5 ms·

Can you quantify the difference between cognitive reasoning and statistical optimization?

mg74 3y ago
Statistical optimization is a known process; one where we understand every step, and can therefore instruct machines on how to perform it. Cognitive reasoning is still today not understood (in the Von Neumann sense) by anyone.
- orbital-decay 3y ago
  That's my point. How can you compare the two if one is not even well-defined, let alone understood?
  - deleted 3y ago
    [deleted]
- famouswaffles 3y ago
  Only the training process of statistical optimization can possibly be described as well understood, and not very much.
  - mg74 3y ago
    The steps are understood well enough to instruct machines to do it, even if the output is to complex for us to comprehend it completely. Not so with thought, intelligence, consciousness, etc.
    - famouswaffles 3y ago
      again, no they aren't. In ML, nobody instructs machines to do anything other than train and find those steps themselves. We have no clue what steps GPT takes when it solves problems. For a straightforward example, when GPT-4 adds, what algorithm does it perform ? I have no clue, you have no clue and neither does anyone at open ai. What we do is instruct machines to train. What they get out of training, we have very little idea.
chx 3y ago
Sure thing. People really don't learn the history of AI any more apparently or this question wouldn't come up all the time. There is basically any number of questions you can ask a two year old human who have never encountered that question nor anything even remotely similar to it and yet they can answer without fail. Meanwhile absolutely no AI can answer these unless the specific question / the rules underlying the questions were previously fed into it. The textbook example is "If Susan goes shopping will her head go with her?" Of course, since this specific question is literally a textbook one, you can't fool an LLM with it but it's easy to come up with brand new ones. In the early 1980s this stopped Douglas Lenat who has worked very successfully on discovery systems and made him turn to assembling these facts and rules into CyC.
- famouswaffles 3y ago
  You've not quantified anything, nor have you even provided any valid example of these so abundant mystery questions. If it's so easy, come up with one and show us.
  - chx 3y ago
    If I do then it'll get gobbled up by fake AI and I won't make up one every time ignorant people who can't open an AI textbook demand one from me. Apropos: https://existentialcomics.com/comic/289 https://existentialcomics.com/comic/289 You could use that comics to generate a few of these questions.
    - famouswaffles 3y ago
      What a ridiculous excuse. Leaving aside the fact the chances of your reply being in the training of the next GPT near 0, it certainly won't happen anywhere near fast enough to disprove your point to anyone reading this today. So you have nothing. Not that i'm surprised.
      - chx 3y ago
        No, the automated plagiarism machines have nothing and I am not obligated to cure your ignorance. Let's continue the conversation after you read an AI textbook, shall we?

mg74 3y ago

Statistical optimization is a known process; one where we understand every step, and can therefore instruct machines on how to perform it. Cognitive reasoning is still today not understood (in the Von Neumann sense) by anyone.

orbital-decay 3y ago
That's my point. How can you compare the two if one is not even well-defined, let alone understood?
- deleted 3y ago
  [deleted]
famouswaffles 3y ago
Only the training process of statistical optimization can possibly be described as well understood, and not very much.
- mg74 3y ago
  The steps are understood well enough to instruct machines to do it, even if the output is to complex for us to comprehend it completely. Not so with thought, intelligence, consciousness, etc.
  - famouswaffles 3y ago
    again, no they aren't. In ML, nobody instructs machines to do anything other than train and find those steps themselves. We have no clue what steps GPT takes when it solves problems. For a straightforward example, when GPT-4 adds, what algorithm does it perform ? I have no clue, you have no clue and neither does anyone at open ai. What we do is instruct machines to train. What they get out of training, we have very little idea.

chx 3y ago

Sure thing. People really don't learn the history of AI any more apparently or this question wouldn't come up all the time. There is basically any number of questions you can ask a two year old human who have never encountered that question nor anything even remotely similar to it and yet they can answer without fail. Meanwhile absolutely no AI can answer these unless the specific question / the rules underlying the questions were previously fed into it. The textbook example is "If Susan goes shopping will her head go with her?" Of course, since this specific question is literally a textbook one, you can't fool an LLM with it but it's easy to come up with brand new ones. In the early 1980s this stopped Douglas Lenat who has worked very successfully on discovery systems and made him turn to assembling these facts and rules into CyC.

famouswaffles 3y ago
You've not quantified anything, nor have you even provided any valid example of these so abundant mystery questions. If it's so easy, come up with one and show us.
- chx 3y ago
  If I do then it'll get gobbled up by fake AI and I won't make up one every time ignorant people who can't open an AI textbook demand one from me. Apropos: https://existentialcomics.com/comic/289 https://existentialcomics.com/comic/289 You could use that comics to generate a few of these questions.
  - famouswaffles 3y ago
    What a ridiculous excuse. Leaving aside the fact the chances of your reply being in the training of the next GPT near 0, it certainly won't happen anywhere near fast enough to disprove your point to anyone reading this today. So you have nothing. Not that i'm surprised.
    - chx 3y ago
      No, the automated plagiarism machines have nothing and I am not obligated to cure your ignorance. Let's continue the conversation after you read an AI textbook, shall we?