5 ms·

You can still regurgitate a chain of thought response… You people are so close to getting it. So close to understanding that you're the ones doing the regurgi

You can still regurgitate a chain of thought response… You people are so close to getting it. So close to understanding that you're the ones doing the regurgitating.

dartos 2y ago
That sword cuts both ways.
- CamperBob2 2y ago
  I mean, you talk about a predictable, deterministic next-token generator...
fmbb 2y ago
Why do you believe that is how humans reason?
- mrbungie 2y ago
  Neuroscience and evidence be damned, [the brain is a computer] is being transformed into [the brain is an LLM]. Happens with every new technology. Edit: What is happening with people these days? They seem to be reducing people and their minds to machines more easily than ever.
  - CamperBob2 2y ago
    You want to talk evidence, let's talk evidence. What does the brain do that these models don't (or, more generally, can't)? Be specific. And no, it doesn't "happen with every new technology." Nothing even remotely like this has been seen before the present decade.
    - mjmas 2y ago
      Have experiences and feelings. Have personal knowledge of something. Be able to express language with a training/knowledge/etc corpus of far smaller size.
      - error_logic 2y ago
        A lot of that is provided by multi-modality including feedback from the body and interacting with objects and people with more than just the words. That expands the context of a human's experiences dramatically compared to just reading books. Plus even when humans are reading books a mental image of what's going on in the story is common. Not everyone has that, but it shows how much a basic LLM lacks and multi-modal would add. Now the real question to my mind is whether we can train models with actual empathy to learn from the experiences of other people without having to go through the experience directly. Doing so would put them above many individuals' understanding already. . .
      - ben_w 2y ago
        While I agree with: > Be able to express language with a training/knowledge/etc corpus of far smaller size. Nobody knows what it would mean to test for: > Have experiences and feelings. And I am unclear if: > Have personal knowledge of something. Would or would not be met by putting them in charge of a robot of some kind to collect experiences? I'd certainly agree that the default is to make them "book smart" rather than "street smart", but at most that's an "is not" rather than "cannot".
    - mrbungie 2y ago
      You can't win this argument through logic less mere rhetoric. You need scientific evidence. Hint: There isn't. Edit: Ok, let me be less harsh and play with your argument. Can an LLM have an acid/psilocybin/ketamine trip, distort its view of self and reality, then rewire some of its internal connections and come a little different based on the experience? I guess not, there are more examples and they all show that as far as we know LLMs are not minds/brains and viceversa even if they seem similar in a chat interface. (If you don't empathise with that example remove the drugs and change them for a near-death experience). I strongly argue humans are not (just) LLMs, but I think we're far from getting evidence*. I think we will get to AGI before we know how the brain works and there is no dicotomy in that, if anything the tech is showing us that we can get at least some intelligence in other substrates. *PS: fwiw ausence of evidence does not support any sides. I shouldn't remark that, but here we are.
      - ben_w 2y ago
        > You need scientific evidence This, while true, is putting the cart before the horse. One needs to define what the question is before it is possible to seek evidence. I see many things different between Transformer models and brains, but I do not know which, if any, matters. > Can an LLM have an acid/psilocybin/ketamine trip, distort its view of self and reality, then rewire some of its internal connections and come a little different based on the experience? Specifically those chemicals? No, not even during training when the weights aren't frozen, as nobody bothered to simulate the relevant receptors*. Can LLMs experience anything, in the way we mean it? Nobody even knows. We don't know what the question means well enough to more than merely guess what to look for. Do the inputs to an LLM, a broader idea of "experience" without needing to solve the question of which definition of consciousness everyone should be using, rewire some of its internal connections? All the time. * caveat: it doesn't, at first glance, seem totally impossible that the structure of these models is sufficiently complicated for them to create the simulation of those things in the weights themselves in order to better model the behaviour of humans generating text from experiencing those things. But I also don't have any positive reason to expect this any more than I would expect an actor portraying a near-death-experience to have any idea what that's like on the inside.
    - mkleczek 2y ago
      The brain can invent formal language that enables unambiguously specifying an algorithm that when provided with a huge amount of input data can simulate the output of the brain itself. Can these models do that?
      - ben_w 2y ago
        > The brain can invent formal language that enables unambiguously specifying an algorithm that when provided with a huge amount of input data can simulate the output of the brain itself. Can *a* brain do this? Every effort I've seen has (1) involved *many people* working on different parts of the problem, and (2) so far we've only got partial solutions. Even if you allow collaboration, then when (2) goes away, your question is trivially answered "yes" because we just feed into the model the same experiences that were fed into the human(s) who invented the model, and the simulation then simulates them inventing the same model.
        Jensson 2y ago
        > Even if you allow collaboration, then when (2) goes away, your question is trivially answered "yes" because we just feed into the model the same experiences that were fed into the human(s) who invented the model, and the simulation then simulates them inventing the same model. These models doesn't work that way. Its probably possible to make such a model, but currently we don't know how to make them.
        ben_w 2y ago
        I literally said so, yes.
        mkleczek 2y ago
        Do you have empirical proof any LLM can generate another program that can be trained to simulate it? Otherwise it is science fiction.
        ben_w 2y ago
    - sgt101 2y ago
      I'm no neuroscientist, but I hung out with a few, so here are some bits I picked up from them. In biological brians: - neurons have signal surpression (-ive activation) as well as propagation (+activation) - neurons appear to process and propagation due to unknown internal mechanisms. - there are complex sub structures within biological neural nets where the architecture is radically different from in other sections of the network, strongly in contrast to the homogenous structure of ANN's - many different types of neurons with different properties and behaviors in terms of network formation and network activity are present in BNN's in contrast to ANN's - BNN's learn during processing. ANN's are static after training.
      - qrsjutsu 2y ago
        [dead]
      - CamperBob2 2y ago
        Those are mostly structure-versus-function arguments, centered on what a brain is as opposed to what it does. Only the static nature of the current ML models seems like a valid point... and it's changing too. I'd wager that future models (which admittedly may not look much like today's LLMs) will blur the lines between mutable context and immutable weights. When that happens, all bets are truly off.
        daveguy 2y ago
        > future models (which admittedly may not look much like today's LLMs) will blur the lines between mutable context and immutable weights. "may not look much like today's LLMs" is really sweeping the whole point under the rug. The difference between what we are doing now with static LLM models and dynamic models will require algorithmic changes that have not yet been invented. That's in addition to the fact that the processing of a single neuron is completely parallel with respect to inputs. GPUs make them more parallel, but the analog devices we are trying to mimic are vastly more complicated than what we are using as a substitute.
        CamperBob2 2y ago
  - ninetyninenine 2y ago
    The brain is a neural net. And so is the LLM.
    - Jensson 2y ago
      > The brain is a neural net No it isn't, neural networks aren't a network of neurons, they are just named after neurons but they are nothing alike. Neurons grow new connections etc and do a whole slew of different things that neural networks doesn't do. The ability to grow new connections seems like an integral part to intelligence that neural networks can't replicate, at least not without changing them to something very different than they are today.
      - ninetyninenine 2y ago
        The brain is a neural net. It’s made up of neurons that are networked. You’re just pointing out differences between a LLM and a brain. The fact that they are both neural nets is a similarity. Forming new connections Is not integral. Connections don’t form in seconds so second to second existence is a static neural network. Thus your second to second existence is comparable to an LLM in the sense that both neural networks don’t form new connections. We can though. We can make neural networks form new connections.
        Jensson 2y ago
        > The brain is a neural net. It’s made up of neurons that are networked The brain neurons are not the same kind of object as neural net neurons, hence its not a neural net. It is like saying a dragonfly is a helicopter since the helicopter were designed to look a bit like a firefly, no a dragonfly isn't a helicopter they fly in totally different ways the dragonfly has wings and thus don't even move similarly. > Connections don’t form in seconds so second to second existence is a static neural network But a human isn't intelligent over a second, and synapses do form over longer periods which is what it takes for humans to solve harder problems. > We can though. We can make neural networks form new connections. I said the neural network couldn't form those, not that we couldn't alter the neural network. That is a critical difference, our brains updates itself intelligently on its own, that is integral to its function, if our brains couldn't do that we wouldn't be intelligent, when that happens we say a person has Alzheimer's. You wouldn't hire a person with far gone Alzheimer's precisely because they can't learn. Edit: So you can see in brains being able to form connections makes it smarter. In our neural nets when we try to alter their connections live they get dumber, which is why we don't do it. That should tell you that we are clearly missing something critical here.
hooverd 2y ago
What's with AI boosters and not viewing other people as human?
- error_logic 2y ago
  In my experience it's not even dismissing the humanity of others, it's recognizing their own minds following similar patterns. In my youth I lacked the confidence to speak without a sentence "pre-written" in my mind and would stall out if I ran out of written material. It caused delays in conversation and lagging sometimes minutes behind the chatter of peers. Since I've gained more experience and confidence in adulthood I can talk normally and trust that the sentence will "work itself out" but it seems like most people gloss over that implicit trust in their own impulses. It really gets in the way to be too self-conscious so I can understand it being something most people would benefit from being able to ignore...selfishly, at least. Lots of stupidity from people not thinking through the cumulative/collective effects of their actions if everyone follows the same patterns, though.
  - daveguy 2y ago
    I think a lot of this confidence that the sentence will "work itself out" has to do with being able to frame a general direction of the thought before you start, but not have the precise sentence. It takes advantage of the continual parallel processing humans perform in their brain. Confidence in a simple structure of what you expect to convey. When LLMs are able to generate this kind of dynamic structure from a separate logical/heuristic process + fill in the blanks efficiently, then I think we are getting close to AGI. That's a very Chomsky informed view of sentence structure and human communication, but I believe it is correct. Currently the future tokens are dependent on the probabilistic next token rather than having the outline of a structure determined from the start (sentence or idea structure). When LLMs are able to incorporate the structured outline I think we will be much closer to an AGI, but that is an algorithmic problem we have not been able to figure out and one that may not be feasible until we have parallel processing equivalent to a human brain.
- sensanaty 2y ago
  AI bros have a vested interest in people believing the hype that we're just around the corner of figuring out AGI or whatever the buzzword of the week is. They'll anthropomorphize LLMs in a variety of ways, that way people will be more likely to believe it's some kind of magical system that can do everyone's jobs. It's also useful when trying to downplay the harm these systems cause, like the mass theft of literally everything in order to facilitate training (the favorite refrain of "They learn just like humans do!" - By consuming 19 trillion books and then spitting out random words from those books, yeah real humanlike), boiling entire lakes to power training, wasting billions of dollars etc. Many of them are also solipsistic sociopaths that believe everyone else is just a useful idiot that'll help make them fabulously wealthy, so they have to bring everyone else down in order for the AIs to seem useful beyond the initial marketing coolness.
daveguy 2y ago
Equating human reasoning to regurgitating a single token at a time requires that you pretend there are not trillions of analog calculations happening in parallel in the human mind. How could there not be a massive search included as part of the reasoning process? LLMs do not perform a massive search, or in any way update their reasoning capability after the model is generated.
- deleted 2y ago
  [deleted]

You can still regurgitate a chain of thought response… You people are so close to getting it. So close to understanding that you're the ones doing the regurgitating.

dartos 2y ago

That sword cuts both ways.

CamperBob2 2y ago
I mean, you talk about a predictable, deterministic next-token generator...

fmbb 2y ago

Why do you believe that is how humans reason?

mrbungie 2y ago
Neuroscience and evidence be damned, [the brain is a computer] is being transformed into [the brain is an LLM]. Happens with every new technology. Edit: What is happening with people these days? They seem to be reducing people and their minds to machines more easily than ever.
- CamperBob2 2y ago
  You want to talk evidence, let's talk evidence. What does the brain do that these models don't (or, more generally, can't)? Be specific. And no, it doesn't "happen with every new technology." Nothing even remotely like this has been seen before the present decade.
  - mjmas 2y ago
    Have experiences and feelings. Have personal knowledge of something. Be able to express language with a training/knowledge/etc corpus of far smaller size.
    - error_logic 2y ago
      A lot of that is provided by multi-modality including feedback from the body and interacting with objects and people with more than just the words. That expands the context of a human's experiences dramatically compared to just reading books. Plus even when humans are reading books a mental image of what's going on in the story is common. Not everyone has that, but it shows how much a basic LLM lacks and multi-modal would add. Now the real question to my mind is whether we can train models with actual empathy to learn from the experiences of other people without having to go through the experience directly. Doing so would put them above many individuals' understanding already. . .
    - ben_w 2y ago
      While I agree with: > Be able to express language with a training/knowledge/etc corpus of far smaller size. Nobody knows what it would mean to test for: > Have experiences and feelings. And I am unclear if: > Have personal knowledge of something. Would or would not be met by putting them in charge of a robot of some kind to collect experiences? I'd certainly agree that the default is to make them "book smart" rather than "street smart", but at most that's an "is not" rather than "cannot".
  - mrbungie 2y ago
    You can't win this argument through logic less mere rhetoric. You need scientific evidence. Hint: There isn't. Edit: Ok, let me be less harsh and play with your argument. Can an LLM have an acid/psilocybin/ketamine trip, distort its view of self and reality, then rewire some of its internal connections and come a little different based on the experience? I guess not, there are more examples and they all show that as far as we know LLMs are not minds/brains and viceversa even if they seem similar in a chat interface. (If you don't empathise with that example remove the drugs and change them for a near-death experience). I strongly argue humans are not (just) LLMs, but I think we're far from getting evidence*. I think we will get to AGI before we know how the brain works and there is no dicotomy in that, if anything the tech is showing us that we can get at least some intelligence in other substrates. *PS: fwiw ausence of evidence does not support any sides. I shouldn't remark that, but here we are.
    - ben_w 2y ago
      > You need scientific evidence This, while true, is putting the cart before the horse. One needs to define what the question is before it is possible to seek evidence. I see many things different between Transformer models and brains, but I do not know which, if any, matters. > Can an LLM have an acid/psilocybin/ketamine trip, distort its view of self and reality, then rewire some of its internal connections and come a little different based on the experience? Specifically those chemicals? No, not even during training when the weights aren't frozen, as nobody bothered to simulate the relevant receptors*. Can LLMs experience anything, in the way we mean it? Nobody even knows. We don't know what the question means well enough to more than merely guess what to look for. Do the inputs to an LLM, a broader idea of "experience" without needing to solve the question of which definition of consciousness everyone should be using, rewire some of its internal connections? All the time. * caveat: it doesn't, at first glance, seem totally impossible that the structure of these models is sufficiently complicated for them to create the simulation of those things in the weights themselves in order to better model the behaviour of humans generating text from experiencing those things. But I also don't have any positive reason to expect this any more than I would expect an actor portraying a near-death-experience to have any idea what that's like on the inside.
  - mkleczek 2y ago
    The brain can invent formal language that enables unambiguously specifying an algorithm that when provided with a huge amount of input data can simulate the output of the brain itself. Can these models do that?
    - ben_w 2y ago
      > The brain can invent formal language that enables unambiguously specifying an algorithm that when provided with a huge amount of input data can simulate the output of the brain itself. Can *a* brain do this? Every effort I've seen has (1) involved *many people* working on different parts of the problem, and (2) so far we've only got partial solutions. Even if you allow collaboration, then when (2) goes away, your question is trivially answered "yes" because we just feed into the model the same experiences that were fed into the human(s) who invented the model, and the simulation then simulates them inventing the same model.
      - Jensson 2y ago
        > Even if you allow collaboration, then when (2) goes away, your question is trivially answered "yes" because we just feed into the model the same experiences that were fed into the human(s) who invented the model, and the simulation then simulates them inventing the same model. These models doesn't work that way. Its probably possible to make such a model, but currently we don't know how to make them.
        ben_w 2y ago
        I literally said so, yes.
      - mkleczek 2y ago
        Do you have empirical proof any LLM can generate another program that can be trained to simulate it? Otherwise it is science fiction.
        ben_w 2y ago
  - sgt101 2y ago
    I'm no neuroscientist, but I hung out with a few, so here are some bits I picked up from them. In biological brians: - neurons have signal surpression (-ive activation) as well as propagation (+activation) - neurons appear to process and propagation due to unknown internal mechanisms. - there are complex sub structures within biological neural nets where the architecture is radically different from in other sections of the network, strongly in contrast to the homogenous structure of ANN's - many different types of neurons with different properties and behaviors in terms of network formation and network activity are present in BNN's in contrast to ANN's - BNN's learn during processing. ANN's are static after training.
    - qrsjutsu 2y ago
      [dead]
    - CamperBob2 2y ago
      Those are mostly structure-versus-function arguments, centered on what a brain is as opposed to what it does. Only the static nature of the current ML models seems like a valid point... and it's changing too. I'd wager that future models (which admittedly may not look much like today's LLMs) will blur the lines between mutable context and immutable weights. When that happens, all bets are truly off.
      - daveguy 2y ago
        > future models (which admittedly may not look much like today's LLMs) will blur the lines between mutable context and immutable weights. "may not look much like today's LLMs" is really sweeping the whole point under the rug. The difference between what we are doing now with static LLM models and dynamic models will require algorithmic changes that have not yet been invented. That's in addition to the fact that the processing of a single neuron is completely parallel with respect to inputs. GPUs make them more parallel, but the analog devices we are trying to mimic are vastly more complicated than what we are using as a substitute.
        CamperBob2 2y ago
- ninetyninenine 2y ago
  The brain is a neural net. And so is the LLM.
  - Jensson 2y ago
    > The brain is a neural net No it isn't, neural networks aren't a network of neurons, they are just named after neurons but they are nothing alike. Neurons grow new connections etc and do a whole slew of different things that neural networks doesn't do. The ability to grow new connections seems like an integral part to intelligence that neural networks can't replicate, at least not without changing them to something very different than they are today.
    - ninetyninenine 2y ago
      The brain is a neural net. It’s made up of neurons that are networked. You’re just pointing out differences between a LLM and a brain. The fact that they are both neural nets is a similarity. Forming new connections Is not integral. Connections don’t form in seconds so second to second existence is a static neural network. Thus your second to second existence is comparable to an LLM in the sense that both neural networks don’t form new connections. We can though. We can make neural networks form new connections.
      - Jensson 2y ago
        > The brain is a neural net. It’s made up of neurons that are networked The brain neurons are not the same kind of object as neural net neurons, hence its not a neural net. It is like saying a dragonfly is a helicopter since the helicopter were designed to look a bit like a firefly, no a dragonfly isn't a helicopter they fly in totally different ways the dragonfly has wings and thus don't even move similarly. > Connections don’t form in seconds so second to second existence is a static neural network But a human isn't intelligent over a second, and synapses do form over longer periods which is what it takes for humans to solve harder problems. > We can though. We can make neural networks form new connections. I said the neural network couldn't form those, not that we couldn't alter the neural network. That is a critical difference, our brains updates itself intelligently on its own, that is integral to its function, if our brains couldn't do that we wouldn't be intelligent, when that happens we say a person has Alzheimer's. You wouldn't hire a person with far gone Alzheimer's precisely because they can't learn. Edit: So you can see in brains being able to form connections makes it smarter. In our neural nets when we try to alter their connections live they get dumber, which is why we don't do it. That should tell you that we are clearly missing something critical here.

hooverd 2y ago

What's with AI boosters and not viewing other people as human?

error_logic 2y ago
In my experience it's not even dismissing the humanity of others, it's recognizing their own minds following similar patterns. In my youth I lacked the confidence to speak without a sentence "pre-written" in my mind and would stall out if I ran out of written material. It caused delays in conversation and lagging sometimes minutes behind the chatter of peers. Since I've gained more experience and confidence in adulthood I can talk normally and trust that the sentence will "work itself out" but it seems like most people gloss over that implicit trust in their own impulses. It really gets in the way to be too self-conscious so I can understand it being something most people would benefit from being able to ignore...selfishly, at least. Lots of stupidity from people not thinking through the cumulative/collective effects of their actions if everyone follows the same patterns, though.
- daveguy 2y ago
  I think a lot of this confidence that the sentence will "work itself out" has to do with being able to frame a general direction of the thought before you start, but not have the precise sentence. It takes advantage of the continual parallel processing humans perform in their brain. Confidence in a simple structure of what you expect to convey. When LLMs are able to generate this kind of dynamic structure from a separate logical/heuristic process + fill in the blanks efficiently, then I think we are getting close to AGI. That's a very Chomsky informed view of sentence structure and human communication, but I believe it is correct. Currently the future tokens are dependent on the probabilistic next token rather than having the outline of a structure determined from the start (sentence or idea structure). When LLMs are able to incorporate the structured outline I think we will be much closer to an AGI, but that is an algorithmic problem we have not been able to figure out and one that may not be feasible until we have parallel processing equivalent to a human brain.
sensanaty 2y ago
AI bros have a vested interest in people believing the hype that we're just around the corner of figuring out AGI or whatever the buzzword of the week is. They'll anthropomorphize LLMs in a variety of ways, that way people will be more likely to believe it's some kind of magical system that can do everyone's jobs. It's also useful when trying to downplay the harm these systems cause, like the mass theft of literally everything in order to facilitate training (the favorite refrain of "They learn just like humans do!" - By consuming 19 trillion books and then spitting out random words from those books, yeah real humanlike), boiling entire lakes to power training, wasting billions of dollars etc. Many of them are also solipsistic sociopaths that believe everyone else is just a useful idiot that'll help make them fabulously wealthy, so they have to bring everyone else down in order for the AIs to seem useful beyond the initial marketing coolness.

daveguy 2y ago

Equating human reasoning to regurgitating a single token at a time requires that you pretend there are not trillions of analog calculations happening in parallel in the human mind. How could there not be a massive search included as part of the reasoning process? LLMs do not perform a massive search, or in any way update their reasoning capability after the model is generated.

deleted 2y ago
[deleted]