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Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

thebullshitmachines.com

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Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#591
post #588

Earlier quoted context omitted.

Well no. If you create a machine that produces output indistinguishable from the output of things we "know" can "reason" aka "humans". Then I would call that reasoning. If the output has a low probability of occuring by random chance then it must be reason. >For example, if you prove that the reasoning is somehow embedded as a spatial in-network set of dimensions rather than in-time, wouldn't that be literally equiva…

A side effect of this is that a zip.exe that unzips a zip into a book that contains text indistiguishable from the output of a human must reason too. From what I can see, you’re only massaging semantics. That is uninteresting.

No. I clearly said it must output novel things that aren’t part of the input.

In your example the book is the training data or aka the input.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#592

Earlier quoted context omitted.

You'll have to drop a bit of rigor here. I ask the question, what is 2 * 2, which is an obviously loaded question that's pattern matched to death. The LLM can answer "4" or "The answer is 4" of "looks like the answer is 4" All valid answers but all the same. We count all 3 of those answers as just 4 out of the set of numbers. But we have to use our own language faculties to cut through the noise of the language itsel…

> I ask the question, what is 2 * 2, which is an obviously loaded question that's pattern matched to death. Yeah, that was my point. Small codomain -> easy to validate. Large codomain -> open to interpretation. You implied that to prove reasoning, pick a prompt with a large codomain and if the LLM answers with accurate precision, then viola, reasoning. So my question was, can you give an example of a prompt with a hi…

Pick a prompt with a wide codomain but a single answer. That’s reasoning if it can get the answer right.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#593

Earlier quoted context omitted.

I wrote a post below about how AI hallucinated a whole regulation that didn’t exist which has been flagged for some reason. I have colleagues who have had arguments with clients who have asked AI questions about planning law and been given bullshit which they then insist is true and they can’t understand why thier architects won’t submit the appeal that they’re asking for. I think we’re in an era where any text, true…

It was flagged because it’s a copy paste of the same comment you made ten days ago.

Oh right, sorry. Should I have linked to it? It seemed pretty relevant to this discussion and most of it was quoting something so I reused a lot of it. Didn't realise that was taboo.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#594

Earlier quoted context omitted.

> I ask the question, what is 2 * 2, which is an obviously loaded question that's pattern matched to death. Yeah, that was my point. Small codomain -> easy to validate. Large codomain -> open to interpretation. You implied that to prove reasoning, pick a prompt with a large codomain and if the LLM answers with accurate precision, then viola, reasoning. So my question was, can you give an example of a prompt with a hi…

Pick a prompt with a wide codomain but a single answer. That’s reasoning if it can get the answer right.

Your original claim was that an LLM can reason. And you say it can be proven by picking one of these prompts with a large codomain that has a precise answer which requires reason. If an LLM can come to a specific answer out of a huge codomain, and that answer requires reason, you claim that proves reasoning. Do I have that right?

So my question is, and has been these three replies: Can you give any example of one of these prompts?

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#595

Earlier quoted context omitted.

Then I'll come up with a prompt such that the answer can only be arrived at via reasoning. I only have to demonstrate this once to prove LLMs CAN reason.

Go ahead then.

This is the count donut problem. Given a grid of 1s and 0s where 1 represents land and 0 represents water find the amount of donuts. A donut is an island with at least one hole in it. Two grid cells that are diagonal or adjacent form a barrier that water cannot cross. Count the amount of donuts in the grid.

This is a unique problem I came up with. It’s a variation on counting islands. There are actually two correct answers that are straightforward. Other answers may exist but are generally not straightforward and often wrong. One answer is mathematical the other is a leetcode style solution.

Try to solve this yourself before using ai to get a feel for how hard it is. The solution should be extremely straightforward. It’s also fun to think about. When you try to think of a solution you will invariably come up with a bunch of possible solutions that are wrong which is a strong indicator of how large the range of possible answers are. Few answers are correct but many look correct.

I give this test to candidates and I never expect the candidate to solve it because it’s one of the few algorithms that requires actual reasoning and actual creativity as I came up with it so no variation of it really exists anywhere else. You can’t pattern match for it. Out of like 50 candidates you probably get one person able to solve it in less than an hour.

It’s unlikely most people on hn will be able to solve it. If you do solve it don’t post the answer as it will become training data for the next iteration of the LLM.

I gave the prompt to o3. It solved. It generated code as well which I was too lazy to verify but it solved it correctly in the description of the algorithm involved.

There is also a 3D version of this problem where the grid is 3D. It changes the entire problem if a donut is in 3D space. It is harder and I have only found one possible solution for it. I have not tried it on an LLM.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#596

Earlier quoted context omitted.

They can fail at reasoning. But they can demonstrably succeed to. So the the statement that they CAN reason is demonstrably true. Ok if given a prompt where the solution can only be arrived at by reasoning and the LLM gets to the solution for that single prompt, then how can you say it can't reason?

Given your set of theoreticals then I would concede, yes the model is reasoning. At that point, though, the world would probably be far more concerned with your finding of a question that can only be met via reasoning and would be uninfluenced or paralleled by any empirical phenomenon including written knowledge as a medium of transference. The core issue I see here is you being able to prove that the model is actual…

https://news.ycombinator.com/item?id=43011839

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#597

Earlier quoted context omitted.

You'll have to drop a bit of rigor here. I ask the question, what is 2 * 2, which is an obviously loaded question that's pattern matched to death. The LLM can answer "4" or "The answer is 4" of "looks like the answer is 4" All valid answers but all the same. We count all 3 of those answers as just 4 out of the set of numbers. But we have to use our own language faculties to cut through the noise of the language itsel…

> I ask the question, what is 2 * 2, which is an obviously loaded question that's pattern matched to death. Yeah, that was my point. Small codomain -> easy to validate. Large codomain -> open to interpretation. You implied that to prove reasoning, pick a prompt with a large codomain and if the LLM answers with accurate precision, then viola, reasoning. So my question was, can you give an example of a prompt with a hi…

[deleted]

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#598
post #588

Earlier quoted context omitted.

A side effect of this is that a zip.exe that unzips a zip into a book that contains text indistiguishable from the output of a human must reason too. From what I can see, you’re only massaging semantics. That is uninteresting.

No. I clearly said it must output novel things that aren’t part of the input. In your example the book is the training data or aka the input.

[deleted]

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#599
post #588

Earlier quoted context omitted.

A side effect of this is that a zip.exe that unzips a zip into a book that contains text indistiguishable from the output of a human must reason too. From what I can see, you’re only massaging semantics. That is uninteresting.

No. I clearly said it must output novel things that aren’t part of the input. In your example the book is the training data or aka the input.

Agreed, that was a wrong example.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#600

Earlier quoted context omitted.

Pick a prompt with a wide codomain but a single answer. That’s reasoning if it can get the answer right.

Your original claim was that an LLM can reason. And you say it can be proven by picking one of these prompts with a large codomain that has a precise answer which requires reason. If an LLM can come to a specific answer out of a huge codomain, and that answer requires reason, you claim that proves reasoning. Do I have that right? So my question is, and has been these three replies: Can you give any example of one of…

https://news.ycombinator.com/item?id=43011839
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