Comment by brotchie | Hacker News Reader

Comment by brotchie | Hacker News Reader

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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 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.

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.

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.

You used to find amazing information on the internet back then. It is quite likely that someone else had already worked on a similar topic and blogged about it in a very searchable way. Without good search that kind of online culture died out.

While we do have a video tutorial culture that exceeds what we had back then in many ways, and to be fair that technically happened under Google's umbrella (Youtube), destroying search and with it a lot of the open internet, will not only be Google's downfall, it's also a silent tragedy.

Actually, measured by what a global online population could have achieved, with human information truly at anyone's fingertips, with infinite communities forming all over the place instead of in non-searchable Discord and monoculture Reddit ... this might be one of the main tragedies ever.

Are you asking if Google could ever find documentation and sample code on the internet?

Or are you asking if Google could understand a badly written query and understand it's about the sample code?

Because the answer to the first is "yes", and to the second is "no". But the first one adds almost all of the productivity here.

The notion of "stochastic parrot" has been disproven. It did come from a reputable AI researcher in the first place. You can call anything you want a "stochastic parrot", including human intelligence.

tremendously bigly

Meta. Made me laugh.

I think it's not necessarily Google's fault, it's an arms race and they're losing it.

AI race perspective by sama. https://www.washingtonpost.com/opinions/2024/07/25/sam-altma...

Just FYI, Phind cites it's sources. Which has proven beneficial for additional context.

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.

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.

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.

> 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.

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.

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...

> 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.

> 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.

Well put.

> the foundation of civilization is standing on the shoulders of giants [...] Progress in large part is figuring out better ways of doing that

Cynical take: When it comes to helping someone find exactly the right piece of esoteric knowledge needed... There's no profit for that in a search engine, and an LLM reflects word associations rather than facts.

IANAScienceHistorian, but I find myself thinking of how DNA analysis would be different if nobody had found Thermus aquaticus, with it's extra-hardy variant of polymerase, or how the history of stealth aircraft was kicked off when someone realized the implications of Petr Ufimtsev's equations for reflected EM waves. (A work which went--heh--under the radar inside the USSR.)

>When you look at things that way - LLMs start to seem deeply ground-breaking (assuming we work out the confabulation kinks).

even if you don't work out the confabulation kinks, given what was said by the first post, it still seems groundbreaking enough.

Although I can't figure out why every time I do anything with LLMs it's not worth it, I guess because I am using it for things I am an expert in, it doesn't help.

Actually maybe it's like the article Suggestions from Idiots - https://medium.com/luminasticity/suggestions-from-idiots-6b0... - suggesting distracting instead of diverting is not helpful when diverting is the better word in context, but for someone who doesn't know what word to use in the context or uses diverting when it is not the best word the suggestion is useful.

If that's the case what he got out of Claude is probably not that great, but it is passable, just like the word distraction instead of diverting in the right context is still passable, or when I get back a time conversion function from CoPilot that fits all but a few edge cases, it's just fine as long as the edge case never hits it, then it sucks - maybe there's some edge cases where his LED diffusers won't work quite as well as they could if written by an expert.

Then again it might be that since what he is doing is analog edge cases and tolerance for failure is such that when it does fail it is not as problematic as when a bit of code fails because the computer dealing with the output is not as forgiving as the human eye and brain.

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

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