not by any reasonable definition.
  - sebzim4500 4y ago
    I don't think any technology has been rolled out with the speed you are suggesting LLMs should have been rolled out. It's like saying 4 months after the first useful car was manufactured. "If these are so good, how come there are still horses? Clearly the market disagrees with you".
  - emodendroket 4y ago
    To give an example of the limitations of these things that's hopefully easy to understand, I got access to Bard this morning and asked it to write a limerick. It gave me what could charitably be called a free verse poem that happened to begin "there once was a man from Nantucket." I'm sure they can improve on it (ChatGPT was better at this kind of thing when I had access to it) but "solved problem" is clearly a long way off.
    - FeepingCreature 4y ago
      Yeah, at least two years.
    - hallway_monitor 4y ago
      Seems Pretty Good to me! Better than I could do anyway. Bard is a joke compared to GPT-4: "Write a limerick about a dog" There once was a dog from the pound Whose bark had a curious sound With a wag and a woof, He'd jump on the roof, Delighting the folks all around.
swyx 4y ago
as a senior academic in the area, might you have a list of the most influential papers in your field in the past year that you would recommend?
- ninjin 4y ago
  I am actually not quite sure what would be best to recommend, as the pandemic has seen me lag behind the zeitgeist somewhat. A recent favourite would be the Toolformer paper [1], that I intend to read in detail later today. If an LLM would be able to use external tools efficiently, it could be rather powerful and perhaps allow us to scale down the parameter sizes, which somewhat fascinates me. [1]: https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 Other research questions, but without concrete papers to reference to that current keeps me up at night: 1.) to which degree can we train substantially smaller LLMs for specific tasks that could be run in-house, 2.) it seems like these new breakthroughs may need a different mode of evaluation compared to what we have used since the 80s in the field and I am not sure what that would look like (maybe along the lines of HELM [2]?), and 3.) can AI academia continue to operate in the way it currently does with small research teams or is a change towards what we see in the physical sciences necessary? [2]: https://arxiv.org/abs/2211.09110 https://arxiv.org/abs/2211.09110
  - swyx 4y ago
    Toolformer seems to already have been productized in ChatGPT Plugins fwiw > 1.) to which degree can we train substantially smaller LLMs for specific tasks that could be run in-house 2.) it seems like these new breakthroughs may need a different mode of evaluation compared to what we have used since the 80s in the field and I am not sure what that would look like (maybe along the lines of HELM [2]?) so you are proposing a set of benchmarks for domain specific tasks? by definition they wont be shared benchmarks...
YeGoblynQueenne 4y ago
This is clearly preliminary work. Not to be disparaging of the authors' background but their background is in political sciences, not in machine learning or NLP which should account for the limitations of the study. But anyway that's just an arxiv preprint so probably more like something exploratory than a research direction the authors are invested in.

by ninjin 4y ago

sebzim4500 4y ago

>If we had a general solution to language intelligence we would have artificial intelligence at the level of at least human intelligence I don't see why this follows. We do loads of stuff other than language, it is entirely possible for an AI to be better than us at language but worse at everything else.

famouswaffles 4y ago

We have artificial intelligence that is general and above average human intelligence for the majority of tasks it can perform. Near expert level for some. NLP is a solved problem. Bespoke models are out the door. Large enough LLMs crush anything else for any NLP task. Honestly, this whole "they are not intelligent" argument is becoming ridiculous. might as well argue that a plane isn’t a real bird or a car isn’t a real horse.

mistrial9 4y ago
no, a short answer to this is .. these models are probabilistic, therefore they will always have errors along with whatever else. Secondly "intelligence" is not one thing; no one has all of it or none of it, including computers.
- nico 4y ago
  > these models are probabilistic, therefore they will always have errors There's nothing perfect. Even computers and computer networks need to have error-correcting code because information gets randomly corrupted. Our whole reality is probabilistic. And us humans are way worse than AI at consistency. We even overwrite our own memories all the time, so we can't even be sure what we remember is actually what happened! (btw, this is currently being used in therapy to re-write traumatic memories and help people overcome PTSD). https://www.npr.org/sections/health-shots/2014/02/04/271527934/our-brains-rewrite-our-memories-putting-present-in-the-past https://www.npr.org/sections/health-shots/2014/02/04/2715279...
ninjin 4y ago
> We have artificial intelligence that is general and above average human intelligence for the vast majority of tasks it can perform. Even when I give it the benefit of the doubt, this sentence makes no sense to me. Do we have a list of tasks a language model can perform? To the best of my knowledge, they can arguably perform any language task. > Large enough LLMs crush anything else for any NLP task. and evidently they beat top humans too. Yes, they are certainly (rightfully) the go-to model for most tasks at this point if your concern is outright performance. Have I indicated otherwise? As for beating “top” humans, I am sure that can be investigated, but it is a fairly nuanced research question. It is inarguable that they are amazingly good though, especially relative to what we had just a few years ago. > Honestly, this whole "they are not intelligent" argument is becoming ridiculously obtuse. > > might as well argue that a plane isn’t a real bird or a car isn’t a real horse. Which is a claim and argument that I never made – hallucinating? How about you calm down a little and get back on the ground? You are talking to someone that has argued in favour of these kinds of models for about a decade. But that does not mean that I am willing to spout nonsense or lose track of what we know and what we do not yet know.
- famouswaffles 4y ago
  You said NLP is unsolved because we don't have human level artificial intelligence. We absolutely do. at least by any evaluations we can carry out. no-one wants to call a spade a spade yet but the sentiment is obvious in recent research. directly being called General purpose technologies from the jobs paper, general artificial intelligence from the creativity paper. That last one is particularly funny, they just switched the two words.
ummonk 4y ago
> might as well argue that a plane isn’t a real bird or a car isn’t a real horse. They aren’t though… They are far superior at specific things birds and horses are known for, but they can’t do everything that birds and horses can, so they aren’t even artificial birds and horses.
- famouswaffles 4y ago
  Of course they aren't. The point is that it's irrelevant. what matters is that the plane still flies, the car still drives and the boat still sails. For the people who are now salivating at their potential, or dreading the possibility of being made redundant by them, these large language models are already intelligent enough to matter. Handwringing bout some non-existent difference between "true understanding" and "fake understanding" which by the way nobody seems to be able to actually distinguish (I mean wow such a supposed huge difference and you can't even show me what that is. a distinction you can't test for is not a distinction ) is so far beyond the point, it's increasingly maddening to read.
  - ummonk 4y ago
    Okay I agree with you on that. The technology will be disruptive regardless of whether we attribute true understanding to it, and as we start adding long term memory and planning to these AIs, we will start seeing significant alignment risk as well. This is true regardless of whether we decide to cope by saying they have "fake understanding" and are "stochastic parrots".
shock-value 4y ago
The debate over what kind of intelligence these models possess is rightly lively and ongoing. It’s clear that at the least, they can decipher very numerous patterns across a wide range of conceptual depths — it’s an architectural advance easily on the the level of the convolutional neural network, if not even more profound. The idea that NLP is “solved” isn’t a crazy notion, though I won’t take a side on that. That said, it’s equally obvious that they are not AGI unless you have a really uninspired and self-limiting definition of AGI. They are purely feedforward aside from the single generated token that becomes part of the input to the next iteration. Multimodality has not been incorporated (aside from possibly a limited form in GPT-4). Real-world decision-making and agency is entirely outside the bounds of what these models can conceive or act towards. Effectively and by design these models are computational behemoths trained to do one singular task only — wring a large textual input though an enormous interconnected web of calculations purely in service of distilling everything down to a single word as output, a hopefully plausible guess at what’s next given what’s been seen.
- famouswaffles 4y ago
  AGI is Artificial General Intelligence. We have absolutely passed the bar of artificial and generally intelligent. It's not my fault goal post shifting is rampant in this field. And you want to know the crazier thing? Evidently a lot of researchers feel similarly too. General Purpose Technologies ( from the Jobs Paper), General Artificial Intelligence (from the creativity paper). Want to know the original title of the recent Microsoft paper ? "First contact with an AGI system". The skirting around the word that is now happening is insanely funny. Look at the last one. Fuck, they just switched the word order. Nobody wants to call a spade a spade yet but it's obvious people are figuring it out. I can you show you output that clearly demonstrates understanding and reasoning. That's not the problem. The problem is that when I do, the argument Quickly shifts to "it's not true understanding!" What a bizzare argument. This is the fallacy of the philosophical zombie. Somehow there is this extra special distinction between two things and yet you can't actually show it. You can't test for so called huge distinction. A distinction that can't be tested for is not a distinction. The intelligence arguments are also stupid because they miss the point entirely. What matters is that the plane still flies, the car still drives and the boat still sails. For the people who are now salivating at their potential, or dreading the possibility of being made redundant by them, these large language models are already intelligent enough to matter.
Mawr 4y ago
The market disagrees with you. How come there are billions of dollars spent on all these knowledge workers around the world every day when they could be replaced by this expert-level AI? I'm not sure where this idea of LLMs being intelligent even comes from. It took me a whopping 9 prompts (genuine questions, no clever prompt engineering) of interacting with ChatGPT to conclude it does not understand anything. It doesn't understand addition, what length is, doesn't remember what it said a second ago, etc. The output of ChatGPT is clearly just a reflection of its inner workings - predicting the next word based on training data. It's clever and undoubtely useful for a certain set of repetitive problems like generating boilerplate but it's not intelligence, not by any reasonable definition.
- sebzim4500 4y ago
  I don't think any technology has been rolled out with the speed you are suggesting LLMs should have been rolled out. It's like saying 4 months after the first useful car was manufactured. "If these are so good, how come there are still horses? Clearly the market disagrees with you".
- emodendroket 4y ago
  To give an example of the limitations of these things that's hopefully easy to understand, I got access to Bard this morning and asked it to write a limerick. It gave me what could charitably be called a free verse poem that happened to begin "there once was a man from Nantucket." I'm sure they can improve on it (ChatGPT was better at this kind of thing when I had access to it) but "solved problem" is clearly a long way off.
  - FeepingCreature 4y ago
    Yeah, at least two years.
  - hallway_monitor 4y ago
    Seems Pretty Good to me! Better than I could do anyway. Bard is a joke compared to GPT-4: "Write a limerick about a dog" There once was a dog from the pound Whose bark had a curious sound With a wag and a woof, He'd jump on the roof, Delighting the folks all around.

swyx 4y ago

as a senior academic in the area, might you have a list of the most influential papers in your field in the past year that you would recommend?

ninjin 4y ago
I am actually not quite sure what would be best to recommend, as the pandemic has seen me lag behind the zeitgeist somewhat. A recent favourite would be the Toolformer paper [1], that I intend to read in detail later today. If an LLM would be able to use external tools efficiently, it could be rather powerful and perhaps allow us to scale down the parameter sizes, which somewhat fascinates me. [1]: https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 Other research questions, but without concrete papers to reference to that current keeps me up at night: 1.) to which degree can we train substantially smaller LLMs for specific tasks that could be run in-house, 2.) it seems like these new breakthroughs may need a different mode of evaluation compared to what we have used since the 80s in the field and I am not sure what that would look like (maybe along the lines of HELM [2]?), and 3.) can AI academia continue to operate in the way it currently does with small research teams or is a change towards what we see in the physical sciences necessary? [2]: https://arxiv.org/abs/2211.09110 https://arxiv.org/abs/2211.09110
- swyx 4y ago
  Toolformer seems to already have been productized in ChatGPT Plugins fwiw > 1.) to which degree can we train substantially smaller LLMs for specific tasks that could be run in-house 2.) it seems like these new breakthroughs may need a different mode of evaluation compared to what we have used since the 80s in the field and I am not sure what that would look like (maybe along the lines of HELM [2]?) so you are proposing a set of benchmarks for domain specific tasks? by definition they wont be shared benchmarks...

YeGoblynQueenne 4y ago

This is clearly preliminary work. Not to be disparaging of the authors' background but their background is in political sciences, not in machine learning or NLP which should account for the limitations of the study. But anyway that's just an arxiv preprint so probably more like something exploratory than a research direction the authors are invested in.