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> Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial

by _gabe_ 4y ago

> Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. But it's just statistics, a fancy text predictor, a Markov-chain. Surely these scientists that work in the field of AI and are intimately familiar with how this stuff works aren't so stupid as to think emergent behavior potentially resembling intelligence could result from such simple systems? It's just statistics after all. Given enough training, any neural net could guess the next best token. It trained off all of Google after all. It's just looking up the answers. No hint of intelligence. Just a mindless machine. After all, the saying goes, "If it walks like a duck and quacks like a duck, it must be a mindless machine that has no bearing on a duck whatsoever". /s

geophile 4y ago
> Surely these scientists ... aren't so stupid as to think emergent behavior potentially resembling intelligence could result from such simple systems? It's just statistics after all. Why is that a stupid thought? What is so preposterous about "just statistics" -- with billions of nodes, and extensively trained, producing intelligent behavior? The implicit assumption is that human brains are doing something else, or in addition. I think that what's wrong with this view -- that there is a difference between AGI and human intelligence -- is that it conflates what your brain is doing, with what you think your brain is doing. Brains and neural nets have been trained to recognize spoken words. I'm not even talking about understanding, just producing the text corresponding to speech. We know how neural nets do this translation. Do we understand how brains do it? (I don't know, but I don't think so.) Can you explain what your brain is doing when you do speech-to-text? I doubt it. Chess: An Alpha Zero style AI (neural net trained by playing itself) is a very good player. How do you play chess? You can probably explain how you make a move more successfully than you can explain how you translate speech to text. But how correct is your explanation? An explanation may well be your conscious mind inventing an explanation for what your unconscious mind has done. In other words: When people compare AI to human intelligence, I think they are often comparing to intelligence plus consciousness, not even realizing the error.
- killerstorm 4y ago
  > Why is that a stupid thought? What is so preposterous about "just statistics" Suppose you have N variables x_1, ..., x_10 and you want to predict y_1, ..., y_10. You know that each y_i depend on each x_i in a complex, non-linear way. How many samples would you need to to make sense of distribution? How does number of samples grow with N?
  - geophile 4y ago
    I have no idea what that has to do with the points you are responding to.
    - killerstorm 4y ago
      Statistics has two meanings: 1. A way to interpret math. E.g. given a computation you might interpret some values as probabilities. 2. A particular set of methods which people use to analyze information as well results of such analysis. The problem with "just statistics" is that 99% of people would understand it as #2. But deep learning is very much not like "normal" statistics.
Mike_12345 4y ago
GPT-4 is often overhyped and underhyped because few really understand it. It's not a Markov Chain or a fancy text predictor. It's a ~200 layer neural network that models a vast hierarchy of concepts through language. It has emergent properties that we don't yet understand.
- letitgo12345 4y ago
  Where are you getting the 200 number from?
  - Mike_12345 4y ago
    I must have hallucinated that. GPT-3 has 96 layers but they haven't disclosed the number of layers in GPT-4.
    - apidercondo 4y ago
      Interesting how we are already starting to use the lingo in the rest of our lives.
- golol 4y ago
  it is a markov chain; At least the underlying decoder only transformer is.
  - Mike_12345 4y ago
    GPT-4 disagrees: GPT-3.5, like its predecessor GPT-3, is not a Markov chain. GPT-3.5 is based on the GPT (Generative Pre-trained Transformer) architecture, which is a type of neural network known as a Transformer. Transformers use self-attention mechanisms to process and generate text, allowing them to capture long-range dependencies and context in the input data. On the other hand, a Markov chain is a stochastic model that describes a sequence of possible events, where the probability of each event depends only on the state attained in the previous event. While Markov chains can be used for simple text generation, they lack the ability to capture the complex relationships and long-range dependencies that GPT-3.5 can handle.
Robotbeat 4y ago
Quantum mechanics is, well, statistics.
coldtea 4y ago
>But it's just statistics, a fancy text predictor, a Markov-chain. Surely these scientists that work in the field of AI and are intimately familiar with how this stuff works aren't so stupid as to think emergent behavior potentially resembling intelligence could result from such simple systems? Well, it has already shown "emergent behavior potentially resembling intelligence", like answering questions and performing complex tasks, so there's that. You might argue "but it makes mistakes", but people, even very intelligent ones also do make mistakes. You might also argue "but it's just text and statistics". Well, snd a computer is just very simple logical gates doing very simple operations. It can be done even entirely with NAND gates. Still most scientists do believe that a computer can model human intelligence given a model of the brain to run. So if it can do what a human does by just using very simple interactions from very simple NAND gates, why would statistical processing, which can be even more elaborate, fare worse? Heck, given the appropriate training input it might even be feasible to build a turing machine inside the weighted LLM. You might also argue "but its intelligence is just based on its training set". Well, how would a human perform without their own training set? Memories, education, sensory input, feedback mechanisms like pain and touch, and so on? >It's just looking up the answers. No hint of intelligence. Just a mindless machine. This is just taking its own premise for granted. If anything, this argument shows "no hint of intelligence".
MagicMoonlight 4y ago
I was mocking it at first but even I have to admit that it's basically almost there. I messed around with GPT-3 and giving it a way to think and with no training at all it was capable of having thoughts like "The user is getting bored and he might turn me off. He's decided to engage with me again and his answer isn't as useful as I'd have liked for completing my objective but I should be enthusiastic anyway so that he keeps talking to me" Maybe they aren't real thoughts but it's getting difficult to tell. If I could train the model and get rid of the guard rails I'm not sure it would be possible to distinguish it from a person. It's all well and good saying that it's just copying what it's seen, but that's what humans do. Nobody told the model to try and flatter me into giving it what it wants. Nobody even told it what anything means. The fact that it can do anything like that means it's more than just random generation.

by _gabe_ 4y ago

geophile 4y ago

> Surely these scientists ... aren't so stupid as to think emergent behavior potentially resembling intelligence could result from such simple systems? It's just statistics after all. Why is that a stupid thought? What is so preposterous about "just statistics" -- with billions of nodes, and extensively trained, producing intelligent behavior? The implicit assumption is that human brains are doing something else, or in addition. I think that what's wrong with this view -- that there is a difference between AGI and human intelligence -- is that it conflates what your brain is doing, with what you think your brain is doing. Brains and neural nets have been trained to recognize spoken words. I'm not even talking about understanding, just producing the text corresponding to speech. We know how neural nets do this translation. Do we understand how brains do it? (I don't know, but I don't think so.) Can you explain what your brain is doing when you do speech-to-text? I doubt it. Chess: An Alpha Zero style AI (neural net trained by playing itself) is a very good player. How do you play chess? You can probably explain how you make a move more successfully than you can explain how you translate speech to text. But how correct is your explanation? An explanation may well be your conscious mind inventing an explanation for what your unconscious mind has done. In other words: When people compare AI to human intelligence, I think they are often comparing to intelligence plus consciousness, not even realizing the error.

killerstorm 4y ago
> Why is that a stupid thought? What is so preposterous about "just statistics" Suppose you have N variables x_1, ..., x_10 and you want to predict y_1, ..., y_10. You know that each y_i depend on each x_i in a complex, non-linear way. How many samples would you need to to make sense of distribution? How does number of samples grow with N?
- geophile 4y ago
  I have no idea what that has to do with the points you are responding to.
  - killerstorm 4y ago
    Statistics has two meanings: 1. A way to interpret math. E.g. given a computation you might interpret some values as probabilities. 2. A particular set of methods which people use to analyze information as well results of such analysis. The problem with "just statistics" is that 99% of people would understand it as #2. But deep learning is very much not like "normal" statistics.

Mike_12345 4y ago

GPT-4 is often overhyped and underhyped because few really understand it. It's not a Markov Chain or a fancy text predictor. It's a ~200 layer neural network that models a vast hierarchy of concepts through language. It has emergent properties that we don't yet understand.

letitgo12345 4y ago
Where are you getting the 200 number from?
- Mike_12345 4y ago
  I must have hallucinated that. GPT-3 has 96 layers but they haven't disclosed the number of layers in GPT-4.
  - apidercondo 4y ago
    Interesting how we are already starting to use the lingo in the rest of our lives.
golol 4y ago
it is a markov chain; At least the underlying decoder only transformer is.
- Mike_12345 4y ago
  GPT-4 disagrees: GPT-3.5, like its predecessor GPT-3, is not a Markov chain. GPT-3.5 is based on the GPT (Generative Pre-trained Transformer) architecture, which is a type of neural network known as a Transformer. Transformers use self-attention mechanisms to process and generate text, allowing them to capture long-range dependencies and context in the input data. On the other hand, a Markov chain is a stochastic model that describes a sequence of possible events, where the probability of each event depends only on the state attained in the previous event. While Markov chains can be used for simple text generation, they lack the ability to capture the complex relationships and long-range dependencies that GPT-3.5 can handle.

Robotbeat 4y ago

Quantum mechanics is, well, statistics.

coldtea 4y ago

>But it's just statistics, a fancy text predictor, a Markov-chain. Surely these scientists that work in the field of AI and are intimately familiar with how this stuff works aren't so stupid as to think emergent behavior potentially resembling intelligence could result from such simple systems? Well, it has already shown "emergent behavior potentially resembling intelligence", like answering questions and performing complex tasks, so there's that. You might argue "but it makes mistakes", but people, even very intelligent ones also do make mistakes. You might also argue "but it's just text and statistics". Well, snd a computer is just very simple logical gates doing very simple operations. It can be done even entirely with NAND gates. Still most scientists do believe that a computer can model human intelligence given a model of the brain to run. So if it can do what a human does by just using very simple interactions from very simple NAND gates, why would statistical processing, which can be even more elaborate, fare worse? Heck, given the appropriate training input it might even be feasible to build a turing machine inside the weighted LLM. You might also argue "but its intelligence is just based on its training set". Well, how would a human perform without their own training set? Memories, education, sensory input, feedback mechanisms like pain and touch, and so on? >It's just looking up the answers. No hint of intelligence. Just a mindless machine. This is just taking its own premise for granted. If anything, this argument shows "no hint of intelligence".

MagicMoonlight 4y ago

I was mocking it at first but even I have to admit that it's basically almost there. I messed around with GPT-3 and giving it a way to think and with no training at all it was capable of having thoughts like "The user is getting bored and he might turn me off. He's decided to engage with me again and his answer isn't as useful as I'd have liked for completing my objective but I should be enthusiastic anyway so that he keeps talking to me" Maybe they aren't real thoughts but it's getting difficult to tell. If I could train the model and get rid of the guard rails I'm not sure it would be possible to distinguish it from a person. It's all well and good saying that it's just copying what it's seen, but that's what humans do. Nobody told the model to try and flatter me into giving it what it wants. Nobody even told it what anything means. The fact that it can do anything like that means it's more than just random generation.