I'll also note that when there was hype around Stable Diffusion, one of the images shared around was that of an astronaut riding a horse. If you actually run Stable Diffusion with its default tuning and ask for that prompt, you will get 6 images, of which 5 of them are outright disasters (horses with 6 legs and going downhill from there), and then the 6th image, the only one which could possibly pass as a decent result, is the one that everyone shared and reshared and hyped. Usually other prompts give even more terrible results where there are 0 passable images without extensive tuning. Stable Diffusion now is acknowledged to actually be crap --despite the hype-- and I supposedly need to try the next best thing, whatever that is. But I find myself facing the same situation with ChatGPT 3.5, and now with ChatGPT 4, despite the fact there is no "next best thing".
Obviously you have to play around with them and figure out how to prompt & tune them.
Once you do that, you can get pretty amazing results.
I make do a bunch of social media graphics and stable diffusion + midjourney has been insanely useful.
You literally cannot respond with "you are holding it wrong" specially when I'm claiming that even for the popular _example prompts_ SD authors used they had to hand-pick the best random result over a sea of extremely shitty images.
And even in the original paper they disclaim it by saying "oh, our model is just bad at limbs". No, it's not just bad at limbs. They just happened to try examples where it could particularly show how terrible it is at limbs (i.e. spider legged horses and the like). But in truth, it's just bad at everything.
I'll repeat myself. You have to play around with the models and learn how to use it (just like you have to do for everything) .
> But in truth, it's just bad at everything
Thousands of people (including myself) have had the complete opposite result and have gotten amazing pictures. You can play around with the finetuning with different models from civitai and get completely different art styles too.
Like, this is so dumb I don't even know how to respond lol.
You're like some guy who got a computer for the first time and couldn't figure out how to open the web browser, so he just dismissed it as useless.
Most egregious if you are even doing more tuning and cherry picking that the authors of the models are doing, which you definitely are.
Drawing isn't hard, you know
SD will never compare to the power of pencil and paper imo. Drawing is an essential skill for any visual artist not just for mechanics but for developing style, taste, and true understanding of the world around you visually.
I recommend Freehand Figure Drawing for Illustrators as a good starting point (along with some beginner art lessons). It won't take 1k hours before you see results. It's also fun!
It works surprisingly well.
Why do I want to avoid that reliance, other than to be smug to the nerds? And as far as general self-satisfaction, let's assume I would rather master a different skill with the time it would take.
Especially because that training cost only has to be done once, so on a per-person basis it beats learning to draw by a lot.
If you said something about flexibility and specificity of what you can create I could get behind that, but I think the arguments you're making are very unconvincing.
If drawing was that easy, no one would worry about disruption from AI image generators, because everyone who wanted images would be knocking them out by hand, not paying people for them, so there’d be nothing to disrupt.
Therefore, I would say most people hyped for SD and friends are the capitalists and consumers of the art world - not the creators.
Repeating an assertion more times doesn’t make it true.
> If you want to create, you can and will learn to draw.
“Drawing”, even when it comes to imagery, is far from the only form of creation, and, even if it were, will is not determinative of capacity.
But, yeah, I mean, people have said similar things about basically evey new means of creation ever.
No, the fact that something is made with it makes it creation. Imagination makes it creative, on top of being creation.
The problem I see here is you are trying to insert your moral/aesthetic judgement of the quality/value of the mechanism/process of creation into the objective description of whether creation is happening, probably because you have subscribed to a worldview which valorizes creation and denigrates consumption so accepting something you don’t want valorized as “creation” is incompatible with that worldview.
You can just say you don't like Generative AI and wish people wouldn’t use it. You don’t need to declare that creating things with it isn’t creation to try to mask your (valid as any other) aesthetic preference in a very silly circumlocution designed to resemble an objective description, albeit a patently self-contradictory one.
Besides, there are models that are much more capable than Stable Diffusion. The best one currently seems to be Midjourney V5.
No, it wouldn't have – not to people in the know. We just didn't have powerful enough computers back in the 90s. Sure, the techniques we've got now are better, but 90s algorithms (with modern supercomputers) can get you most of the way.
Transformers are awesome, but they're not that much of a stretch from 90s technology. GANs are… ridiculously obvious, in hindsight, and people have been doing similar things since the dawn of AI; I imagine the people who came up with the idea were pretty confident of its capabilities even before they tested them.
Both these kinds of system – and neural-net-based systems in general – are based around mimicry. Their inability to draw limbs, or to tell the truth, or count, are fundamental to how they function, and iterative improvement isn't going to fix them.
I'm not talking about the 90s. I'm talking about April 29, 2020.
If you're talking about popular awareness of GPT-style autocomplete, then I agree. If you're talking about academic awareness of what these things can and can't do, we've had that for a while.
Three years ago there was nothing remotely as impressive as modern GPT style or text to image models. Basically nobody predicted what was about to happen. The only exception I know is Scott Alexander [1]. I don't know about any similar predictions from the experts, but I'm happy to be proven wrong.
[1] https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
Well, yes (that file was), but actually no. StyleGAN1's public release was February 2019, and it's capable of far more than just faces.
> Three years ago there was nothing remotely as impressive as modern GPT style
I predicted that! Albeit, not publicly. (My predictions claimed it would have certain limitations; I can show that those still exist in GPT-4, but nobody on Hacker News seems to understand when I try to communicate it.¹)
> or text to image models.
Artbreeder (then called Ganbreeder) existed in early 2020, and it didn't take me by surprise when it came out. It parameterises the output of the model by mapping sliders to regions of the latent space; quite an obvious thing to do, if you want to try getting fine-grained control over the output. (A 2015 paper built on this technique: https://arxiv.org/abs/1508.06576)
I was using spaCy back in 2017–2018-time. It represents sentences as vectors, that you can do stuff like cosine similarity on.
If I'd been more interested in the field back then, I could have put two and two together, and realised you could train a net on labelled images (with supervised learning) to map a spaCy model's space to StyleGAN's, which would be a text-to-image model. It was very much imaginable back before April of 2020; a wealthy non-researcher hobbyist could've made one, using off-the-shelf tools.
If I were better at literature searches, I could probably find you an example of someone who'd done that, or something like it!
---
¹: See e.g. here: they tell me GPT-4 can translate more than just explicitly-specified meaning, and the "evidence" doesn't even manage that. https://news.ycombinator.com/item?id=35530316 (They also think translating the title of a game is the same as translating the game, for some reason; that confusion was probably my fault.)
I don't know. I've had chatbots for decades before "a few years ago", so I have never been particularly impressed. I would say that for someone who was already impressed with that you could practically describe a landscape in plain old 2000s Google Images and get a result, SD feels like just an incremental improvement over it -- the ability to create very surreal-looking 'melanges', at the cost of it almost always generating non-sensical ones. And also add that Google Images is much easier to use than SD...
I do a lot of my SD with a fixed seed and 1-image batches, once you know the specific model you are using getting decent pictures isn't hard, and zeroing in on a specific vision is easier with a fixed seed. Once I am happy with it, I might do multiple images without a fixed seed using the final prompt to see if I get something better
If you are using a web interface that only use the base SD models and don’t allow negative prompts, yes, its harder (negative prompts and in particular good, model specific, negative embeddings are an SD superpower.)
I do agree that it's bad at following the prompt exactly. It will produce most of the things you mentioned in the prompt but not necessarily in the same fashion you asked for. I don't agree that produced images are mostly shitty, just visit that subreddit.
This doesn't really say anything, because it's just survivor bias. The entire purpose of that site is to show the successes. Most people get disasters every single day, they just don't upload them to the site. Even if I try the same prompt I will get a disaster image, as long as I don't use e.g., exactly the same random seed they happened to use. This is not even "prompt engineering". It's just outright playing with the dice.
I madeots of wallpapers in bulk recently with a good prompt and model. None of the images, all via random seeds, was shitty by any measure. Only images I ended up deleting had some usual Ai artifacts I couldn't stand.
I have thousands upon thousands of beautiful images, each one more inspiring than the other and I did nothing or very close to nothing. You are belittling this amazing tech so much it sounds like you are scared of it. What’s it to you?
Have you ever lived in a time where it was even remotely possible to go from “astronaut on horse” and get even 1 decent result, in seconds?
I can’t even.
> You need to agree to share your contact informations to access this model
All of this does feel like another scam where the tech is exaggerated and side hustles are actually the endgame.
I think the coined phrase is "prompt engineering".
Side note, where's the eye roll emoji hiding?
goalposts
You can sit in that tarpit and duke it out with fanatics and doomsayers while patting yourself on the back for "real zingers"... or you can just learn to use the tool and extract your own value from it.
Straw man.
"You can sit in that tarpit and duke it out with fanatics and doomsayers while patting yourself on the back for "real zingers"... or you can just learn to use the tool and extract your own value from it."
GPT offers zero value which reflects my position. Though, it does perform some neat tricks.
GPT offers you zero value because you don't know how to use it, so you're limited to cheap tricks.
GPT offers me zero value because its internal processes are opaque. It's fundamentally unsuitable for business.
Personally, I have no use for it either. Its output is inferior to mine.
To present the best prompt/response while not disclosing that it is a result of trial and error is a different thing altogether.
Advanced users of both Midjourney and SD can get some stellar results out of them. Some of that is due to trial and error, and going through dozens or hundreds of images to pick the best ones, but being adept at crafting prompts and using other features of the programs plays a big role too.
Know your tools.
I use it for my side projects, for tech I have no experience in, and it works very well, because I know what I want, I know that it is possible and I just need it to vomit the boilerplate to save me 5 google searches
For my day job it's next to useless, and if your day job can already be automated by chatgpt I have bad news for you
If they can automate the work of a physician, who exactly is safe? Low skill labor, maybe, for awhile.
My point is that “a longer” implies “when” rather than “if”. Everyone is replaceable, in the long run.
But they can't, it's like saying you're 15cl stove top italian coffe maker is replacing a starbucks tier coffee machine
If the only metric you account for is "it makes coffee" boolean then sure, if you actually implement it you'll notice things falling apart in the first 10 minutes.
(This is a genuine question) Is it hard to find a solution to that question on the web by using a search engine?
I do agree that it's bad at following the prompt exactly. It will produce most of the things you mentioned in the prompt but not necessarily in the same fashion you asked for. I don't agree that produced images are mostly shitty, just visit that subreddit.
SD is definitely very good in the right hands, and it’s a little unfair to expect to be able to get instant good results without any skill. It’s honestly pretty crazy that we now have things like ChatGPT and SD that – and people are already calling them crap because they don’t work perfectly and their productive use actually requires some skill!
But r/StableDiffusion, or any public gallery, is obviously one giant selection effect. 99.9% of attempts could be crap and the 0.1% would still be enough to fill a subreddit.
To give you a general idea of what percentage can be good images: I recently made a lots of wallpapers to cycle through daily using SD. I found a good prompt, a good model, and let it generate bunch of images continuously for few hours.
None of the images were shitty (they were all random seeds), only images I discarded had artifacts I didn't like or couldn't keep my eyes away from. With SD you can't just expect to give a prompt "beautiful landscape" and expect it to give you a beautiful landscape. It won't. You shall get shitty images and might get a few pleasing ones. You must tune your prompt to get good results.
Fixed that for you.
If you have very little technical aptitude, patience, and willingness to learn, it would be better for you to stick with some thing a little more newbie friendly such as mid journey.
However, loading a proper model such as 526, realistic Vision, or ReV, using control net, and guiding a prompt to where you want it to go while making changes using inpainting/img2img/etc can result in stunning images.
It costs you zero dollar to not post an irrelavant comment then. I really wonder how you justify this "I didn't read the article, but I have a very, very strong opinion (straight up calling it a made-up) on it" behavior. The internet is rotting people's brains I guess.
This is relevant to the real world of primary care only if your sole access to a medical professional is Reddit...
N.B. as well: If someone thinks they have bleach in their _eye_ and can still open their eyes enough to write a Reddit post, much less read through ChatGPT’s extremely long answer, they’re almost certainly fine.
Yes. Down in the limitations section of the study (https://jamanetwork.com/journals/jamainternalmedicine/fullar...):
> evaluators did not assess the chatbot responses for accuracy or fabricated information
That is a... significant issue with the methodology of the study.
It seems like you could maybe automate that. Let it spit out its first draft of an answer, have the framework tell it "please correct the errors" and then let it have another go and only present the second attempt to the user.
I'm reminded particularly of a screenshot of someone gaslighting ChatGPT into repeatedly apologising and providing different suggestions for Neo's favourite pizza topping, despite it answering correctly that the Matrix did not specify his favourite pizza topping first time round, but it applies equally to non-ridiculous questions
It's not much use if ChatGPT gives you a diagnosis of your symptoms which may or may not be accurate.
What if someone refers to x part of their body incorrectly? What if they're not able to think clearly due to y reasons and tell it something completely wrong?
An LLM is wholly inappropriate.
As for its "bedside manner"/how polite or friendly it is, that's meaningless if it isn't good at what it does. Some of the best docs/profs I've known have been very detached and seemingly unfriendly, but I'll be damned if they weren't great at what they do. Give me the stern and grumpy doc that knows what they're doing over the cocksure LLM that can't reason.
That said, I can’t really use ChatGPT as a search engine, but I did plug it into a self-hosted telegram bot and I do ask it some basic questions from time to time - telegram is a good UI for it.
I usually need someone to explain something to me. And I used Google before to land on a site where I could find the explanation (e.g. how to use a library). ChatGPT can explain most things I need and I can skip Google and the other sites. But it's indeed not a search engine, if you need factual information then your best bet is to find the documentations, articles, databases, etc.
Not really sure why you are so fixated on the echo chamber thing. We are on the internet! The biggest echo chamber humanity has ever built.
Because it's a layer of abstraction, mate. One known to get things wrong, because it's an LLM. If I write a post about Richard Stallman's opinions on paedophilia and Jeffrey Epstein (https://en.wikipedia.org/wiki/Richard_Stallman#Controversies), and it incorrectly tells you that Stallman associated directly with Epstein, or is a paedophile himself, that would not be accurate to the source.
At least with a Google search result you can go more directly to the source. If the scientific method is getting to the truth, why on Earth would you put an obstacle in front of it?
If someone tells me x, am I going to believe them? No. So, why would I believe an LLM if it isn't presenting sources to me that are 1:1 in accuracy to the information it presents to me?
Alright, but most people aren't looking for accuracy relative to the source material.
They're looking for code that does a specific thing, or food that tastes a certain way.
> If the scientific method is getting to the truth, why on Earth would you put an obstacle in front of it?
Possibly because they aren't doing science. They may be doing software development or baking instead.
Given that it hallucinates in particular over measurements/specs/stats, I'd be extremely sceptical of taking a recipe from it, whether that's generated and original or coming from a known source.
Baking requires very specific measurements, the slightest mistake and it won't turn out well in most cases. Again, why go via an LLM and not a search engine to the actual source? It makes zero sense, especially if it only returns text and you can't see what the recipe produces if it's an existing thing.
I believe the argument presented (possibly in a separate thread) was that search engines have degraded to the point where what they show you is worse than LLM output.
If you ask it what the facts are, it just gives you a load of nonsense.
Put another way: People who don't pick up these tools and learn how to be effective at them will increasingly be at a significant performance disadvantage from people at their skill level who do pick them up.
You're trusting that it is secure and sensible in its output too.
But even if it was just boilerplate, I'd have had to apply it, and that takes time to do. I've started dozens of projects over the 28 years in this industry - I have a very good idea of how long it takes both me and a typical developer to do what I've had it do, and it's far faster.
And no, I'm not trusting it to be "secure or sensible" at all, no more than I trust a developer. But overall the quality of what it has produced all of which I've reviewed the same way I would with code delivered by a developer, has overall been of good quality. That does not mean free of bugs, any more than human developers write flawless code on first try, but it does mean I've had it write cleaner code than a whole lot of people I've worked with who'd take many times as long and cost a hell of a lot more.
I would much rather have a "rubber ducky" that I can try and explain my thought process to, and then it can try to question me and poke holes in my thinking. I think my expectations on AI are pretty realistic and I don't really expect it to ever be ever *thinking* in the way I associate with the word, not with today's SoA at least. In that respect I'm just not particularly interested in it generating code, but that also may come down to our individual preferences for how we write code.
At any rate, my issue is that it fails at the "rubber ducky" position I mentioned earlier. It's not really able to follow a train of thought that I have, in a reasonably competent way, and every time I have tried to do work with it I just end up feeling silly for anthropomorphizing something that I know isn't really, even if for a second. Just my $0.02 though, I'm glad so many people seem to like it and am happy for them.
How do you know if they don't? Do you expect them to report back to a throwaway comment on HN? The sentiment is shifting on HN, that much is clear, just like it shifted with crypto.
> if it doesn't know well a person it simply makes up things
Asking it for factual information about a subject can be a bit hit/miss depending on the subject. Better to use bing chat, because it will use info from the web to inform the response
> if I ask code examples or scripts, most of the time they are wrong, I need to fix them, they contain obsolete syntax etc...
How wrong? More wrong than having a junior or mid level developer contributing code?
Think about it a different way: you just gained an assistant developer that writes mostly correct code in seconds. Big time saver.
Also: if you want it to use a particular code style etc, give it few shot examples.
> if I'm asking a question, I'm expecting being asked for more context if the subject is not clear
Then you need to tell it that in you prompt: "if the subject isn't clear, ask me some clarifying questions. Don't respond with your answer until I have answered your clarifying questions first". Or: "ask me 3 clarifying questions before answering" to force it to "consider" how well it "knows" the subject first.
ChatGPT isn't an AI in the sci fi sense of the word. It's a language model that needs to be prompted the right way to get the results you want. You will get a feel for that the more you use it.
The article compares verified responses on r/AskDocs (yeah, a subreddit) and those from ChatGPT. That's it. How is its coding compatibility even remotely relevant? It's like saying "Excel is bad in editing photos, so it must be a bad spreadsheet software as well."
This is disingenuous. OP is right. ChatGPT is mostly inaccurate and contextless by nature.
It always produces something confidently so it's very easy to think it's the right answer.
Now, it can be extremely useful for any task that can be easily verifiable and you don't know the syntax, how to approach something, etc. Because any decent software developer can use it as part of the prototyping or brainstorming and get wherever they want to go in coordination with ChatGPT.
What you can't do is assume it's "the expert". You are the expert and you're the intelligent one and the chat generates potential useful things.
That's on verifiable stuff. On other things it can be so laughably bad it's impressive how much "everyone" (as in articles, hype, HN users who downvote criticism as being from luddites) pushes it as something that is not: an AI that can think and be relied upon and any inacuracy will just "get better with time" aa opposed to "the model and also the politics around it" doesn't have a path to get to that idealisation that is being sold.
It's a tool, but it's usually sold as better than it is, like in this case, presumably with the intent of relying upon it to save cost in some key integration point. The problem is that it mostly won't work and comparing it to bad humans or worse integrations doesn't show the fundamental low ceiling that it has.
I think people with authoritarian mindsets (and I don't mind Left or right but inherent trust in authority) easily want this to be a source of truth they can magically use, but there's no path for that to be true. Just to appear true.
Yes. The failure mode of a human and chatgpt are nothing alike — I am far more experienced in spotting beginner mistakes in code reviews, then seemingly good, but actually illogical bullshit code generated by LLMs.
I have never had it produce non-trivial, novel code that was correct, so I mostly use it as a search engine instead.
Either that or the problems they're solving are nothing boilerplate couldn't handle.
- write a contract for the sale of my motorcycle: put all details, names and numbers with labels on a spreadsheet, paste on the chat and ask for a contract, then edit.
- learn french: I told gpt "when I write wrong stuff in french, always let me know and teach me the correct ways". Then, after a few weeks I asked for a .csv with the stuff that he corrected so I could import into Anki, which actually worked.
- coding on a daily basis: I am learning Rust on my new job, so I ask it things all the time, it helps me a lot.
I both use ChatGPT to boost productivity but also see the amount of mistakes it makes and will keep making and am surprised at the extreme denial of anyone who tries to shut down criticism of the wrong type of hype (the one that sells something that is not there)
Thanks for such a clear cut example for my points.
The point still remains: let's see what the LLM delivered that the user actually used.
If you know this, you don't need GPT.
If you don't know this, you don't have a way to assess GPT's attempts at a contract. A bill of sale is indeed simple, but there's a lot of more subtle legal issues someone might run into in life.
If, on the other hand you actually put together a prompt which tells it what you expect, the results are very different.
E.g. I've experimented with "co-writing" specs for small projects with it, and I'll start with a prompt of the type "As a software architect you will read the following spec. If anything is unclear you will ask for clarification. You will also offer suggestions for how to improve. If I've left "TODO" notes in the text you will suggest what to put there." and a lot more steps, but the key element is to 1) tell it what role it should assume - you wouldn't hire someone without telling them what their job is, 2) tell it what you expect in return, and what format you want it in if applicable, 3) if you want it to ask for clarifications, either ask for it and/or tell it to follow a back and forth conversational model instead of dumping a large / full answer on you.
The precise type of prompt you should use will depend greatly on the type of conversation you want to be able to have.
An intelligent agent shouldn't need this type of prompting, in my opinion.
There are many categories of usage for them, and relatively "dumb" completion and boilerplate is still hugely helpful. In fact, probably 3/4 of my use of ChatGPT are uses where I have a pretty good idea what it'll output for a given input and that is why I'm using it, because it saves me writing and adjusting boilerplate that it can produce faster. Most of the time I don't want it to be smart, I want it to reliably do almost the same as it it's done for me before, but adjusted to context in a predictable way (the reason I'll reach for it over e.g. copying something and adapting it manually).
We use far dumber agents all the time and still derive benefits from it. Sure it'd be nice if it gets smarter, but it's already saving me a tremendous amount of time.
It’s a save 15 minutes here, 20 minutes there kind of thing that can add up to hours saved over the course of a day.
One of my go-to examples is that I asked ChatGPT to write a DNS server. It didn't get it perfect and needed followup questions, but it 1) got it better than I could (having written a couple in the past) without spending time reading the RFCs, 2) because it's something I have done before, even though I'd have to look up docs to do it again, I could instantly tell which areas it doing the right thing, and where I needed to check specs and adjust details.
But if I didn't know how a DNS server works, on the other hand, it'd have been little help to do it that way because I'd have no idea what.it got right or not, and it's have been far more productive in that case to ask it for a step by step guide of what to do and which portions of the specs to look up.
You have to treat it as a junior:
Either give it tasks you can trivially validate and improve because you know the subject, or ask it to help condense down and explore the search space.
Don't try to get it to complete a task you don't understand how to quickly validate, because you'll waste tremendous amounts of time trying to figure out whether the answer is right.
Most of the people complaining seems to expect it to work well for the latter even when told it's not (yet, anyway) a good use.
Like I know ChatGPT-4 can general a bunch of code really quickly, but is coding in python so hard that it wouldn't just be easy to write some code? It's super neat that it can do what it does, but on the other hand, modern editors with language servers are super efficient too.
My point was that if you just ask it an ambiguous question it will return something that is a best guess. It's what it does. To get it to act the way the person above want it to, you need to feed it a suitable prompt first.
You don't need to write a new set of instructions every time. When I "co-write" specs with it, I cut and paste a prompt that I'm gradually refining as I see what works, and I get answers that fit the context I want. When I want it to spit out Systemd unit files, I cut and paste a prompt that works for that.
The stuff I'm using it for is stuff where I couldn't possibly produce what it spits out as productively not because it's hard but because even typing well above average typing speed I couldn't possibly type that fast.
Some times this presenting of problem to it means I spend anywhere from 5-10 mins actually writing the points down that describes the requirement - which would result in a working component/module (UI/backend).
We have been trialing GPT4 in my company and unfortunately almost everyone's experience is more on the lines of yours than mine. I know it shouldn't, but honestly it frustrates me a lot when I see people complain that it doesn't work :). It definitely works but it depends on the problem domain and inputs. Often people forget that it has no other context about the problem than just the input you are providing. It pays to be descriptive.
I only believe if they actually trained chatgpt on those type of tests specifically.
Not the actualy dynamic nature of dealing with patients & lawsuits.
NLP can recognize alt accounts of individuals on places like HN and reddit, but a person would probably need to study the comments pretty hard to determine the same thing, its not natural for people imo but it seems to be the foremost aspect of any kind of model that's processing human writing.
It's almost like "AI hacking people's brains" turned out to happen accidentally, and a huge number of supposedly smart people are getting turned into mindless enthusiasts by nothing more than computer generated bullshit.
E.g., lots of HN users claim to use it for dev or learning new programming languages. Given the frequency of hallucination and their Dunning-Kruger complexes in full-effect, they don't know when it's teaching bad information or functions that don't exist.
It's an LLM. Not an AGI.
If you ask it for boilerplate or for something that's a basic combination of things its seen before, it can give you something decent, possibly even useable as-is. But as soon as you step into more novel territory, forget it.
There was one case where I wanted it to add an async method to an interface as a way of seeing if it "understood" the limitations of covariant type parameters in C# with regards to Task<T>. It did not. I replied explaining the issue and it actually did come back with a solution, but it wasn't a good solution. I told it very specifically that I wanted it to instead create a second interface for holding the async method. It did that but made the original mistake despite my message about covariance still being within the context fed back in for generating this response. I corrected it again, but the output from that ended up being so stupid I stopped trying.
And at no point was it actually doing something that's very important when given tasks that are not precisely specified: ask me questions back. This seems equally likely to be a problem for one of these language models replacing a doctor. It doesn't request more context to better answer questions so the only way to know it needs more is if you already know enough to be able to recognize that the output doesn't make sense. It basically ends up working like a search engine that can't actually give you sources.
I find it very helpful to ask a series of questions and see a number of examples to get a primer on what to expect with something. The main benefit over Google or going straight to the docs is I can start with my specific requirements. I then dig into the documentation to deepen my understanding. I can typically move forward with ChatGPT generating some code as a starting point.
It can be incorrect or out of date but combined with my experience I find myself being more productive with it.
A weakness I see is complex code requirements. It knows what it knows.
I note that you seem a little frustrated with vague or incorrect responses. It helps to tell ChatGPT the role it should play. It helps as well to instruct it to ask questions of you to improve the response. Personally I prefer to tell it keep its answers brief, I get less walls of text and I can narrow in on the specific answer I am after more quickly.
For my experience is terrible for any question I ask: - if it doesn't know well a person it simply makes up things - if I ask code examples or scripts, most of the time they are wrong, I need to fix them, they contain obsolete syntax etc... - if I'm asking a question, I'm expecting being asked for more context if the subject is not clear, instead it starts spitting text without even realising I asked a completely different thing etc...
I could go on for hours with other examples, but I'm seriously not finding it useful