8 ms·

I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed line

I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things. The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we found it takes much less than "infinite", a handful will already get you a long way. A stack of this basic building block, as you describe it, is really all it takes - we know that mathematically already. The interesting question is: how complex are the functions we need to model? So if we create a neural network of a certain size, is that size large enough to model the problem space? > However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach. I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works?

kranke155 3y ago
Hallucinations are an indication that something is missing, surely? Does chatGPT need a way to verify reality for itself to become truly intelligent ?
- superzamp 3y ago
  Just the other day, my friend asked me if I knew what this bug she took a picture of was. I replied it looked like a house centipede, and it was indeed one. Two things: 1) My confidence was around 80%, without any sense of absolute certainty. 2) I had no idea why I knew that, and where did I originally learned about what a house centipede looks like.
  - ImHereToVote 3y ago
    Yeah but your brain is wet. Try to make a GPU wet, and see how general the problem solving becomes. Check mate.
  - TheSpiceIsLife 3y ago
    If you have genuinely no idea why you know that and where you learned it, you should probably seek medical advice concerning maybe early onset dementia or some such. There's a good argument to be made you learned about potentially dangerous insects as a child, from books aimed at children. A reasonable person would make this argument. It's also likely that you retained that information because your would-be peers who aren't around, aren't around because their ascendants inserted their genetic material in to the humanity's descent's to a lesser extent than your extant peers. You might not be able to immediately recall where and when you learned something, and that shouldn't be equated with genuinely having no idea.
  - lipoid_ecole 3y ago
    If your friend asked you for proof would you make up an article that doesn't exist and create a link that leads to no where?
- ml-anon 3y ago
  Calling them "hallucinations" is a shoddy piece of terminology by the community which anthropomorphises them.
  - rullelito 3y ago
    We're building human-like intelligence, so this seems quite apt?
    - deleted 3y ago
      [deleted]
    - TheSpiceIsLife 3y ago
      Are we? I'd settle for a poor fascimillie, and argue strongly against human-like. Human-like would be, let's say, a Cylon skin-job. ChatGPT is just a fraking toaster, at best.
  - 23B1 3y ago
    What would you recommend calling them? Genuinely curious.
    - pmoriarty 3y ago
      Mistakes.
    - a13o 3y ago
      When humans do it we call them bullshitters. But I agree with GP's eager-anthropomorphization complaint. When algorithms produce verifiably wrong output we call them errors.
    - jebarker 3y ago
      According to Geoff Hinton the correct term from human psychology is "confabulations". Hallucinations are a mistake in perception.
  - deleted 3y ago
    [deleted]
- vidarh 3y ago
  Humans "hallucinate" in the AI sense (it's an awful word that obscures how often we do it) all the time too. You'll find people confidently make claims not supported by data or indeed by any of their own data all the time, where when you poke around in their justifications you'll find they have none. The key difference appears a combination of two factors: We appear to be more likely to have learnt which subjects we're not very knowledgeable about through extensive feedback, be it through school or conversations where we're told we're wrong, and which also would appear to teach us to be more cautious in general. We also appear to have a (somewhat; far from perfect) better ability to separate memory from thoughts about our knowledge. We certainly can go off on wild tangents and make stuff up about any subject, but we get reinforced from very young that there's a time and a place for making stuff up vs. drawing on memory. Both goes back simply to extensive (many years of) reinforcement telling us it has negative effects to make stuff up and/or believe things that aren't true, and yet we still do both of those, just not usually as blatantly as current LLMs without being aware. So I'd expect one missing component is to add a training step that subjects the model to batteries of tests of the limits of their knowledge and incorporating the results of that in the training.
  - beanbean01 3y ago
    Yes, but there's a critical difference. When humans hallucinate, it's normally because they misremember something: did you know, for instance, that our brains have to piece every memory together from scratch every time we recall something? It would be a miracle if humans did NOT hallucinate. In other words, the human has a concept of truth or facts and simply had a memory lapse. This thing has no concept of truth at all.
  - maister 3y ago
    > Humans "hallucinate" in the AI sense (it's an awful word that obscures how often we do it) all the time too. Agreed. I'd like to add another point to the discussion. It seems to me, as if LLMs are held to a higher standard regarding telling the truth than humans are. In my opinion the reason for this is that computers have been traditionally used to solve deterministic tasks and that people are not used to them making wrong claims.
- jebarker 3y ago
  Hallucinations are a feature, not a bug. GPT pre-training teaches the model to always produce an answer, even when it has little or not relevant training experience, and on average it does well at that. Part of the point of RLHF in ChatGPT was to teach the model not to hallucinate when it doesn't have good supporting experience encoded in it's weights. This helps but is not perfect. However, it seems like there might be a path to much less hallucinations with more RL training data. As others pointed out, humans hallucinate all the time we just have better training for what level of hallucination is appropriate given supporting evidence and context.
goatlover 3y ago
> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? Animals have bodies and have to survive in an environment. They're not just sitting there waiting on a prompt to generate text. They do things in the real world. Language is later invention by one particular social animal which serves our needs to communicate, which is different than waiting on prompts.
- streakfix 3y ago
  Reinforcement learning emulates survival of the fittest, in that smarted algorithms/parameters survive.
  - ml-anon 3y ago
    [flagged]
    - spuz 3y ago
      Why is it wrong?
  - mikhmha 3y ago
    What that’s not how it works. You can be dumb and stil survive if you are better adapted to your environment.
- vidarh 3y ago
  That is just taking data in and generating data out, though. Language is just data, not something fundamentally different. There are certainly some missing elements, such as ongoing training and memory, but it's not at all clear that there's any major conceptual differences. There could be, we just don't really know.
  - goatlover 3y ago
    The environment is noisy. Organisms generate data by making sense of the noise in ways that are beneficial to their survival.
depr 3y ago
>I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? People don't need the gigantic amount of input data that ChatGPT needs to learn. However I'm not sure what exactly "this" is you and GP are referring to, and it may be possible to improve existing ideas so that it works with less input data.
- streakfix 3y ago
``` USER: If a = 33223 and b = 22335, what is a + b? ASSISTANT: a + b = 55558. ``` The training data isn't the input. It's a part of the algorithm.
- amitport 3y ago
  "People don't need the gigantic amount of input data that ChatGPT needs to learn" Sure they do. Humans rely on tons of audio and video before they can even read (or walk).
  - saurik 3y ago
    But I have learned to program and do complex math having read and analyzed ridiculously less source material on those subjects (and in fact in the case of programming having seen very little code as I mostly only had reference manuals at the time).
    - vidarh 3y ago
      We are trained in a way that involves far more back and forth, giving corrections to specific failure modes in our thinking, though. It'll be fascinating to see to what extent interleaving "independent study" (training on large chunks of data) with interaction fed back in as training data could reduce the amount of training needed.
    - xtreme 3y ago
      Before you learned how to code from a book, you had to learn how to read and write English. You also had to learn how to follow instructions, how to imbibe and compose information etc. How many books and hours of instruction did that take?
misnome 3y ago
> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? What evidence do we have that this is how intelligence works?
- dr_dshiv 3y ago
  A transformative aspect of human verbal intelligence involves dealing with concepts and their combinations intersection. ChatGPT does this very well. I think we can agree that ChatGPT provides intelligent completions for an astonishing range of human concepts? It seems appropriate to describe what ChatGPT understands and what it doesn’t understand through evals or assessments (in the same way that we can use assessments to determine what a student understands or doesn’t). So if we have to call it “computational understanding”, fine —- but clearly ChatGPT understands an incredible range of concepts and their combinations.
  - andrepd 3y ago
    > ChatGPT does this very well Lol, this is precisely what chatgpt doesn't do well at all! It fails to understand simple concepts, logical inference, simple math, etc.
    - dr_dshiv 3y ago
      It’s terrible and math and logic, but ChatGPT is amazing at concepts—that’s why it is so powerful. It doesn’t work programmatically—that’s why it fails at logic. But it can reason inductively very very well. Do you have an example besides logic/math where it doesn’t understand simple concepts?
      - andrepd 3y ago
        What do you mean exactly "besides math/logic"? Because logic underpins everything we think. For example it cannot identify musical chords because despite (I presume) ample training material including explanations of how exactly this works, it cannot reasonable represent this as an abstract rigorous rule, as humans do. So I ask what is C E G and it tells me C major correctly, as it presumably appears many times throughout the training set, yet I ask F Ab Db and it does not tell me Db major, because it did not understand the rules at all. In a sense though, this is just a logic problem.
wesammikhail 3y ago
Andrej Karpathy, the director of AI at tesla, published an AMAZING video about how the current models are built which I think everyone in the tech space should watch. It is intuitive, easy to follow and quite frankly the best video I have seen on the topic. Here is the link: https://www.youtube.com/watch?v=kCc8FmEb1nY&list=FL2tbfd7UpJdKc76jlXrKLEg&index=1&t=2s https://www.youtube.com/watch?v=kCc8FmEb1nY&list=FL2tbfd7UpJ... If you conclude, after watching this, that this is all intelligence is - namely cascading optimization and data structure problems chained in a row, then you and a fair number of people here wont ever find common ground. That is not to say that you're wrong, it just seems as if there is a missing component here that we haven't yet discovered.
- spuz 3y ago
  > If you conclude, after watching this, that this is all intelligence is - namely cascading optimization and data structure problems chained in a row, then you and a fair number of people here wont ever find common ground. They won't find common ground with who?
- Lerc 3y ago
  I'm ok with believing that all it takes to make intelligence (probably not the only way) is a sufficiently large neural net with the right architecture and weights. I think it is easy to distracted by the specific mechanisms by which these models work, but most of the technological detail is because we want something to happen on systems at the scale of what we can actually build. We simply can't build a human brain scale neural net yet. We build what we can and besides maybe with all this research we will figure something significant about what intelligence actually is. The notion "This can't be all thought is" is as old as the idea of AI. I think it informed Turing when he proposed the Imitation Game. The insight is that people would be resistant to the idea of a bunch of simple things stuck together becoming thinking until they were faced with something that behaves sufficiently indistinguishable from themselves that to doubt that they were thinking would be akin to doubting everyone you meet. In the end some people won't even accept an AI that does everything a human does as actually thinking, but then again some people are actually solipsistic.
  - 3y ago
somewhereoutth 3y ago
You don't need non-linearities, an infinite series of sine functions is enough to model any function. For extraordinary claims ('intelligence'), the burden of proof is on those making the claim, not on others to prove the negative.
- meindnoch 3y ago
  >You don't need non-linearities, an infinite series of sine functions is enough The sine function is nonlinear.
  - somewhereoutth 3y ago
    Ah so it is! I guess my point was that even this simple function is enough, you don't need any 'magic' beyond that. In particular, this continuous function can be used (infinitely) to represent discontinuous functions - the step function being the usual example. That is the more interesting and relevant mathematical fact I think.
    - meindnoch 3y ago
      Well, yes. The 'magic' is the nonlinearity. It's because the composition of linear (actually also affine) functions is still just linear (affine). You don't get any additional power by combining many of them - which is also the reason why linear functions are so well understood and easy to work with. You give sprinkle in a tiny nonlinearity (e.g. x^2 instead of x) and suddenly you can get infinite complexity by weighted composition - which is also the reason why we're so helpless with nonlinear functions and reach for linear approximations immediately (cf. gradient descent).
      - imtringued 3y ago
        >It's because the composition of linear (actually also affine) functions is still just linear (affine). Except you can "compose" affine functions using Horner's schema to get any polynomial... No need to sprinkle tiny non linearities. It's a buffet. Grab as much as you need.
danielrpa 3y ago
I like what you are saying but not the last line. Angels exist - what evidence do we have that they don't? ChatGPT points to us possibly going in the right direction. It is so good that people need to write long articles about why it is not as good as a human. Contrast to older efforts, which were pretty clearly not so great. I find this pretty compelling, and GOTCHA examples that show that ChatGPT isn't as good as humans in everything miss the point. Birds fly and drones don't look like birds - but they fly. If the goal is flying, it's okay that we achieve it through something that doesn't exactly mimic what inspired it. Do we need a "full human" for all we do? Our billions of machines, many performing work previously done by humans, show that we don't. If we can largely replicate intelligence, it's not super important whether "this is how human intelligence works".
- burnished 3y ago
  You inverted the argument accidentally with your angel analogy, I think something like "we have labradors, what evidence do we have that they are not angels?" matches much more closely to the thrust of their point while also highlighting what you find lacking about the argument. Edit: the important bit is that the subject already has some qualities in common with the proposed classification which is a significantly different proposition than a purely hypothetical orbital teapot
dragonwriter 3y ago
> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? The fact that ChatGPT doesn’t behave like an intelligence. (Though it converses like one with radical deficiencies in certain areas, which highlights the narrowness of the Turing Test, which itself is a big step.) OTOH, it gets less bad at this when you wrap it in a loop that provides recall and other capacities with carefully constructed prompting as to how (“thought” process-wise, not just interaction mechanics) to integrate those capacities, so there’s maybe a decent argument that it models an important component of general intelligence.
grigri9 3y ago
Another way of putting this is: 1) Given an infinite series of nonlinearities, one can model any mathematical function to an arbitrary degree of precision. This has been proven and is accepted by any working mathematician. 2) The human brain / intelligence can be simulated by a sufficiently complex mathematical function. It is somewhat accepted that simulating the human brain is sufficient for intelligence. Disagreements usually boil down to: It won't have a soul, you can't simulate biological / quantum processes on digital computers, or something abut Qualia I don't really understand, 3) How big / complex is the function that we need to get to AGI / simulate human or above intelligence? TBD, it seems like experts disagree but many working in the field have been surprised by the capabilities that GPT has and we are likely closer to AGI than anyone thought 2 years ago.

kranke155 3y ago

Hallucinations are an indication that something is missing, surely? Does chatGPT need a way to verify reality for itself to become truly intelligent ?

superzamp 3y ago
Just the other day, my friend asked me if I knew what this bug she took a picture of was. I replied it looked like a house centipede, and it was indeed one. Two things: 1) My confidence was around 80%, without any sense of absolute certainty. 2) I had no idea why I knew that, and where did I originally learned about what a house centipede looks like.
- ImHereToVote 3y ago
  Yeah but your brain is wet. Try to make a GPU wet, and see how general the problem solving becomes. Check mate.
- TheSpiceIsLife 3y ago
  If you have genuinely no idea why you know that and where you learned it, you should probably seek medical advice concerning maybe early onset dementia or some such. There's a good argument to be made you learned about potentially dangerous insects as a child, from books aimed at children. A reasonable person would make this argument. It's also likely that you retained that information because your would-be peers who aren't around, aren't around because their ascendants inserted their genetic material in to the humanity's descent's to a lesser extent than your extant peers. You might not be able to immediately recall where and when you learned something, and that shouldn't be equated with genuinely having no idea.
- lipoid_ecole 3y ago
  If your friend asked you for proof would you make up an article that doesn't exist and create a link that leads to no where?
ml-anon 3y ago
Calling them "hallucinations" is a shoddy piece of terminology by the community which anthropomorphises them.
- rullelito 3y ago
  We're building human-like intelligence, so this seems quite apt?
  - deleted 3y ago
    [deleted]
  - TheSpiceIsLife 3y ago
    Are we? I'd settle for a poor fascimillie, and argue strongly against human-like. Human-like would be, let's say, a Cylon skin-job. ChatGPT is just a fraking toaster, at best.
- 23B1 3y ago
  What would you recommend calling them? Genuinely curious.
  - pmoriarty 3y ago
    Mistakes.
  - a13o 3y ago
    When humans do it we call them bullshitters. But I agree with GP's eager-anthropomorphization complaint. When algorithms produce verifiably wrong output we call them errors.
  - jebarker 3y ago
    According to Geoff Hinton the correct term from human psychology is "confabulations". Hallucinations are a mistake in perception.
- deleted 3y ago
  [deleted]
vidarh 3y ago
Humans "hallucinate" in the AI sense (it's an awful word that obscures how often we do it) all the time too. You'll find people confidently make claims not supported by data or indeed by any of their own data all the time, where when you poke around in their justifications you'll find they have none. The key difference appears a combination of two factors: We appear to be more likely to have learnt which subjects we're not very knowledgeable about through extensive feedback, be it through school or conversations where we're told we're wrong, and which also would appear to teach us to be more cautious in general. We also appear to have a (somewhat; far from perfect) better ability to separate memory from thoughts about our knowledge. We certainly can go off on wild tangents and make stuff up about any subject, but we get reinforced from very young that there's a time and a place for making stuff up vs. drawing on memory. Both goes back simply to extensive (many years of) reinforcement telling us it has negative effects to make stuff up and/or believe things that aren't true, and yet we still do both of those, just not usually as blatantly as current LLMs without being aware. So I'd expect one missing component is to add a training step that subjects the model to batteries of tests of the limits of their knowledge and incorporating the results of that in the training.
- beanbean01 3y ago
  Yes, but there's a critical difference. When humans hallucinate, it's normally because they misremember something: did you know, for instance, that our brains have to piece every memory together from scratch every time we recall something? It would be a miracle if humans did NOT hallucinate. In other words, the human has a concept of truth or facts and simply had a memory lapse. This thing has no concept of truth at all.
- maister 3y ago
  > Humans "hallucinate" in the AI sense (it's an awful word that obscures how often we do it) all the time too. Agreed. I'd like to add another point to the discussion. It seems to me, as if LLMs are held to a higher standard regarding telling the truth than humans are. In my opinion the reason for this is that computers have been traditionally used to solve deterministic tasks and that people are not used to them making wrong claims.
jebarker 3y ago
Hallucinations are a feature, not a bug. GPT pre-training teaches the model to always produce an answer, even when it has little or not relevant training experience, and on average it does well at that. Part of the point of RLHF in ChatGPT was to teach the model not to hallucinate when it doesn't have good supporting experience encoded in it's weights. This helps but is not perfect. However, it seems like there might be a path to much less hallucinations with more RL training data. As others pointed out, humans hallucinate all the time we just have better training for what level of hallucination is appropriate given supporting evidence and context.

goatlover 3y ago

> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? Animals have bodies and have to survive in an environment. They're not just sitting there waiting on a prompt to generate text. They do things in the real world. Language is later invention by one particular social animal which serves our needs to communicate, which is different than waiting on prompts.

streakfix 3y ago
Reinforcement learning emulates survival of the fittest, in that smarted algorithms/parameters survive.
- ml-anon 3y ago
  [flagged]
  - spuz 3y ago
    Why is it wrong?
- mikhmha 3y ago
  What that’s not how it works. You can be dumb and stil survive if you are better adapted to your environment.
vidarh 3y ago
That is just taking data in and generating data out, though. Language is just data, not something fundamentally different. There are certainly some missing elements, such as ongoing training and memory, but it's not at all clear that there's any major conceptual differences. There could be, we just don't really know.
- goatlover 3y ago
  The environment is noisy. Organisms generate data by making sense of the noise in ways that are beneficial to their survival.

depr 3y ago

>I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? People don't need the gigantic amount of input data that ChatGPT needs to learn. However I'm not sure what exactly "this" is you and GP are referring to, and it may be possible to improve existing ideas so that it works with less input data.

streakfix 3y ago
``` USER: If a = 33223 and b = 22335, what is a + b? ASSISTANT: a + b = 55558. ``` The training data isn't the input. It's a part of the algorithm.
amitport 3y ago
"People don't need the gigantic amount of input data that ChatGPT needs to learn" Sure they do. Humans rely on tons of audio and video before they can even read (or walk).
- saurik 3y ago
  But I have learned to program and do complex math having read and analyzed ridiculously less source material on those subjects (and in fact in the case of programming having seen very little code as I mostly only had reference manuals at the time).
  - vidarh 3y ago
    We are trained in a way that involves far more back and forth, giving corrections to specific failure modes in our thinking, though. It'll be fascinating to see to what extent interleaving "independent study" (training on large chunks of data) with interaction fed back in as training data could reduce the amount of training needed.
  - xtreme 3y ago
    Before you learned how to code from a book, you had to learn how to read and write English. You also had to learn how to follow instructions, how to imbibe and compose information etc. How many books and hours of instruction did that take?

misnome 3y ago

> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? What evidence do we have that this is how intelligence works?

dr_dshiv 3y ago
A transformative aspect of human verbal intelligence involves dealing with concepts and their combinations intersection. ChatGPT does this very well. I think we can agree that ChatGPT provides intelligent completions for an astonishing range of human concepts? It seems appropriate to describe what ChatGPT understands and what it doesn’t understand through evals or assessments (in the same way that we can use assessments to determine what a student understands or doesn’t). So if we have to call it “computational understanding”, fine —- but clearly ChatGPT understands an incredible range of concepts and their combinations.
- andrepd 3y ago
  > ChatGPT does this very well Lol, this is precisely what chatgpt doesn't do well at all! It fails to understand simple concepts, logical inference, simple math, etc.
  - dr_dshiv 3y ago
    It’s terrible and math and logic, but ChatGPT is amazing at concepts—that’s why it is so powerful. It doesn’t work programmatically—that’s why it fails at logic. But it can reason inductively very very well. Do you have an example besides logic/math where it doesn’t understand simple concepts?
    - andrepd 3y ago
      What do you mean exactly "besides math/logic"? Because logic underpins everything we think. For example it cannot identify musical chords because despite (I presume) ample training material including explanations of how exactly this works, it cannot reasonable represent this as an abstract rigorous rule, as humans do. So I ask what is C E G and it tells me C major correctly, as it presumably appears many times throughout the training set, yet I ask F Ab Db and it does not tell me Db major, because it did not understand the rules at all. In a sense though, this is just a logic problem.

wesammikhail 3y ago

Andrej Karpathy, the director of AI at tesla, published an AMAZING video about how the current models are built which I think everyone in the tech space should watch. It is intuitive, easy to follow and quite frankly the best video I have seen on the topic. Here is the link: https://www.youtube.com/watch?v=kCc8FmEb1nY&list=FL2tbfd7UpJdKc76jlXrKLEg&index=1&t=2s https://www.youtube.com/watch?v=kCc8FmEb1nY&list=FL2tbfd7UpJ... If you conclude, after watching this, that this is all intelligence is - namely cascading optimization and data structure problems chained in a row, then you and a fair number of people here wont ever find common ground. That is not to say that you're wrong, it just seems as if there is a missing component here that we haven't yet discovered.

spuz 3y ago
> If you conclude, after watching this, that this is all intelligence is - namely cascading optimization and data structure problems chained in a row, then you and a fair number of people here wont ever find common ground. They won't find common ground with who?
Lerc 3y ago
I'm ok with believing that all it takes to make intelligence (probably not the only way) is a sufficiently large neural net with the right architecture and weights. I think it is easy to distracted by the specific mechanisms by which these models work, but most of the technological detail is because we want something to happen on systems at the scale of what we can actually build. We simply can't build a human brain scale neural net yet. We build what we can and besides maybe with all this research we will figure something significant about what intelligence actually is. The notion "This can't be all thought is" is as old as the idea of AI. I think it informed Turing when he proposed the Imitation Game. The insight is that people would be resistant to the idea of a bunch of simple things stuck together becoming thinking until they were faced with something that behaves sufficiently indistinguishable from themselves that to doubt that they were thinking would be akin to doubting everyone you meet. In the end some people won't even accept an AI that does everything a human does as actually thinking, but then again some people are actually solipsistic.
- 3y ago

somewhereoutth 3y ago

You don't need non-linearities, an infinite series of sine functions is enough to model any function. For extraordinary claims ('intelligence'), the burden of proof is on those making the claim, not on others to prove the negative.

meindnoch 3y ago
>You don't need non-linearities, an infinite series of sine functions is enough The sine function is nonlinear.
- somewhereoutth 3y ago
  Ah so it is! I guess my point was that even this simple function is enough, you don't need any 'magic' beyond that. In particular, this continuous function can be used (infinitely) to represent discontinuous functions - the step function being the usual example. That is the more interesting and relevant mathematical fact I think.
  - meindnoch 3y ago
    Well, yes. The 'magic' is the nonlinearity. It's because the composition of linear (actually also affine) functions is still just linear (affine). You don't get any additional power by combining many of them - which is also the reason why linear functions are so well understood and easy to work with. You give sprinkle in a tiny nonlinearity (e.g. x^2 instead of x) and suddenly you can get infinite complexity by weighted composition - which is also the reason why we're so helpless with nonlinear functions and reach for linear approximations immediately (cf. gradient descent).
    - imtringued 3y ago
      >It's because the composition of linear (actually also affine) functions is still just linear (affine). Except you can "compose" affine functions using Horner's schema to get any polynomial... No need to sprinkle tiny non linearities. It's a buffet. Grab as much as you need.

danielrpa 3y ago

I like what you are saying but not the last line. Angels exist - what evidence do we have that they don't? ChatGPT points to us possibly going in the right direction. It is so good that people need to write long articles about why it is not as good as a human. Contrast to older efforts, which were pretty clearly not so great. I find this pretty compelling, and GOTCHA examples that show that ChatGPT isn't as good as humans in everything miss the point. Birds fly and drones don't look like birds - but they fly. If the goal is flying, it's okay that we achieve it through something that doesn't exactly mimic what inspired it. Do we need a "full human" for all we do? Our billions of machines, many performing work previously done by humans, show that we don't. If we can largely replicate intelligence, it's not super important whether "this is how human intelligence works".

burnished 3y ago
You inverted the argument accidentally with your angel analogy, I think something like "we have labradors, what evidence do we have that they are not angels?" matches much more closely to the thrust of their point while also highlighting what you find lacking about the argument. Edit: the important bit is that the subject already has some qualities in common with the proposed classification which is a significantly different proposition than a purely hypothetical orbital teapot

dragonwriter 3y ago

> I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? The fact that ChatGPT doesn’t behave like an intelligence. (Though it converses like one with radical deficiencies in certain areas, which highlights the narrowness of the Turing Test, which itself is a big step.) OTOH, it gets less bad at this when you wrap it in a loop that provides recall and other capacities with carefully constructed prompting as to how (“thought” process-wise, not just interaction mechanics) to integrate those capacities, so there’s maybe a decent argument that it models an important component of general intelligence.

grigri9 3y ago

Another way of putting this is: 1) Given an infinite series of nonlinearities, one can model any mathematical function to an arbitrary degree of precision. This has been proven and is accepted by any working mathematician. 2) The human brain / intelligence can be simulated by a sufficiently complex mathematical function. It is somewhat accepted that simulating the human brain is sufficient for intelligence. Disagreements usually boil down to: It won't have a soul, you can't simulate biological / quantum processes on digital computers, or something abut Qualia I don't really understand, 3) How big / complex is the function that we need to get to AGI / simulate human or above intelligence? TBD, it seems like experts disagree but many working in the field have been surprised by the capabilities that GPT has and we are likely closer to AGI than anyone thought 2 years ago.