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For somebody who likes building things and has many side projects Claude and ChatGPT have been huge productivity multipliers. For example, I’m designing and 3D

For somebody who likes building things and has many side projects Claude and ChatGPT have been huge productivity multipliers. For example, I’m designing and 3D printing custom LED diffuser channels from TPU filament. My first attempt was terrible, because I didn’t have an intuition for how light propagates through a material. After a bit of chatting with ChatGPT, I had an understanding and some direction of where to go. To actually approach the problem properly I decided to run some Monte Carlo light transport simulations against an .obj of my diffuser exported from Fusion 360. The problem was, the software I’m using only supports directional lights with uniform intensity, while the LEDs I’m using have a graph in their datasheet showing light intensity per degree away from orthogonal to the LED SMD component. I copy pasted the directional light implementation from the light transport library as well as the light-intensity-by-degree chat from the LED datasheet and asked Claude to write code to write a light source that samples photons from a disc of size x with the probability of emission by angle governed by the chat from the datasheet. A few iterations later and I had a working simulation which I then verified back against the datasheet chart. Without AI this would have been a long long process of brushing up on probability, vector math, manually transcribing the chart. Instead in like 10 minutes I had working code and the light intensity of the simulation against my mesh matched what I was seeing in real life with a 3D printed part.

Bjorkbat 2y ago
As much of a curmudgeon as I am on AI I do sincerely believe that one area that it's effective at is going from absolute beginner to decent understanding on something completely new and foreign. Recently I've been somewhat curious about Skia, the graphics library that Google uses. A while back I fielded a few questions about Skia to Claude. Nothing crazy, just questions on how to draw a few primitive shapes, but I felt it would have taken some effort to find the answers on my own. And I must say I was pretty satisfied with what it gave me back.
- lxgr 2y ago
  It is indeed incredibly useful for getting from zero to an undergrad level of understanding of most subjects. In my personal experience, it's also been largely useless beyond that. That's not to diminish the potential impact of having an almost-free grad-student-level tutor available 24/7! Even if AI stops improving here, this alone will have a huge impact on future science (there being more trained people around capable of helping solve hard problems and all). But we're also definitely some way away from AIs doing their own research, and I'm not sure if scaling the current architecture alone will get us there.
  - throwup238 2y ago
    > But we're also definitely some way away from AIs doing their own research, and I'm not sure if scaling the current architecture alone will get us there. Hot take: I think it's fairly obvious the current architecture is not enough BUT it's already or almost at the point where it can be the backbone for a proper AGI/ASI system. Linking human communication and some light language-based reasoning to other AI models that handle the rest of the world model and reasoning, using special tokens to coordinate them. I think it's just really early and most of this stuff hasn't even been attempted yet. There's tools/function calling which is kind of a proof of concept, and a lot of academic labs researching transformers applied to other fields like robotic control and learning, but no one's put it all together yet.
    - lxgr 2y ago
      What's critically missing in the current architectures, in my view, is models that are able to "keep learning" (in a way beyond just providing a summary of the previous conversation, which grants surface-level insights at most). If we keep the current iteration cycle (retrain an LLM every couple of months, incorporating the current "status quo", including all of its previous conversations), we might get somewhat interesting results, but even the least motivated grad student has an iteration cycle orders of magnitude faster than that.
- devjab 2y ago
  As an expert who uses LLMs daily on my field of expertise I’m not looking forward to dealing with works created by people who were taught by them. They are wrong far too often to be used for any sort of learning in my opinion. You can feed them a book and they’ll give you answers which don’t exist in any of the written material, and that’s the good part of them. Simply asking a LLM about things will give you answers that are hopelessly wrong and unless you’re an expert you won’t know it. Which isn’t necessarily worse than learning things from search engines or YouTube. I recently had ChatGPT give me a recipe for bread which was certainly better than the top 10 results on Google except for one things. The recipe listed 3x the amount of salt which you should ever reasonably use. I asked it a few other times, I even asked it for my friend Tommy’s recipe and all those answers were perfect. It obviously doesn’t know Tommy but it pretended to know and just gave me a pretty basic recipe. Salt is a harmless error. Most people would know not to use that much salt, and even if they did, it wouldn’t harm them (much). But imagine if you had used it for something electric or chemistry.
joshmarlow 2y ago
This dovetails with something I've been thinking lately; the foundation of civilization is standing on the shoulders of giants (ie, I might make small contributions but I didn't invent compilers, CPUs, mining, agriculture, etc). Progress in large part is figuring out better ways of doing that (language, written language, printing press, internet access, etc). When you look at things that way - LLMs start to seem deeply ground-breaking (assuming we work out the confabulation kinks). EDIT: fixed grammar/typos. Maybe I should have had an LLM proof-read this...
- visarga 2y ago
  > the foundation of civilization is standing on the shoulders of giants. Progress in large part is figuring out better way of doing that It's so much easier to imitate than to invent something truly novel and useful. Let's do a bit of napkin math. A human lifetime is about 500M words. GPT-4 used up about 30,000 human lifetimes of language. But cultural evolution took 200K years and 120B people to get here, about 4 million times the size of GPT-4's training set. That is of course hand wavy, but it shows progress is a million times harder than imitation. We really are standing on the shoulders of giants, or a very long chain of people. If we forgot all the knowledge preserved by language it would take us the same effort to recover as the first time around. I think all progress comes from search - search for experience (data), and search for understanding (data compression). This feeds on itself, but is coupled with the search space - the real world. And the world is not eager to tell us all its secrets. AI will only advance as fast as it can search, it's not a matter of pure scaling of computation, we need to scale interaction and validation as well.
  - joshmarlow 2y ago
    > AI will only advance as fast as it can search, it's not a matter of pure scaling of computation, we need to scale interaction and validation as well. I think Kevin Kelly made a similar point in explaining his skepticism around intelligence explosions; even if we build something much much smarter than us, the thing will still need to do experiments to fine-tune it's (super-human nuanced) understanding of physics/biology/whatever - and the clock-cycle of external reality isn't speeding up like our computation is. I think something smarter than us could design better/more informative experiments than we could to gather information about the world. That being said, I think his/your point is insightful.
swagasaurus-rex 2y ago
While I agree these LLMs are getting pretty good, I think google shares fault for making it so hard to find information on the internet. You have to scroll past 4 sponsored links to get to their algorithm results, which themselves have been gamed by SEO and content farms. As far as I can tell, google has no plans to limit AI generated botfarm search results. LLMs now seem to get you an answer more quickly than google. LLMs don’t cite their sources, so IMO they can’t fully replace google for me yet.
- piombisallow 2y ago
  I think it's not necessarily Google's fault, it's an arms race and they're losing it.
  - hnthrow562 2y ago
    AI race perspective by sama. https://www.washingtonpost.com/opinions/2024/07/25/sam-altman-ai-democracy-authoritarianism-future/ https://www.washingtonpost.com/opinions/2024/07/25/sam-altma...
- ctoth 2y ago
  I don't really understand what this comment about Google has to do with OP's comment. Is this something that even old Google could have done pre-SEOification? Would Google have been able to read the datasheet? Or just point OP at what they already said they were happy to avoid? > Without AI this would have been a long long process of brushing up on probability, vector math, manually transcribing the chart. Sometimes, it feels like stochastic parrots are seizing on a few words from the comment to pattern match it to the closest typical refutation and failing bigly.
  - anthonypasq 2y ago
    tremendously bigly
    - ctoth 2y ago
deleted 2y ago
[deleted]
namaria 2y ago
Congrats on learning way less about the systems and tools you're interacting with?

Bjorkbat 2y ago

As much of a curmudgeon as I am on AI I do sincerely believe that one area that it's effective at is going from absolute beginner to decent understanding on something completely new and foreign. Recently I've been somewhat curious about Skia, the graphics library that Google uses. A while back I fielded a few questions about Skia to Claude. Nothing crazy, just questions on how to draw a few primitive shapes, but I felt it would have taken some effort to find the answers on my own. And I must say I was pretty satisfied with what it gave me back.

lxgr 2y ago
It is indeed incredibly useful for getting from zero to an undergrad level of understanding of most subjects. In my personal experience, it's also been largely useless beyond that. That's not to diminish the potential impact of having an almost-free grad-student-level tutor available 24/7! Even if AI stops improving here, this alone will have a huge impact on future science (there being more trained people around capable of helping solve hard problems and all). But we're also definitely some way away from AIs doing their own research, and I'm not sure if scaling the current architecture alone will get us there.
- throwup238 2y ago
  > But we're also definitely some way away from AIs doing their own research, and I'm not sure if scaling the current architecture alone will get us there. Hot take: I think it's fairly obvious the current architecture is not enough BUT it's already or almost at the point where it can be the backbone for a proper AGI/ASI system. Linking human communication and some light language-based reasoning to other AI models that handle the rest of the world model and reasoning, using special tokens to coordinate them. I think it's just really early and most of this stuff hasn't even been attempted yet. There's tools/function calling which is kind of a proof of concept, and a lot of academic labs researching transformers applied to other fields like robotic control and learning, but no one's put it all together yet.
  - lxgr 2y ago
    What's critically missing in the current architectures, in my view, is models that are able to "keep learning" (in a way beyond just providing a summary of the previous conversation, which grants surface-level insights at most). If we keep the current iteration cycle (retrain an LLM every couple of months, incorporating the current "status quo", including all of its previous conversations), we might get somewhat interesting results, but even the least motivated grad student has an iteration cycle orders of magnitude faster than that.
devjab 2y ago
As an expert who uses LLMs daily on my field of expertise I’m not looking forward to dealing with works created by people who were taught by them. They are wrong far too often to be used for any sort of learning in my opinion. You can feed them a book and they’ll give you answers which don’t exist in any of the written material, and that’s the good part of them. Simply asking a LLM about things will give you answers that are hopelessly wrong and unless you’re an expert you won’t know it. Which isn’t necessarily worse than learning things from search engines or YouTube. I recently had ChatGPT give me a recipe for bread which was certainly better than the top 10 results on Google except for one things. The recipe listed 3x the amount of salt which you should ever reasonably use. I asked it a few other times, I even asked it for my friend Tommy’s recipe and all those answers were perfect. It obviously doesn’t know Tommy but it pretended to know and just gave me a pretty basic recipe. Salt is a harmless error. Most people would know not to use that much salt, and even if they did, it wouldn’t harm them (much). But imagine if you had used it for something electric or chemistry.

joshmarlow 2y ago

This dovetails with something I've been thinking lately; the foundation of civilization is standing on the shoulders of giants (ie, I might make small contributions but I didn't invent compilers, CPUs, mining, agriculture, etc). Progress in large part is figuring out better ways of doing that (language, written language, printing press, internet access, etc). When you look at things that way - LLMs start to seem deeply ground-breaking (assuming we work out the confabulation kinks). EDIT: fixed grammar/typos. Maybe I should have had an LLM proof-read this...

visarga 2y ago
> the foundation of civilization is standing on the shoulders of giants. Progress in large part is figuring out better way of doing that It's so much easier to imitate than to invent something truly novel and useful. Let's do a bit of napkin math. A human lifetime is about 500M words. GPT-4 used up about 30,000 human lifetimes of language. But cultural evolution took 200K years and 120B people to get here, about 4 million times the size of GPT-4's training set. That is of course hand wavy, but it shows progress is a million times harder than imitation. We really are standing on the shoulders of giants, or a very long chain of people. If we forgot all the knowledge preserved by language it would take us the same effort to recover as the first time around. I think all progress comes from search - search for experience (data), and search for understanding (data compression). This feeds on itself, but is coupled with the search space - the real world. And the world is not eager to tell us all its secrets. AI will only advance as fast as it can search, it's not a matter of pure scaling of computation, we need to scale interaction and validation as well.
- joshmarlow 2y ago
  > AI will only advance as fast as it can search, it's not a matter of pure scaling of computation, we need to scale interaction and validation as well. I think Kevin Kelly made a similar point in explaining his skepticism around intelligence explosions; even if we build something much much smarter than us, the thing will still need to do experiments to fine-tune it's (super-human nuanced) understanding of physics/biology/whatever - and the clock-cycle of external reality isn't speeding up like our computation is. I think something smarter than us could design better/more informative experiments than we could to gather information about the world. That being said, I think his/your point is insightful.

swagasaurus-rex 2y ago

While I agree these LLMs are getting pretty good, I think google shares fault for making it so hard to find information on the internet. You have to scroll past 4 sponsored links to get to their algorithm results, which themselves have been gamed by SEO and content farms. As far as I can tell, google has no plans to limit AI generated botfarm search results. LLMs now seem to get you an answer more quickly than google. LLMs don’t cite their sources, so IMO they can’t fully replace google for me yet.

piombisallow 2y ago
I think it's not necessarily Google's fault, it's an arms race and they're losing it.
- hnthrow562 2y ago
  AI race perspective by sama. https://www.washingtonpost.com/opinions/2024/07/25/sam-altman-ai-democracy-authoritarianism-future/ https://www.washingtonpost.com/opinions/2024/07/25/sam-altma...
ctoth 2y ago
I don't really understand what this comment about Google has to do with OP's comment. Is this something that even old Google could have done pre-SEOification? Would Google have been able to read the datasheet? Or just point OP at what they already said they were happy to avoid? > Without AI this would have been a long long process of brushing up on probability, vector math, manually transcribing the chart. Sometimes, it feels like stochastic parrots are seizing on a few words from the comment to pattern match it to the closest typical refutation and failing bigly.
- anthonypasq 2y ago
  tremendously bigly
  - ctoth 2y ago

deleted 2y ago

[deleted]

namaria 2y ago

Congrats on learning way less about the systems and tools you're interacting with?