4 ms·

Isn't the "Chat" part of ChatGPT already doing something close to this? I mean the clarification comes from the end-user, not from the AI, but with enough of th

by Jupe 4y ago

Isn't the "Chat" part of ChatGPT already doing something close to this? I mean the clarification comes from the end-user, not from the AI, but with enough of this stuff to feed upon, perhaps AIs could "get there" at some point? For example, this guy was able to do some amazing stuff with ChatGPT. He even managed to get a (mostly working) GPU-accelerated version of his little sample "race" problem. See: https://youtu.be/pspsSn_nGzo https://youtu.be/pspsSn_nGzo

qsort 4y ago
> Isn't the "Chat" part of ChatGPT already doing something close to this? No, the amount of handholding you have to do to get it to work effectively presumes you already know how to solve the problem in the first place. The best way to use it is the opposite everyone is busy selling: as a linter of sorts that puts blue squiggles below my code saying stuff like "hey stupid human, you're leaking memory here", or even "you're using snake case, the project uses camel case, fix that". That would actually lower my cognitive load and be an effective copilot.
- anon7725 4y ago
  I agree with your point about how to best use it today. We have seen that each new model generation both improves the prior tasks and unlocks new ones through emergent behavior. That’s the fascinating/scary part of this development. And yes, it’s “just” a language model. It’s “just” predicting next token given training + context. We don’t really understand why it’s working and it’s evolving non-linearly. I asked GPT-4 to give me an SVG map of my town. I then asked it to put dots on some local landmarks. The map was toddler level, but the landmarks were relatively accurate in terms of their relationship to each other and the blob that it drew. So this is a language model that has some emergent notion of space in its code generation abilities.
- Jupe 4y ago
  Fair enough - assuming steady state, but the acceleration is the curve I'm most curious about. The point I was alluding to above was that the prompts themselves will be recursively mined over time. Eventually, except for truly novel problems, the AI interpretation of the prompts will become more along the lines of "that's what I wanted". Some things to think about: What happens when an entire company's slack history is mined in this fashion? Or email history? Or GIT commit history, with corresponding links to Jira tickets? Or the corporate wiki? There are, I'd guess, hundreds of thousands to millions of project charter documents to be mined; all locked behind an "intranet" - but at some point, businesses will be motivated to, at the least, explore the "what if" implications. Given enough data to feed upon, and some additional code/logic/extensions to the current state of the art, I think every knowledge worker should consider the impact of this technology. I'm not advocating for it (to be honest, it scares the hell out of me) - but this is where I see the overall trend heading.
  - qsort 4y ago
    This is the doomsday scenario again, though. In a world where we have the technology to go from two lines of prompt in a textbox to a complete app, no questions asked, then the same technology can run the entire company. It's kind of hard to believe transformers models are capable of this, given we are already starting to see diminishing returns, but if that's what you believe they are, then you believe they can effectively do anything. It's the old concept of AI-complete. If you need to formally specify behavior, at any point in the pipeline, then we're back to square one: you just invented a programming language, and a very bad one at that. This remains true for any version of a language model, even an hypothetical future LLM that has "solved" natural language. I would not rather write natural language than formal language given the chance.
    - Jupe 4y ago
      > If you need to formally specify behavior, at any point in the pipeline, then we're back to square one: you just invented a programming language, and a very bad one at that. But what if the "programming language" is not a general-purpose language, but a context/business domain specific language? One that is trained on the core business at hand? What if that "language" had access to all the same vocabulary, project history (both successful and unsuccessful), industry regulations, code bases from previous (perhaps similar) solutions, QC reports, etc.? What if the "business savvy" consumer of this AI can phrase things succinctly in a fashion that the AI can translate into working code? I don't see it as a stretch "down the road." Is it possible today? Probably not. Is it possible in 5-10 years time, I definitely think so.

by Jupe 4y ago

qsort 4y ago

> Isn't the "Chat" part of ChatGPT already doing something close to this? No, the amount of handholding you have to do to get it to work effectively presumes you already know how to solve the problem in the first place. The best way to use it is the opposite everyone is busy selling: as a linter of sorts that puts blue squiggles below my code saying stuff like "hey stupid human, you're leaking memory here", or even "you're using snake case, the project uses camel case, fix that". That would actually lower my cognitive load and be an effective copilot.

anon7725 4y ago
I agree with your point about how to best use it today. We have seen that each new model generation both improves the prior tasks and unlocks new ones through emergent behavior. That’s the fascinating/scary part of this development. And yes, it’s “just” a language model. It’s “just” predicting next token given training + context. We don’t really understand why it’s working and it’s evolving non-linearly. I asked GPT-4 to give me an SVG map of my town. I then asked it to put dots on some local landmarks. The map was toddler level, but the landmarks were relatively accurate in terms of their relationship to each other and the blob that it drew. So this is a language model that has some emergent notion of space in its code generation abilities.
Jupe 4y ago
Fair enough - assuming steady state, but the acceleration is the curve I'm most curious about. The point I was alluding to above was that the prompts themselves will be recursively mined over time. Eventually, except for truly novel problems, the AI interpretation of the prompts will become more along the lines of "that's what I wanted". Some things to think about: What happens when an entire company's slack history is mined in this fashion? Or email history? Or GIT commit history, with corresponding links to Jira tickets? Or the corporate wiki? There are, I'd guess, hundreds of thousands to millions of project charter documents to be mined; all locked behind an "intranet" - but at some point, businesses will be motivated to, at the least, explore the "what if" implications. Given enough data to feed upon, and some additional code/logic/extensions to the current state of the art, I think every knowledge worker should consider the impact of this technology. I'm not advocating for it (to be honest, it scares the hell out of me) - but this is where I see the overall trend heading.
- qsort 4y ago
  This is the doomsday scenario again, though. In a world where we have the technology to go from two lines of prompt in a textbox to a complete app, no questions asked, then the same technology can run the entire company. It's kind of hard to believe transformers models are capable of this, given we are already starting to see diminishing returns, but if that's what you believe they are, then you believe they can effectively do anything. It's the old concept of AI-complete. If you need to formally specify behavior, at any point in the pipeline, then we're back to square one: you just invented a programming language, and a very bad one at that. This remains true for any version of a language model, even an hypothetical future LLM that has "solved" natural language. I would not rather write natural language than formal language given the chance.
  - Jupe 4y ago
    > If you need to formally specify behavior, at any point in the pipeline, then we're back to square one: you just invented a programming language, and a very bad one at that. But what if the "programming language" is not a general-purpose language, but a context/business domain specific language? One that is trained on the core business at hand? What if that "language" had access to all the same vocabulary, project history (both successful and unsuccessful), industry regulations, code bases from previous (perhaps similar) solutions, QC reports, etc.? What if the "business savvy" consumer of this AI can phrase things succinctly in a fashion that the AI can translate into working code? I don't see it as a stretch "down the road." Is it possible today? Probably not. Is it possible in 5-10 years time, I definitely think so.