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> it's not capable of actually reasoning Define reasoning. Because in my definition GPT 4 could reason without doubt. It definitely can't reason better than ex

> it's not capable of actually reasoning Define reasoning. Because in my definition GPT 4 could reason without doubt. It definitely can't reason better than experts in the field, but it could reason better than say interns.

intended 3y ago
ChatGPT is trained on text that includes most reasoning problems that people come up with. You see reasoning issues when you use more real world examples, rather than theoretical tests. I had 4 failure states. 1) Summarization: It summarized 3 transcripts correctly, for the fourth it described the speaker as a successful VC. The speaker was a professor. 2) It was to act as a classifier, with a short list of labels. Depending on the length of text, the classifier would swap over to text gen. Other issues included novel labels, new variations of labels, and so on. 3) Agents - This died on the vine. Leave having to learn asynch, vector DBs or whatever. You can never trust the output of an LLM, so you can never chain agents. 4) I focused on using ChatGPT to complete a project. I hadnt touched HTML ever - the goal was to use ChatGPT to build the site. This would cover design, content, structure, development, hosting, and improvements. I still have trauma. Wrong code, bad design, were base issues. If code was correct, it simply meant I had dug a deeper grave. I had anticipated 70% of the work being handled by ChatGPT, it ended up at 30% at the most. ChatGPT is great IF you already are a subject expert - you can brush over the issues and move on. "Hallucinations" is the little bit of string that you pull on, and the rest unravels. There are no hallucinations, only humans can hallucinate - because we have an actual ground truth to work with. LLMs are only creating the next token. For them to reason, they must be holding structures and proxies in some data store, and actively altering it. Its easier to see once you deal with hallucinations.
deleted 3y ago
[deleted]
mcguire 3y ago
What is your definition?
- YetAnotherNick 3y ago
  If it can solve basic logic problems, then it could reason. And if it could write code of a new game with new logic, then it could reason for sure. Example of basic problem: In a shop, there are 4 dolls of different heights P,Q,R and S. S is neither as tall as P nor as short as R. Q is shorter than S but taller than R. If Kittu wants to purchase the tallest doll, which one should she purchase? Think step by step.
  - kaibee 3y ago
    Seems to handle it easily. https://chat.openai.com/share/4d8ab2af-f824-44c8-9311-e3893c744612 https://chat.openai.com/share/4d8ab2af-f824-44c8-9311-e3893c...
szundi 3y ago
Really?
NovemberWhiskey 3y ago
I don't have access to GPT 4 but I'd be interested to see how it does on a question like this: "Say I have a container with 50 red balls and 50 blue balls, and every time I draw a blue ball from the container, I add two white balls back. After drawing 100 balls, how many of each different color ball are left in the container? Explain why." ... because on GPT 3.5 the answer begins like the below and then gets worse: "Let's break down the process step by step: Initially, you have 50 red balls and 50 blue balls in the container. 1) When you draw a blue ball from the container, you remove one blue ball, and you add two white balls back. So, after drawing a blue ball, you have 49 blue balls (due to removal) and you add 2 white balls, making it a total of 52 white balls (due to addition) ..." If I was hiring interns this dumb, I'd be in trouble. EDIT: judging by the GPT-4 responses, I remain of the opinion I'd be in trouble if my interns were this dumb.
- jiggawatts 3y ago
  "You're holding it wrong." You're asking GPT to do maths in its head, the AI equivalent of a person standing in the middle of the room with no tools and getting grilled in a oral examination of their knowledge. Instead, collaborate with it, while giving it the appropriate tools to help you. I asked it to write a Monte Carlo simulation of the problem in Wolfram Mathematica script. It did this about 10-100x faster than I would have been able to. It made a few small mistakes with the final visualisation, but I managed to get it to output a volumetric plot showing the 3D scatter plot of the histogram of possible outcomes. I even got it to save a video of the plot rotating: https://streamable.com/2aphbz https://streamable.com/2aphbz
  - freejazz 3y ago
    AI can reason! Just not reasonably!
    - jiggawatts 3y ago
      It can reason better than most humans put into the same situation. This problem doesn't result in a constant value, it results in a 3D probability distribution! Very, very few humans could work that out without tools. (I'm including pencil and paper in "tools" here.) With only a tiny bit of coaxing, GPT 4 produced an animated video of the solution! Try to guess what fraction of the general population could do that at all. Also try to estimate what fraction of general software developers could solve it in under an hour.
_trapexit 3y ago
It's not reasoning. It's word prediction. At least at the individual model level. OpenAI is likely using a collection of models.

intended 3y ago

ChatGPT is trained on text that includes most reasoning problems that people come up with. You see reasoning issues when you use more real world examples, rather than theoretical tests. I had 4 failure states. 1) Summarization: It summarized 3 transcripts correctly, for the fourth it described the speaker as a successful VC. The speaker was a professor. 2) It was to act as a classifier, with a short list of labels. Depending on the length of text, the classifier would swap over to text gen. Other issues included novel labels, new variations of labels, and so on. 3) Agents - This died on the vine. Leave having to learn asynch, vector DBs or whatever. You can never trust the output of an LLM, so you can never chain agents. 4) I focused on using ChatGPT to complete a project. I hadnt touched HTML ever - the goal was to use ChatGPT to build the site. This would cover design, content, structure, development, hosting, and improvements. I still have trauma. Wrong code, bad design, were base issues. If code was correct, it simply meant I had dug a deeper grave. I had anticipated 70% of the work being handled by ChatGPT, it ended up at 30% at the most. ChatGPT is great IF you already are a subject expert - you can brush over the issues and move on. "Hallucinations" is the little bit of string that you pull on, and the rest unravels. There are no hallucinations, only humans can hallucinate - because we have an actual ground truth to work with. LLMs are only creating the next token. For them to reason, they must be holding structures and proxies in some data store, and actively altering it. Its easier to see once you deal with hallucinations.

deleted 3y ago

[deleted]

mcguire 3y ago

What is your definition?

YetAnotherNick 3y ago
If it can solve basic logic problems, then it could reason. And if it could write code of a new game with new logic, then it could reason for sure. Example of basic problem: In a shop, there are 4 dolls of different heights P,Q,R and S. S is neither as tall as P nor as short as R. Q is shorter than S but taller than R. If Kittu wants to purchase the tallest doll, which one should she purchase? Think step by step.
- kaibee 3y ago
  Seems to handle it easily. https://chat.openai.com/share/4d8ab2af-f824-44c8-9311-e3893c744612 https://chat.openai.com/share/4d8ab2af-f824-44c8-9311-e3893c...

szundi 3y ago

Really?

NovemberWhiskey 3y ago

I don't have access to GPT 4 but I'd be interested to see how it does on a question like this: "Say I have a container with 50 red balls and 50 blue balls, and every time I draw a blue ball from the container, I add two white balls back. After drawing 100 balls, how many of each different color ball are left in the container? Explain why." ... because on GPT 3.5 the answer begins like the below and then gets worse: "Let's break down the process step by step: Initially, you have 50 red balls and 50 blue balls in the container. 1) When you draw a blue ball from the container, you remove one blue ball, and you add two white balls back. So, after drawing a blue ball, you have 49 blue balls (due to removal) and you add 2 white balls, making it a total of 52 white balls (due to addition) ..." If I was hiring interns this dumb, I'd be in trouble. EDIT: judging by the GPT-4 responses, I remain of the opinion I'd be in trouble if my interns were this dumb.

jiggawatts 3y ago
"You're holding it wrong." You're asking GPT to do maths in its head, the AI equivalent of a person standing in the middle of the room with no tools and getting grilled in a oral examination of their knowledge. Instead, collaborate with it, while giving it the appropriate tools to help you. I asked it to write a Monte Carlo simulation of the problem in Wolfram Mathematica script. It did this about 10-100x faster than I would have been able to. It made a few small mistakes with the final visualisation, but I managed to get it to output a volumetric plot showing the 3D scatter plot of the histogram of possible outcomes. I even got it to save a video of the plot rotating: https://streamable.com/2aphbz https://streamable.com/2aphbz
- freejazz 3y ago
  AI can reason! Just not reasonably!
  - jiggawatts 3y ago
    It can reason better than most humans put into the same situation. This problem doesn't result in a constant value, it results in a 3D probability distribution! Very, very few humans could work that out without tools. (I'm including pencil and paper in "tools" here.) With only a tiny bit of coaxing, GPT 4 produced an animated video of the solution! Try to guess what fraction of the general population could do that at all. Also try to estimate what fraction of general software developers could solve it in under an hour.

_trapexit 3y ago

It's not reasoning. It's word prediction. At least at the individual model level. OpenAI is likely using a collection of models.