4 ms·

I can’t wait until I tell GPT-5 “I have this idea I want to try, read this book and tell me there’s anything relevant there to make it work better”.

by p1esk 3y ago

deleted 3y ago
[deleted]
ralphist 3y ago
I've never heard of an LLM making up a new idea. Shouldn't this only work if your thing has already been tried before?
- p1esk 3y ago
  In my example I already have an idea, but I’m not sure how good or novel it is, or perhaps I tried it and it didn’t work but I don’t understand why.
- westoncb 3y ago
  How people define "new" varies a lot in this context. I've spent a lot of time talking with ChatGPT exploring interdisciplinary ideas and while I think it frequently says things that qualify as new, I've only run into one situation where I was trying to find some way of doing something technical and it just invented something non-trivially original to handle it: https://x.com/Westoncb/status/1763733064478335326?s=20 https://x.com/Westoncb/status/1763733064478335326?s=20
- bonoboTP 3y ago
  Have you heard of a person making up a new idea? Can you definitely state that it's not just a combination of a few previous ideas?
  - ralphist 3y ago
    The brain is able to make better associations than an LLM. From what I’ve seen, LLMs always stay on topic, even when that means outputting only platitudes. A human can go past that and try different approaches to a question. It can also take part of a problem, choose a lead at random, think through the results, and step back if that doesn’t work. A forward pass in an LLM doesn’t do that, yet. Basically it seems to me that LLMs may have part of what makes us good at inventing ideas, but they’re missing part of that process.
    - bonoboTP 3y ago
      I don't think this is a fundamental limitation. If the LLM is trained (through RLHF or something else) to go on a chain-of-internal-monologue (which may take arbitrarily long) to figure out what it will answer, then this kind of adaptive amount of compute can be achieved.
      - ralphist 3y ago
        Sure, but then how good are Transformers at making connections between distant concepts? The brain seems way better at this, and it doesn't feel like conversation.
        bonoboTP 3y ago
        One of the big hopes of this "train on all text in existence" (including academic works and everything else) is that this enables connections that no human could make before, since there probably a total of zero people who are specialists in medieval history and molecular biology and also music theory and quantum computational complexity and what have you. However current models seem not to do much of this in my experience. Typically they remain in the specialist "persona" that rarely brings up connections that would be known by other human specialists (whom it would otherwise have no issue impersonating). But I'd imagine this can also be improved somehow. Think about how often narrowly specialized academics reinvent a bad version of something that already has an elaborately understood theory in another field. (a meme example is when a medical researcher working on insulin metabolism reinvented the trapezoid rule for integration - but of course many more mundane cases happen every day). It would be a great opportunity to avoid this by checking with an LLM. Arguably if that researcher fed his idea to an LLM as it exists today, the LLM would have recognized that this is just the trapezoid rule. In fact I also use it for such "sounding board" where it gives me good googleable terms like, "what you're looking for is called an XYZ".
hef19898 3y ago
Ypur idea would become even better so if you read the book yourself... You might even learn something new along the way.
- dkarras 3y ago
  we are optimizing for time here, not learning. I'm gonna die anyways and anything I learn will be dust. If I just need the info to make something work, spending months (of which I have limited number of) to see if something has something useful in it for me vs. spending and afternoon to probe it to get most of the benefits is a no-brainer.
  - makapuf 3y ago
    Maybe, but dont expect otherwise from a book explicitly named "Learning Theory from First principles", not "learn large language models in 21 days".
erbdex 3y ago
I am realising that passing context for $this is the tricky part as- 1. It is very difficult for me to tell you about my context as a user within low dimension variables. 2. I do not understand my situation in the universe to be able to tell AI. 3. I dont have a vocabulary with AI. Internet i feel aced this with shared HTTP protocol to consistently share agreed upon state. For ex within Uber I am a very narrow request response universe with.. POST phone, car, gps(a,b,c,d), now, payment. But as a student wanting to learn algorithms how do I pass that I'm $age $internet-type from $place and prefer graphical explanations of algorithms, have tried but gotten scared of that thick book and these $milestones-cs50, know $python upto $proficiency(which again is a fractal variable with research papers on how to define for learning). Similarly how do I help you understand what stage my startup idea is beyond low traction, but want to know have $networks/(VC, devs, sales) APIs, have $these successful partnerships with such evidence $attendance, $sales. Who should I speak to? Could you pls write the needful in mails and engage in partnership with other bots under $budget. Even in the real world this vocabulary is in smaller pockets as our contexts are too different. 4. Learning assumes knowledge exists as a global forever variable in a wider than we understand universe. $meteor being a non maskable interrupt to the power supply at unicorn temperatures in a decade. Similarly one time trends in disposable $companies that $ecosystem uses to learn. I'm in a desert village with with absent electricity might mean those machines never reach me and perhaps most people don't have a basic phone in the world to be able to share state. Their local power mafia politics and absent governance might mean the pdf AI recommends i read might or might not help. I don't know how this will evolve but to think of the possibilities has been so interesting. It's like computers can talk to us easily and they're such smart babies on day 1 and "folks we aren't able to put right, enough, cheap data in" is perhaps the real bottleneck to how much usefulness we are being able to uncover.
- p1esk 3y ago
  What you described is what some of the recent startups are working on: https://www.rewind.ai https://www.rewind.ai (I’m not associated with them). To me it seems like a rather trivial problem to solve, compared to creating an LLM in the first place.

I can’t wait until I tell GPT-5 “I have this idea I want to try, read this book and tell me there’s anything relevant there to make it work better”.

by p1esk 3y ago

I can’t wait until I tell GPT-5 “I have this idea I want to try, read this book and tell me there’s anything relevant there to make it work better”.

deleted 3y ago

[deleted]

ralphist 3y ago

I've never heard of an LLM making up a new idea. Shouldn't this only work if your thing has already been tried before?

p1esk 3y ago
In my example I already have an idea, but I’m not sure how good or novel it is, or perhaps I tried it and it didn’t work but I don’t understand why.
westoncb 3y ago
How people define "new" varies a lot in this context. I've spent a lot of time talking with ChatGPT exploring interdisciplinary ideas and while I think it frequently says things that qualify as new, I've only run into one situation where I was trying to find some way of doing something technical and it just invented something non-trivially original to handle it: https://x.com/Westoncb/status/1763733064478335326?s=20 https://x.com/Westoncb/status/1763733064478335326?s=20
bonoboTP 3y ago
Have you heard of a person making up a new idea? Can you definitely state that it's not just a combination of a few previous ideas?
- ralphist 3y ago
  The brain is able to make better associations than an LLM. From what I’ve seen, LLMs always stay on topic, even when that means outputting only platitudes. A human can go past that and try different approaches to a question. It can also take part of a problem, choose a lead at random, think through the results, and step back if that doesn’t work. A forward pass in an LLM doesn’t do that, yet. Basically it seems to me that LLMs may have part of what makes us good at inventing ideas, but they’re missing part of that process.
  - bonoboTP 3y ago
    I don't think this is a fundamental limitation. If the LLM is trained (through RLHF or something else) to go on a chain-of-internal-monologue (which may take arbitrarily long) to figure out what it will answer, then this kind of adaptive amount of compute can be achieved.
    - ralphist 3y ago
      Sure, but then how good are Transformers at making connections between distant concepts? The brain seems way better at this, and it doesn't feel like conversation.
      - bonoboTP 3y ago
        One of the big hopes of this "train on all text in existence" (including academic works and everything else) is that this enables connections that no human could make before, since there probably a total of zero people who are specialists in medieval history and molecular biology and also music theory and quantum computational complexity and what have you. However current models seem not to do much of this in my experience. Typically they remain in the specialist "persona" that rarely brings up connections that would be known by other human specialists (whom it would otherwise have no issue impersonating). But I'd imagine this can also be improved somehow. Think about how often narrowly specialized academics reinvent a bad version of something that already has an elaborately understood theory in another field. (a meme example is when a medical researcher working on insulin metabolism reinvented the trapezoid rule for integration - but of course many more mundane cases happen every day). It would be a great opportunity to avoid this by checking with an LLM. Arguably if that researcher fed his idea to an LLM as it exists today, the LLM would have recognized that this is just the trapezoid rule. In fact I also use it for such "sounding board" where it gives me good googleable terms like, "what you're looking for is called an XYZ".

hef19898 3y ago

Ypur idea would become even better so if you read the book yourself... You might even learn something new along the way.

dkarras 3y ago
we are optimizing for time here, not learning. I'm gonna die anyways and anything I learn will be dust. If I just need the info to make something work, spending months (of which I have limited number of) to see if something has something useful in it for me vs. spending and afternoon to probe it to get most of the benefits is a no-brainer.
- makapuf 3y ago
  Maybe, but dont expect otherwise from a book explicitly named "Learning Theory from First principles", not "learn large language models in 21 days".

erbdex 3y ago

I am realising that passing context for $this is the tricky part as- 1. It is very difficult for me to tell you about my context as a user within low dimension variables. 2. I do not understand my situation in the universe to be able to tell AI. 3. I dont have a vocabulary with AI. Internet i feel aced this with shared HTTP protocol to consistently share agreed upon state. For ex within Uber I am a very narrow request response universe with.. POST phone, car, gps(a,b,c,d), now, payment. But as a student wanting to learn algorithms how do I pass that I'm $age $internet-type from $place and prefer graphical explanations of algorithms, have tried but gotten scared of that thick book and these $milestones-cs50, know $python upto $proficiency(which again is a fractal variable with research papers on how to define for learning). Similarly how do I help you understand what stage my startup idea is beyond low traction, but want to know have $networks/(VC, devs, sales) APIs, have $these successful partnerships with such evidence $attendance, $sales. Who should I speak to? Could you pls write the needful in mails and engage in partnership with other bots under $budget. Even in the real world this vocabulary is in smaller pockets as our contexts are too different. 4. Learning assumes knowledge exists as a global forever variable in a wider than we understand universe. $meteor being a non maskable interrupt to the power supply at unicorn temperatures in a decade. Similarly one time trends in disposable $companies that $ecosystem uses to learn. I'm in a desert village with with absent electricity might mean those machines never reach me and perhaps most people don't have a basic phone in the world to be able to share state. Their local power mafia politics and absent governance might mean the pdf AI recommends i read might or might not help. I don't know how this will evolve but to think of the possibilities has been so interesting. It's like computers can talk to us easily and they're such smart babies on day 1 and "folks we aren't able to put right, enough, cheap data in" is perhaps the real bottleneck to how much usefulness we are being able to uncover.

p1esk 3y ago
What you described is what some of the recent startups are working on: https://www.rewind.ai https://www.rewind.ai (I’m not associated with them). To me it seems like a rather trivial problem to solve, compared to creating an LLM in the first place.