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GPT-4 reasons about a lot of gotcha logic puzzles correctly any pre GPT-4 opinions should be reconsidered, that is effectively two different epochs in the histo

GPT-4 reasons about a lot of gotcha logic puzzles correctly any pre GPT-4 opinions should be reconsidered, that is effectively two different epochs in the history of AI effectiveness and reasoning. > Me: Mike leaves the elevator at the 2nd floor. Jenny leaves at the 9th floor. Who left the elevator first? > GPT-4: Mike left the elevator first, as he got off at the 2nd floor, while Jenny left at the 9th floor. > Me: If the elevator started at the 10th floor, who would have left first? > GPT-4: If the elevator started at the 10th floor and went downward, then Jenny would have left first, as she got off at the 9th floor, while Mike left at the 2nd floor. > Me: How many footballs fit in a coffe-mug? > GPT-4: A standard football (soccer ball) has a diameter of around 22 centimeters (8.65 inches), while a coffee mug is typically much smaller, with a diameter of around 8-10 centimeters (3-4 inches). Therefore, it is not possible to fit a standard football inside a coffee mug. If you were to use a mini football or a much larger mug, the number of footballs that could fit would depend on the specific sizes of the footballs and the mug. It easily answered all of your questions and produces explanations I would expect most reasonable people to make.

usrbinbash 4y ago
That changes exactly nothing about the validity of my statement. Yes, GPT-4 is better at this mimicry than GPT-3 or GPT-3.5. And GPT-3 was better at it than GPT-2. And all of them were better than my out-of-fun home-built Language Model projects that I trained on small <10GiB Datasets, which in turn were better at it than my Poc models trained on just a few thousand words. But being better at mimicking reason, is still not reasoning. The model doesn't know what a coffeemug is, and it doesn't know what a football is. It also has no idea how elevators work. It can form sequences that make it look to us that it does and knows all these things, but in reality, it only knows that "then Jenny would have left first" is a more likely sequence of tokens at that point, given that the sequence before included "started at the 10th floor". Bear in mind, this doesn't mean that this mimicry isn't useful. It is, tremendously so. I don't care how I get correct answers, I only care that I do.
- ianbutler 4y ago
  I agree about the utility part. However, I don't really accept the idea that this isn't reasoning, but I'm not entirely sold either way. I'd say if it mimics something well enough then eventually it's just doing the thing, which is the same side of the argument I fall on with Searle's Chinese Room Argument. If you can't discern a difference, is there a difference? So far GPT-4 can produce better work than like 50% of humans and better responses to brain teaser questions than most of them too, I'm at least just in a bubble and so I don't run into people that stupid that often. So it's easier for me to see the gaps still.
  - usrbinbash 4y ago
    > I'd say if it mimics something well enough then eventually it's just doing the thing Right up to the point where it actually needs to reason, and the mimickry doesn't suffice. My above example about the Football and the Coffemug is an easy one, the objects are well represented in its training data. What if I need a reason why the Service Ping spikes every 60 seconds, here is the code, please LLM look it up. I am sure I will get a great and well written answer. I am also sure it won't be the correct one, which is that some dumb script I wrote, which has nothing to do with the code shown, blocks the server for about 700ms every minute. Figuring out that something cannot be explained with the data represented, and thus may come from a source unseen, is one example of actual reasoning. And this "giving up on the data shown" is something I have yet to see any AI do.
    - ianbutler 4y ago
      I could say the same about most second rate software engineers. Thats why im not moved by your arguments. Theres plenty of peope just as stupid and who will give you confidently wrong answers.
- m4nu3l 4y ago
  > But being better at mimicking reason, is still not reasoning How do I know people are not using a similar process when they perform "reasoning" but with a way more elaborate model? Can you prove me that the two are inherently different in the type of output they produce regardless of how large a ML model is or can be? Because if you can't, and they produce the same type of output, the processing could be similar enough to be considered reasoning.

usrbinbash 4y ago

That changes exactly nothing about the validity of my statement. Yes, GPT-4 is better at this mimicry than GPT-3 or GPT-3.5. And GPT-3 was better at it than GPT-2. And all of them were better than my out-of-fun home-built Language Model projects that I trained on small <10GiB Datasets, which in turn were better at it than my Poc models trained on just a few thousand words. But being better at mimicking reason, is still not reasoning. The model doesn't know what a coffeemug is, and it doesn't know what a football is. It also has no idea how elevators work. It can form sequences that make it look to us that it does and knows all these things, but in reality, it only knows that "then Jenny would have left first" is a more likely sequence of tokens at that point, given that the sequence before included "started at the 10th floor". Bear in mind, this doesn't mean that this mimicry isn't useful. It is, tremendously so. I don't care how I get correct answers, I only care that I do.

ianbutler 4y ago
I agree about the utility part. However, I don't really accept the idea that this isn't reasoning, but I'm not entirely sold either way. I'd say if it mimics something well enough then eventually it's just doing the thing, which is the same side of the argument I fall on with Searle's Chinese Room Argument. If you can't discern a difference, is there a difference? So far GPT-4 can produce better work than like 50% of humans and better responses to brain teaser questions than most of them too, I'm at least just in a bubble and so I don't run into people that stupid that often. So it's easier for me to see the gaps still.
- usrbinbash 4y ago
  > I'd say if it mimics something well enough then eventually it's just doing the thing Right up to the point where it actually needs to reason, and the mimickry doesn't suffice. My above example about the Football and the Coffemug is an easy one, the objects are well represented in its training data. What if I need a reason why the Service Ping spikes every 60 seconds, here is the code, please LLM look it up. I am sure I will get a great and well written answer. I am also sure it won't be the correct one, which is that some dumb script I wrote, which has nothing to do with the code shown, blocks the server for about 700ms every minute. Figuring out that something cannot be explained with the data represented, and thus may come from a source unseen, is one example of actual reasoning. And this "giving up on the data shown" is something I have yet to see any AI do.
  - ianbutler 4y ago
    I could say the same about most second rate software engineers. Thats why im not moved by your arguments. Theres plenty of peope just as stupid and who will give you confidently wrong answers.
m4nu3l 4y ago
> But being better at mimicking reason, is still not reasoning How do I know people are not using a similar process when they perform "reasoning" but with a way more elaborate model? Can you prove me that the two are inherently different in the type of output they produce regardless of how large a ML model is or can be? Because if you can't, and they produce the same type of output, the processing could be similar enough to be considered reasoning.