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> but it can ace the bar exam A traditional static program could ace the bar exam, or even a well-prepared stack of flash cards. We wouldn't say the flash card

> but it can ace the bar exam A traditional static program could ace the bar exam, or even a well-prepared stack of flash cards. We wouldn't say the flash cards are exhibiting intelligence though, only their creators. > The whole discussion is about intelligence. I was replying to OP. OP was replying to Scientific American's article making numerous unfounded claims of "intelligence." So the party you should be asking for a definition is Scientific American. > MY definition of intelligence is degree of ability to apply prior experience to correctly predict future outcomes And what constitutes a "prediction" exactly? If someone fails to catch a baseball, would you say it's still possible they correctly predicted how to catch it? Because, if not, that would mean control over a body is integral to intelligence. If yes, then any object could be claimed intelligent but lacking in bodily function. If something predicts the future without applying prior experience, does that make it more intelligent or less intelligent? > you seem to want to side with OP and say that GPT-4 isn't intelligent - so it's YOUR definition of intelligence that would be needed support your position. You're trying to put words in my mouth, but I will play along. I'll say intelligence is the ability to autonomously create increasingly complete and consistent axiomatic systems. Since GPT-4 is digital, operating according to decisions (axioms) determined solely by external programmers and external data with little to no concern for consistency, I would say it's not intelligent. However, if a similar schema were applied to some sort of analog computer that had the ability to fluctuate or disobey its instructions then there would be more room for debate.

HarHarVeryFunny 3y ago
The way GPT-4 works is by having built a world model of the generative processes that produced the data it was trained on. The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become. I'm not sure where you are seeing daylight between this and your own definition of intelligence. FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's. The values are imperfectly calculated (limited accuracy) as computer math always is. There is also a large element of pure randomness to the output of any of these LLMs. They don't get to control exactly what words they generate ... the model generates probabilities over 10's of thousands of possible output words, and a random number generator is used to select one of the higher rated words to output. This semi-random word is then fed back into the model, for it to "generate" the next word ... it is continuously having to adapt to this randomness forced upon it.
- mike741 3y ago
  > The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become. Increasing training data doesn't increase consistency. Each data point acts as a potential new axiom, and each axiom decreases consistency. GPT-4 is trained to satisfy humans, and humans are wildly inconsistent. Even if humans were perfectly consistent, attempting to satisfy multiple different humans simultaneously results in inconsistency. Additionally, even if GPT-4 were perfectly complete and consistent it still wouldn't have reached this state autonomously. So the difference between GPT-4 and intelligence, by my definition, is night and day. > FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's. Floating points are digital 1's and 0's. Adding more digits is never going to make something analog. > The values are imperfectly calculated (limited accuracy) as computer math always is. Agreed. >There is also a large element of pure randomness to the output of any of these LLMs. Strongly disagree. There isn't a single element of randomness during the training stage. We know the exact architecture of the neural net, we know the exact data it was trained on, and we know the exact beam selection algorithms used to synthesize outputs. Every single step can be simulated, traced, and recreated to achieve the exact same results. The number of steps involved might overwhelm us, but that doesn't make it random. > They don't get to control exactly what words they generate We do get to control it, we just lose track of the inputs and then pretend it was all out of our control. But of course every single step was willed and controlled by us. We call it "random" for personal convenience, not because its actually true.
  - HarHarVeryFunny 3y ago
    These models don't output sequences - which is where you'd use beam search - they output a single word at a time. The output is a set of probabilities (from a SoftMax) which is then sampled at a given sampling "temperature" (degree of randomness). There's no point discussing it when you obviously don't have clue how these models work, won't listen when you're told, and just prefer to make stuff up.
    - mike741 3y ago

HarHarVeryFunny 3y ago

The way GPT-4 works is by having built a world model of the generative processes that produced the data it was trained on. The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become. I'm not sure where you are seeing daylight between this and your own definition of intelligence. FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's. The values are imperfectly calculated (limited accuracy) as computer math always is. There is also a large element of pure randomness to the output of any of these LLMs. They don't get to control exactly what words they generate ... the model generates probabilities over 10's of thousands of possible output words, and a random number generator is used to select one of the higher rated words to output. This semi-random word is then fed back into the model, for it to "generate" the next word ... it is continuously having to adapt to this randomness forced upon it.

mike741 3y ago
> The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become. Increasing training data doesn't increase consistency. Each data point acts as a potential new axiom, and each axiom decreases consistency. GPT-4 is trained to satisfy humans, and humans are wildly inconsistent. Even if humans were perfectly consistent, attempting to satisfy multiple different humans simultaneously results in inconsistency. Additionally, even if GPT-4 were perfectly complete and consistent it still wouldn't have reached this state autonomously. So the difference between GPT-4 and intelligence, by my definition, is night and day. > FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's. Floating points are digital 1's and 0's. Adding more digits is never going to make something analog. > The values are imperfectly calculated (limited accuracy) as computer math always is. Agreed. >There is also a large element of pure randomness to the output of any of these LLMs. Strongly disagree. There isn't a single element of randomness during the training stage. We know the exact architecture of the neural net, we know the exact data it was trained on, and we know the exact beam selection algorithms used to synthesize outputs. Every single step can be simulated, traced, and recreated to achieve the exact same results. The number of steps involved might overwhelm us, but that doesn't make it random. > They don't get to control exactly what words they generate We do get to control it, we just lose track of the inputs and then pretend it was all out of our control. But of course every single step was willed and controlled by us. We call it "random" for personal convenience, not because its actually true.
- HarHarVeryFunny 3y ago
  These models don't output sequences - which is where you'd use beam search - they output a single word at a time. The output is a set of probabilities (from a SoftMax) which is then sampled at a given sampling "temperature" (degree of randomness). There's no point discussing it when you obviously don't have clue how these models work, won't listen when you're told, and just prefer to make stuff up.
  - mike741 3y ago