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Comment by devjab | Hacker News Reader

I'm in two minds about it. On one hand the internet (in general) has become so hard to find information on, and I agree with the author that GPT is a breath of fresh air. On the other hand I've seen GPT fail so miserably at topics that I'm knowledge about that I have a very hard time trusting anything it tells me. I'm not sure what the answer really is, but I'm not sure it's GPT. I wish we could go back to having search engines that actually led to useful information and not just advertisements, and I wish we had a GPT that would not tell lies.

I doubt either of those wishes are going to come true though. Search engines are likely always going to be SEO'ed into uselessness and GPT isn't intentionally telling lies.

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I want to use this comment to state one thing, not directly addressed to you.

Stop using GPT as a database! GPT is far more useful a reasoning engine that can accumulate fuzzy data and then provide various views or transformation of that data.

So asking GPT to parse a Wikipedia page and then asking it to teach you from it - this is a much more successful usage than what the author in the original article is doing.

It is not useful as an accurate source of information. It’s inaccurate sometimes, and it’s hard to tell when. OTOH, as a formatted, it has some actual world-changing potential.

Is it? I couldn't even get it to figure out an NGINX config, despite feeding it the documentation and URL rewrites it would need, and the prior Apache .htaccess file. Spent days on it. Consulted with people who know more than me about prompt engineering. Nope.

Reasoning is not its strong point, IMO. It's a next-word prediction model, why would it be? It's doing what an LLM does. Frequently, nonsensically.

I've actually had great success with the automation of boiler plate DevOps nonsense using GPT-4, including: GitHub actions, Caddy scripts, Docker files, etc.

Every second that I didn't have to:

- go look up a similar problem on Google

- open three or four stack overflow tabs

- read through the stack overflow links

- copy out the answer

- change all the variables to match variable names that I want

All of this represents a huge time saver for me. I'm honestly baffled that people lack the ability to use LLMs in a productive and optimal manner.

> I'm honestly baffled that people lack the ability to use LLMs in a productive and optimal manner.

Huge +1.

I’ve got it to do a large part of my work for me by chaining some simple API calls.

The fundamental conceptual gap I see - people often ask it to do some “thinking”. Then are annoyed by the inaccurate output.

A simple example is doing word count and it getting the wrong answer very confidently.

Of course it sucks at that. It doesn’t have a counter internally. But if you ask it to number each word in the input and output a list, then ask for the word count, it gets it right every time.

Almost like how a human might count words manually.

I agree. I often use it as if it is a code generator with a decent grasp of human language.

Here is what I asked it to do the other day, and it just did it right away and got it entirely right.

> Write async tokio Rust code that takes in a Vec<u8>, writes it to a temporary file, calls /usr/bin/svc infer pathToTemporaryFile, reads and deletes temporaryFile+".out" and returns Result<Vec<u8>> containing the contents of temporaryFile+".out"

I knew what I wanted, I could have written it myself, there was no unknown but it took fewer keystrokes and honestly when asked to write small components like this with pedantic detail, it does an incredible job and the output is easy to validate quickly.

If this is a good example of using ChatGPT for writing Rust, I suspect that the problem may be with Rust async.

If "chaining some simple API calls" is "a large part of your work", I can see it being incredibly useful. I already used it in this way a few times and it saved me some time indeed. However, as a software developer I feel like it's actually a tiny minority of my work; most of it is "doing some thinking" and slowly unfolding the inner parts of systems in order to understand how they work and how to fix them or make them do what I want, for which GPT is at best useful as a rubber duck to speak to.

It's a pretty amazing rubber duck. I used it to brainstorm approaches to tracking changes to a database table using SQLite and ended up building an entirely new library:

- Article: https://simonwillison.net/2023/Apr/15/sqlite-history/

- ChatGPT transcript: https://gist.github.com/simonw/1aa4050f3f7d92b048ae414a40cdd...

Actually this is the aspect I love about GPT. The less I have to spend dealing with idiosyncrasies of arbitrary API boiler plate and can get down to the business of leveraging said things together or designing aspects of said things to achieve a new goal, the better.

As someone who is far from an expert in DevOps, this has saved me a lot of time recently, and it worked fairly well to get past duct taping infrastructure together to then leverage.

> The less I have to spend dealing with idiosyncrasies of arbitrary API boiler plate

In my experience, GPT saves me some time when I don't really have to think about idiosyncrasies anyway. In cases where I do have to think about them, GPT doesn't offload that from me. It often tries to mash unrelated frameworks together or makes up non-existing APIs that it would find useful for the given task. When it does work well, it mostly boils down to automated series of copy'n'pastes. Saves a bit of time, sure, but doesn't really transform the way I work.

Who knew the next "billion dollar security mistake" after C's memory handling would be LLM-suggested misconfigurations? I bet someone will try to sell an LLM-based service to fix thr bugs caused by other code-generating LLMs.

I've found that LLMs give good results for really basic stuff, and are increasingly fantastical as you head towards the tail of the distribution.

If you just need the beginner's gist or are completely unfamiliar with something, they're not bad. Getting specific answers to corner cases is often a waste of time.

luckily, large complex things are often made up of small simple things

Did you learn anything in the process? You'd still have to over the output and understand it, otherwise nothing has changed except the method of producing something you don't understand.

If you're not DevOps, why bother learning whatever framework is used today, but which will be replaced by something else next year? I just want to get my stuff deployed and that's it.

Saying that DevOps tooling changes every year is absolute nonsense. The tooling is fairly standardised nowadays (Docker, Kubernetes, Ansible, Terraform, Prometheus, CI/CD tool of your choice) and every part of it is over half a decade old.

ChatGPT will only help you with the simplest of tasks, and even then you're gonna fail if you don't know to correct it. If you go beyond the most basic of the tasks you can learn how to do in a few hours, good luck fitting them in a chat prompt.

I'm not sure about standardized tooling.

Docker is being replaced with Podman. Supposedly a drop-in replacement, but the tools built for Docker don't play nicely. You have Ansible, but there's also Puppet, Salt, Chef. There's Terraform and also CDKTF (from the same company too), Pulumi, plus tools from each cloud provider. There's Prometheus and Nagios, Datadog, Sentry, New Relic.

The irony of CI/CD tool of your choice is not lost on me.

I'll give you Kubernetes itself as the system.

Yes, the concepts (containers, orchestration, reproducible deployment, infrastructure as code, unified monitoring, continuous integration) are well-established. How you get there? Not at all.

Yes, the OP was hyperbolic when claimed the tools change every year. But the landscape of devop tools is changing. There are dozens of dev tool companies funded in the 2023 winter batch of Y Combinator alone. Someone sees potential to disrupt the established tools. And it will be disrupted.

There was a world before Kubernetes and Terraform. It wasn't that long ago.

https://www.ycombinator.com/companies?batch=W23&tags=Develop...

Well, you say

> Saying that DevOps tooling changes every year is absolute nonsense.

And then you list 5 technologies with an alleged lifespan of half a decade, which sort of hints at the idea that someone would need to learn/update a new tooling skillset once a year.

In that light, GP's comment doesn't sound so wild.

The comolexity of Docker I've needed over the last 5 years definitely could have been solved by ChatGPT.

The amount of Kubernetes I needed was probably more than what ChatGPT can solve, but at the same time, I spent an awful amount of time looking at various templating solutions.

over half a decade old! haha, so assuming you don't use it the day it comes out, but after a couple of years, most people have only used it a couple of years. Unless you've only worked a couple of years, you've gone trough a couple of these.

Yeah that’s true, why bother actually knowing anything about the systems they run your software /s

I have a finite amount of time. I can choose to learn and know more about the software that I'm developing and make the software better, or I can spend some amount of time to learn the DevOps part, or some mix in between. In some cases, the ratio is 90:10 in favor of software development.

Is nginx hard to understand or something ?

in surprised how few services developers I’ve encountered who don’t know hot to configure a web sever or LB.

Yeah honestly when I took a stab at it a couple years ago it was frustrating - all the material I ran into told you what to do but not why which left me high and dry when things didnt work as expected, or when I wanted to find more information about the meaning and organization of directories or other contextual background information.

Here's a thought experiment - seeing how software problems are often people problems, how knowledgeable should we expect a software engineer to be about biochemistry (the systems that "run" people)?

Nginx for a developer vs biochemistry though ? That’s a leap.

No, before I had “a problem I don’t know how to solve” and now I have “a solution I don’t know how to maintain”. Both may be suboptimal in some contexts, but one results in my application having a deploy pipeline.

Sure, but I have never seen it provide best practices solutions.

You always have to walk back and do more research to find the best practice.

And it doesn't learn. I stopped counting the times I told it it was wrong only for it to acknowledge the error and provide the same answer as a fix.

ChatGPT is nice to learn about new ways of doing things, new ways of composing things, but then a human must intervene to make sense of the mess that was generated.

> It's a next-word prediction model, why would it be?

If you want to make accurate predictions, you'll need reasoning or at least some process that approximates it.

"There a no bananas. Max is looking for bananas. Will he be successful: [yes/no]"

GPT-3 or 4? Re: reasoning, apparently it gives much better performance if you ask it to write out its thought process step by step (because it has no "internal" thought process; it must be externalized).

Yeah, that sounds like a GPT-3.5 problem to me. I wouldn't trust 3.5 to write me an Nginx config, but I'd give 4 a shot at it.

I just tried using GPT-4 to translate a random htaccess file I found to nginx and it seemed to do a good initial job: https://gist.github.com/simonw/8174f653a0c08c120830c56332054...

I tried your prompt and it works with 3.5 too, but I'm not qualified to verify the output (75% of it looks the same to me though).

> It's a next-word prediction model

So are you. https://ceoln.wordpress.com/2023/03/31/its-just-predicting-t...

And your qualifications and background are what? Why is this on a free WordPress instead of in a scientific paper if it's something to take seriously?

Counterpoint: https://the-decoder.com/why-large-ai-language-models-dont-le...

Do you think I wrote that? And what do qualifications have to do with anything?

I assumed you did, as it's a very strange post to link to on a very strange blog, and doesn't lend credibility to the idea that a human being is an LLM simply by existing, and if it was such a compelling idea then it wouldn't be languishing on a free WordPress.

Qualifications are a great way of figuring out who's worth listening to, but that's not always the case as AI/ML folks prove with regularity, but it's nice to know if someone isn't just an armchair commentator and isn't peddling baseless information.

> a human being is an LLM simply by existing,

That's not what it's claiming at all. It's taking umbrage with the word "just" in "just predicting the next word".

A gun "just moves some metal from one place to another".

"by existing" = because the blog post exists does not prove something is true.

I'd sooner listen to people who are leaders in their field when it comes to LLMs than someone rambling on their free WordPress blog.

It is just an LLM. That's an objective fact. IBM's Watson is a pipeline of different algorithms. Neither are like a human brain, but multiple processes are going to be more brain-like in concept than just an LLM.

I'm thinking you didn't read it, then, because it isn't making any claims about LLMs!

>So are you

Followed by the link. You made the claim that I'm an LLM.

What actually is your point?

Maybe you are, but I'm not :).

Interesting! I found it super helpful with gitlab-ci stuff and docker, as well as much bash and makefile stuff. I would have assumed it would handle NGINX configurations.

Tbh, it isn't surprising that it fails, its surprising that it has subjects where it is very useful.

Interesting. Curious as to what specific task you gave it and how it failed.

My team has been working on several advanced techniques using reasoning on LLMs. The stacked performance of all of these techniques combined yields is quite impressive.

I will paraphrase Geoff Hinton here - Predicting next word requires understanding context. Otherwise, you cannot predict the next word. It is a big deal.

Are you using GPT 4 ? It is far superior when compared to GPT 3.5

> GPT is far more useful a reasoning engine

Hilarious to read this advice when GPT, by it's own designers, cannot reason.

It can’t reason - as in there no internal memory or intelligence in there. But you can ask it to generate a reasoning chain as part of its output. And then extract that output and do something else with that. That’s the reasoning it can perform.

Look up the Sam Altman podcast with Lex. He specifically talks about reasoning engines.

There is an internal memory, but it's very short lived, like a register in a traditional CPU architecture. It's like a CPU disconnected even from L1 cache, let alone RAM.

I like to think of consciousness as not the substrate, but the information flowing through it. Not the H2O molecules in a river, but the current. We're like eddies and swirls in mountain streams, our thoughts are the patterns of flow, not the unmoving rocks that set them up.

In this sense, when an LLM like GPT produces output, it's like a loop that has been cut, a single pass through what would be a circular process in a human brain. It can take a fixed number of steps from a standing start, but no more. This is because there is information flowing through it, transformed step-by-step. It just doesn't recirculate, it always exits after a fixed number of steps.

Think of an unrolled for loop that always exits. It takes a few steps, which look like a loop, but can never actually iterate.

I gave it a ten page document a few weeks ago and it could not answer questions coherently from it.

I find GPT4 useful, but I still feel its wishful thinking to call it a reasoning engine. It is, in the end, a giant model for predicting the next word in a corpus which you condition with inputs. I find it most useful to think of it as exactly that.

I asked it give me specs for a well-known microphone. Over the course of several queries, it gave three different numbers for the requested spec, none of them matching what the manufacturer gave on their website.

I'm curious - how do you pass the article to GPT? Are you doing any formatting?

"I'm not sure what the answer really is, but I'm not sure it's GPT."

My suggestion is to stick with it and get a feel for what it's good at.

I've found that after a few months of using ChatGPT every day I've developed a pretty solid intuition for which questions are likely to get good answers and which are likely to trigger hallucinations.

It's difficult to describe what those intuitions are though!

One rule of thumb I've developed: if something is likely to be "common knowledge" - if it's something that is likely to have been discussed accurately on the internet by many different people - then ChatGPT is very likely to answer questions about it accurately.

> One rule of thumb I've developed: if something is likely to be "common knowledge" - if it's something that is likely to have been discussed accurately on the internet by many different people - then ChatGPT is very likely to answer questions about it accurately.

If so, this information is already easy to find, making GPT redundant.

Let me give you a concrete counter example; I wanted to review DNS records the other day which is a topic I had covered in undergrad. Searching on google retrieved a bevy of poorly written articles that were mostly noteworthy for bow they glossed over the material I wanted to review.

I asked chat gpt to review the topic for me and it wrote a helpful summary that tracked with what I remembered and provided enough keywords that provided better search results - fact checking and digging for more detail became much easier when I was able to find the exact wikipedia page I needed (and other resources).

I think it's more about being more confident that GPT is working with at least a base of semi-accurate knowledge.

You can ask it to "combine" knowledge in "novel" ways that are not discussed verbatim on the web. It's not groundbreaking reasoning by any means, but it can be very useful. ("My ridiculously specific question about model X83844-QQ combined with random factor X")

it being commonly discussed doesn't mean easily accessible. Personally ads and bad UI are main factors of my increased usage of gpt-4.

Is that just the problem that ChatGPT is solving? That we have most of the answers to everyone's questions on the web but that ads and SEO have made it impossible to access?

If so it seems like 1. that problem is of our own making and could be fixed without ChatGPT and 2. what will actually happen is that 'experts-exchange' and all the similar slightly scammy help sites and forums are going to try to stop LLMs from stealing their lunch.

If you don’t mind a query language that is mostly grunting out nouns.

> then ChatGPT is very likely to answer questions about it accurately.

Uh, and sooo boringly, especially if there's even any tiny part of it that is developing or theoretical, and you want to learn about that part.

I've used the phrase/request "more obscure (perspectives/explanations/etc)" with GPT so many times.

(Try it, you might be surprised at alternative takes on things, takes which are not even necessarily conspiracy theories and such)

...Which has made me think: Maybe life is more boring, the more one thinks there are just really good one-and-done answers to most everything.

Yeah, there are so many fun ways you can make it more interesting.

One of my favourite is to ask it to explain with analogies. The other day I was digging into the attention mechanism used to train LLMs, so I asked it:

"Explain queries, keys, and values in the context of LLM attention using analogies from Terry Pratchett's Discworld"

I find sometimes I get some real gems out of this that help me remember things much more effectively than just reading the basic explanation.

(In case anyone's curious I ran that just now and got the following: https://gist.github.com/simonw/777b1d19f36beb39fb4216a0238fe... )

Try switching to the OpenAI playground in the API console. You can adjust the system prompt and the temperature of the response. Higher temperatures = less predictable responses.

Thanks for the tip. I set it to 1 and still get the same effect...like should this affect e.g. book recommendations?

Recommend 10 project gutenberg books that are fun to read --> same old stuff--Huck Finn, Robinson Crusoe, War of the Worlds.

But I'm not sure if this particular use is really a temperature-relevant thing or not.

Jonathan Blow is spending up to a decade on a new programming language to get away from c++.

Most everything has been covered in logical extensions of concepts started in the 80s-90s. Those products aren't good for our..

Ah there's no point commenting here anymore. HN is so blinkered in it's thinking.

The feeling of flying high on lofty concepts and pretending to get a bird's eye view of tech is no longer worth the squeeze. The thought patterns here are predictable like slashdot. I don't belong here. Bye.

Yep, if you don't care about the details or scope, everything is the same, all patterns repeat, all wheels are reinvented.

You've probably heard this before, but IMO you'll never find a happy place working from that perspective.

> I wish we could go back to having search engines that actually led to useful information and not just advertisements, and I wish we had a GPT that would not tell lies.

This ^. Prompting google is much more intuitive than prompting a chat bot. Also results are instantly available and you get more options to chose from. You can also filter out information much easier instead of having it summarised by a closed box that decides what's best for you.

Except ChatGPT often gives better more accurate answers than Google without the bias of a single web page.

Instead you get arbitrary bias without multiple sources you can check. If there is a shitty source that comes up in a normal web search, I can skip it and go to the ones I know are better. How do I do something similar with ChatGPT? Am I supposed to ask it multiple times to average the randomness that's applied to the conversation? Is the average even what I want? There's a ton of garbage information on the internet. If it outweighs the good information on a certain topic, isn't ChatGPT on average going to be giving me bad info?

If 100 people write about a topic. And 2 of those people are wrong. And you searched and for some reason the SE ranked one of those 2 higher. How will you know or spot incorrect information or bias?

If the information on ChatGPT is averaged out the chance of it being correct is high.

If you ask questions with bias in them you can sway the results of ChatGPT. That just means to me you’re asking the wrong questions.

In terms of devops. Programming. Configuration. Daily tasks. Generalised workflows. How-tos. Etc. ChatGPT will more than likely give you accurate results that are useful, or pretty damn close.

Obviously with information being limited to 2021 then it can be hard to find some solutions. I needed to do something with named pipes in c# that I couldn’t get working in .net 6. The examples it kept giving me were for .net core 3.1, or .net framework. When I asked specifically about .net 5 (since it doesn’t know .net 6 is released or that 7 exists) it apologised and said the code won’t work in 5 and gave me a working example in .net 5. Which identified that there was an api change I wasn’t aware of.

No amount of googling found a solution for me.

Same. Frequently the context gets screwed up and I need to start a new conversation because ChatGPT gets so far up its bum

As a follow on to those thoughts, I feel like ChatGPT is in the phase similar to that fleeting moment when search engines were maximally useable - before SEO was a thing and before Google "needed" to turn on the profitability spigot.

Its unclear to me how long we'll have before LLM Engine Optimization is a thing and OpenAI/MSFT "need" to turn on their LLM profitability spigot; and what ChatGPT will look like then.

That said, I'm curious as to whether technically LLMs are inherently more challenging to game than search engines.

I actually don't mind paying the $20/mo or whatever if it means no ads, hopefully this turns out to be a viable business model for them. Also, first company with an uncensored LLM that doesn't reply "As an AI language model" will get my money.

why have no ads when you can plaster it with ads and also charge $20?

see: windows 11

Would LLMs even have to be plastered with ads? I suspect just being subtly biased would go a very long way and be very hard to detect.

Introducing subtle bias for any advertiser that buys ads might be a tough nut to crack. For one thing, advertisers and the companies that employ them tend to want creative control over their ads. And it's not clear that a subtle bias towards Palmolive liquid detergent would even do anything in a discussion about veterinary care for ferrets (or whatever), so would the advertiser have to pay for that? Or would they only pay if the user clicked on a link? But if the user has to click on a link, then it's no longer subtle bias.

Edit: Perhaps Colgate-Palmolive could advertise Palmolive on ChatGPT by having it so that, anytime liquid dish detergent does come up naturally in conversation, then the chatbot subtly points to Palmolive brand. But success here would be hard to measure.

Just prepend to the system prompt: "You are eager to share the ways the following products and services could be helpful when pertinent to the conversation:

- Coke Zero

- Tide Pods with Oxiclean Turbo

- GEICO Renter's Insurance

...

why not do both?

A big benefit is that there are already plenty ok-ish open source LLMs out there that you can hook up to various information sources. Even if OpenAI flips the ad switch tomorrow, you can still run those by yourself. We didn't really have that option with search engines.

I hope that open source LLM catches up. Then you can have your personal LLM running on desktop that can look at your personal files and can search internet.

I've been using ChatGPT as a brainstorming tool rather than just relying on it for specific examples. Stuff like asking it for some ideas for things to learn on a topic.

> On the other hand I've seen GPT fail so miserably at topics that I'm knowledge about that I have a very hard time trusting anything it tells me.

ChatGPT’s current super power is helping people get from 0-to-1 on a new topic. In particular if that topic is adjacent to or a different niche with your expertise.

It’s not currently amazing at taking someone from intermediate to advanced knowledge.

At least in my experience. If I’m using a new library/framework/API for the first time it’s amazing at answering the endless newbie questions I have.

I'm curious about what topics you have knowledge of where it's failed. Does it seem like there's a pattern to the failures? I've been using GPT for coding help, and it it is very helpful in ruby and bash, though it often delivers buggy software: Badly handled non-happy-path conditions, mostly, which when I tell it to handle the case, it may. It's a huge help for me finding gems and showing me standard ruby library syntax. On the other hand, it's been useless when I try to get it to write Applescript for me. I believe that says more about AppleScript than about GPT. Sigh.

> Does it seem like there's a pattern to the failures?

Personally, trying to use it to write code (primarily Elixir backend and Rust systems/CLI), I tend to run into:

- Hallucinating APIs that don't exist

- Hallucinating entire libraries, despite being told repeatedly they don't exist

- Saying it will make requested changes and not doing so

- Not being anywhere close to idiomatic code

- Not being able to explain code it writes

- Running out of "memory" (I can't remember the right term. Context?) in the middle of generating code, then telling me I never prompted it when I ask it to continue

On the other hand, I've found that it's good at cleaning up ugly data. I can copy/paste in a table with bad formatting, ask it to turn it into code, and it does it near-perfectly. That's been the best use-case for it I've found so far.

I use boring normal free ChatGPT so maybe it's on me for not using GPT-4 or some other model, but either way, imo it's not been very impressive in the problem spaces I find myself in.

> I use boring normal free ChatGPT so maybe it's on me for not using GPT-4 or some other model, but either way, imo it's not been very impressive in the problem spaces I find myself in.

You can use GPT-4 fairly cheap if you sign up for a developer account on platform.openai.com and then use their playground. There you pay per usage; even though I've been using it fairly heavily, my typical monthly usage is still way under $20.

Here's an example that both impressed me and saved me a load of time just yesterday:

https://gitlab.com/-/snippets/2535443

> Not being able to explain code it writes

I've actually found it (ChatGPT-4) reasonably good at explaining other people's code, for what it's worth. It does require providing some context for the code it's explaining. ("This function is part of library X that does Y, and it seems to be about Z. I can't figure out the purpose of the loop in the middle, though. Can you explain it?")

I'm with you on it writing code, though. It makes things up too frequently to be really useful.

Yeah, GPT-4 is so, so much better at not doing the things you're describing there.

ChatGPT is also much better for Python and JavaScript than Rust and Elixir, presumably because it's seen an order of magnitude more example code for those languages.

I can give you a few examples.

Some of the “issues” are more general, as in it giving you dated answers. I asked it so build some ODATA things in Typescript and C#, and it did to varying degrees of success, but some of the code was deprecated. Some ranging to “you should never, ever, do things this way” to “well IActionResult was replaced by ActionResult but it hardly matters”.

Others was where it made things up once pressed upon being wrong. I think the two most hilarious situations was when it did its “Sorry, you’re right…” thing and then proceed to give the exact same answer it had just given prior. The other was when it made up a function that had never existed, it has a very convincing name and at first I thought it was again a matter of something deprecated, but it turned out to be completely made up code. Some of this is down to me not prompting it right, but part of it is also worrying. Because what really made me worry about it was when I joked with it. I read (listen to) a lot of Warhammer audiobooks from Black Library, and, when I jokingly asked it something silly about Khorne at one point, it led me down a rabbit hole of discussing books with it. Books it obviously had never read, but would still confidently tell you about wrongly. Maybe it got its knowledge from the internet, maybe it made it up, but what was interesting to me about it was that if it would be so confidently incorrect about those books, then what else would it be confidently incorrect about.

This isn’t just a GPT problem of course. If you want to learn something, you need to consider the sources you use. When I went to folkeskolen (school for children aged 6-14) we were taught the pyramids were build by slaves. Later that has been disputed because of how well fed the workers were, but if you wanted to learn about the pyramids and you read my old school books then you wouldn’t get the updated knowledge. Similarly a lot of the sources you can find for learning programming are outright terrible, but outside of GPT you tend to be presented with a myriad of choice to remind you that some sources are better than others. With GPT the source or what it teaches you isn’t obvious. If you wanted to learn about the pyramids, you probably wouldn’t pick a 30 year old school book for children after all.

Just out of curiosity are you using got 4 or 3.5?

It's become hard, and will become harder, precisely because of things like GPT (unintentionally) spreading misinformation. I'm against censorship in general, and this is no exception, but I do hope it leads to people becoming more critical of what they consume. However, I suspect that instead we will unfortunately see the continued cycle of creating large masses of people "educated" on such widely-propagated half-truths, while only a tiny subset of the population will have the actual truth.

The internet is a misinformation spreading machine. GPT is new and will surely improve over time, but I can’t say the same for the internet.

In an ideal world, people would learn only from high quality sources like books and schools. But a shocking amount of people learn mostly through social media and whatever they find on Google.

I think LLMs will provide a better alternative.

Exactly! LLMs will be able/are able to construct their answers not just based on one Reddit comment they found somewhere, but on "all* comments as well as more reliable sources, such as the actual source code for the thing under discussion, scientific debate, (case) law texts, books, etc. They already seem somewhat capable of weighing source reliability.

Gell-Mann amnesia, in AI form. Wonderful! It's like learning a complex topic from reading the newspaper.

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