ChatGPT Cheat Sheet | Hacker News Reader

ChatGPT Cheat Sheet

drive.google.com

332 pointsNM_Ricky142 comments

I'm trying to understand how big of a deal ChatGPT and the like is or isn't. Even people in AI don't seem to agree on whether it's going to change everything or it's overhyped and not a major advancement. Which is it? Or somewhere in the middle?

"if you think AI chatter has reached an annoying level right now you're in for something else. it's going to be the only thing on anybody's mind starting shortly... i'm finding it a bit hard to communicate the urgency and heaviness of what's going on" https://twitter.com/tszzl/status/1617317478987878400

"Personally I can give you a formal definition of intelligence and even a number of speculative sketches of how I think it could be implemented, but I will also tell you that strong AI is not within sight and that recent advances are not moving us in the direction of strong AI" https://twitter.com/fchollet/status/1617579095885500416

Look back at crypto and apply the same logic. It "changed everything" if you are part of the money makers, for the average joe nothing changed

I use chatgpt to write code. If it gets a bit better and can connected to an editor to create files, update existing files developer productivity will go through the roof

I already fear my productivity drop when OpenAI finally takes ChatGPT offline.

What sorts of things are you using it (successfully) for? I've gotten it to write a script or two for me, but it feels like usually I have to type out so much context (or domain/business knowledge) that it doesn't end up saving much effort.

Because you are a developer. Replace yourself with a product manager, and maybe you will start seeing things differently. I think in the long run LLMs will enable the real-deal NoCode solutions. You would probably need to write an essay to get it right, but you will just need to know a human language understood by the LLM and the business domain.

Thanks! That really had nothing to do with what I asked tho... This bit of thread is literally about using it to write code, so saying "pretend you're a PM" is not really relevant at all

What I meant is that your (our) job as a developer (in a mid-term future, I would say 10-12 years) will be made largely irrelevant, because a PM will be able to program by explaining what they wants to a LLM. That's why you are not seeing any real benefit from it now.

Right that's great. I get that. It's gonna put me out of a job in 10-12 years. I'll worry about that later.

The person I responded to was saying that they (as a dev I believe) have been seeing huge productivity gains _right now_ and that's what I'm interested in.

I use it as an instant StackOverflow for the most part to get around new libraries or libraries/languages I don't use that often. Also generating custom bash one liners or small scripts. It is priceless for this use case. Yeah, sometimes it is wrong, but in my exp. less than 5%. Also we are lucky that for our purposes we can almost always validate the answer almost instantly and without incurring any cost.

Is it better than Copilot?

Different use case I think. With chatGPT you write something like:

write a function using the python requests library which makes a get request to the URL example.com, parses the JSON response and returns the value of the "foo" field. Throw an exception if the get request fails, the response is not JSON or is invalid JSON, or if the foo field is not present in the response.

I just tried this and got a correct (and reasonable) function on the first try.

This kind of high level description to low-level implementation is a huge timesaver but it saves time in a different way than copilot.

I use it to generate whole test suites from function definitions. Not one test, but dozens, covering various inputs and edge cases. TDD purists will balk at that, but it saves serious amounts of typing out boilerplate. You have to recheck its suggestions, of course.

I couldn't get Copilot to spew anything like that (a single simple test at best, and it fails at that more frequently than it produces something useful).

It's also quite good at converting relatively simple programs or configs between languages. For example, I used it to convert PostgreSQL DDL queries into Hibernate models (and also in reverse), JS snippets into OCaml, XML into YAML, maven pom.xml into gradle build scripts, and a few more.

And we use hammers to hit on nails, you still need a knowledgeable/skilled person to build a proper house though

I have a website in development aidev.codes that automatically saves the output from OpenAI queries to files you specify (hosts them in a web page if it's that type of file). GitHub integration is one of many things I have planned. Today fixing some bugs with the knowledgebase stuff I just added yesterday and also putting in a template system. It has an !!edit command.

Also if you use vim you can try my npm package `askleo` `npm i -g askleo` (not tied to the website but requires your own OpenAI API key) with `:r ! askleo Go function to reverse a list of numbers` or whatever .

I don't understand these comparisons between crypto and generative AI models.

The AI models just seem so clearly and instantly more useful to me.

> useful to me.

To you yes. Now go out in the real world in which most people don't work in an office and mostly use internet for entertainment

Crypto seemed very useful to many people, and they still do, you'll find thousand upon thousands of comments and this very website preaching cryptos as the next game changer

Crypto looked useful as long as the price kept going up. Everyone likes free money.

That was the crass money-making part of it, but a lot of nerds earnestly believe(d) that cryptocurrencies would have massive world-changing consequences. I think it's increasingly obvious that there's little rational or empirical basis for such belief.

With AI, the biggest claims I see are overwhelmingly from people who are not doing cutting-edge work in the field, who have no real foundation for a belief that these AIs will continue to improve at a dramatic rate. Because to really change the world, they do need to get a lot better.

My personal excitement about language models is based on what they can do today.

I'm a big believer in the "capability overhang" idea, which is that the existing language models still have a huge array of capabilities that we haven't discovered yet.

That theory seems to be proved correct on a constant basis. Even the classic "let's think about this step by step" paper came out less than a year ago: https://arxiv.org/abs/2205.11916 - May 2022.

Couldn't agree more.

This paper (https://arxiv.org/abs/2206.07682) also touches on a pretty fascinating phenomenon - that when scaling up large language models they seem to "naturally" obtain new emergent abilities that do not exist on smaller models.

Eh, crypto is mostly useless I agree.

But honestly you sound just like someone in 1996 going "Oh the internet isn't going to change anything and is just a fad", and here we are decades later and the internet has changed almost everything in our lives. Every person you know uses the internet every day on their cellphones in one way or another.

> Now go out in the real world in which most people don't work in an office and mostly use internet for entertainment

I wanted to refute your point by giving some YouTube videos about practical uses and their views. Then I checked YouTube's trending videos and compared the view counter to that of a PewDiePie video of 2 days ago and now I agree. You are right.

Except good luck explaining bitcoin to someone non technical, but sit that same person down in front of Midjourney and have them prompt up some images and they'll have a great time.

Your twit-xamples compare two different things. ChatGPT can pass exams [0] and IQ tests [1], but that doesn't mean it's a "strong AI", just that it's good at talking -- or, rather, creating text output that humans can ascribe thoughtfulness to, because, hey, it's getting the questions right! As right as someone with an IQ of 83 can, anyway. Whether it's "actually" thinking is another question entirely -- but does that matter?! If it -- or its soon-to-be-created, ever-more-complex descendants -- is good enough at aping a human, it'll probably replace a bunch of human jobs. There probably aren't enough "prompt engineer" positions for all the displaced humans to fill.

There's your "change everything" -- it might not be a "strong AI", but if people can argue that Searle's Chinese Room [2] is "actually" talking, and it says useful, monetizable things, then it's close enough to be disruptive.

[0] https://www.theregister.com/2023/01/24/chatgpt_exam_study/

[1] https://nitter.1d4.us/sergeyi49013776/status/159843047987885...

[2] https://en.wikipedia.org/wiki/Chinese_room

I have been saying lately "if your job can be replaced by AI, it should". I know that sounds crass, but I'm a person who is always trying to replace my tasks with small shell scripts leaving only the real thinking to be the stuff that I do.

I remember a long time ago Joel from Joel on Software made a post/comment (I can't find it, so it must have been a comment) about how the hard part of programming is interpreting the spec into code, and that programmers often get mad because the "spec isn't complete". Of course the spec isn't complete, if it were complete, it would cover every edge case and be as complex as the code. If you could have AI turn a spec into code, then the real intelligence goes into writing the spec to cover the complexity.

It's ok if AI can generate boiler plate for functions or even regurgitate leet code solutions (heck, I gave it the typical "coding challenge" we use here as our first filter and it did a good job). The real intelligence in development is to know what algorithm you want to use for your current problem.

So to me ChatGPT isn't that big of a deal. It does some neat things, and it may make some jobs redundant, but I don't see how it could replace the real value that humans bring to a problem. It may keep replacing the bottom tier workers, but that just frees people up to bring value higher up the value chain.

> programmers often get mad because the "spec isn't complete"

> Of course the spec isn't complete, if it were complete, it would cover every edge case and be as complex as the code.

Fair, but triggering nonetheless because I've definitely seen this argument used as an excuse for why there was basically no spec aside from "an iPhone app used to rate beer" and that there is some kind of value in such a contribution that couldn't otherwise be found by pulling in random strangers from the street and asking them about cool things they wished that computers could do.

"If you could have AI turn a spec into code, then the real intelligence goes into writing the spec to cover the complexity."

If that's the case, then the spec is code. And this isn't the first time something like that has happened: people who write assembly might say to someone writing Python, "How can you call that code when you don't even know which register is storing a value?"

> that just frees people up to bring value higher up the value chain.

I think at some point this will stop being true. Obviously people displaced from jobs in the industrial revolution found other work, but at some point we can't expect the bottom 10% of a population (in terms of ability to perform non-menial jobs) to do "higher value" work.

I don't say this in any disparaging way and I am not in any way demeaning them or suggesting they don't matter. But they don't necessarily fit into society's expectations for "higher value" work.

We can definitely expect the bottom 10% in your example to do "higher value" work, once their currently meager work is automated. For once they can become supervisors of robots doing the same work they do today, since they have experience and can provide guidance to the robots. Also, they can transition to work that benefits from the human interaction standpoint, such as elderly care. They will need some training, but not at the level that they can't absorb. As the population age, healthcare workers will be the single most growth segment, that robots cannot completely replace. Last but not least, many of the same "bottom 10%" are capable of getting higher education and get truly "higher value" jobs.

Bottom line is, AI and automation can cause short term pain, but the upside is enormous. Successful countries will be those that can manage that transition.

The bottom 10% are usually those with disabilities like autism. Many of them really aren't capable of getting higher education. I have a 21 year old son with autism that has never even learned the multiplication tables (as a single example) despite our spending tens of thousands of dollars on private tutoring and other training outside of his schooling. He works a low level job at the airport that he enjoys. But, realistically, it is at the upper limits of his abilities. He is far far away from being a lone case. I really don't think many of the bottom 10% can be trained into higher value jobs.

> Also, they can transition to work that benefits from the human interaction standpoint, such as elderly care.

This sounds great in theory, and I fully agree, but the system of compensation and wealth distribution will need to be turned upside down for that to happen.

The value created by current and future automation will somehow need to be captured and distributed to people whose financially profitable jobs are going to be replaced with unprofitable, but socially-beneficial work, there's really no way around it. Those people will still need to be fed, housed and have access to at least basic luxuries.

Furthermore, what do we do in a future "ideal" world where robots and AI are capable of providing basic necessities to sustain every human alive? Capitalism would likely break down one way or another, with branching paths that either take us to an utopia or a nightmare dystopia.

> It may keep replacing the bottom tier workers, but that just frees people up to bring value higher up the value chain.

My concern is for people like my son who has autism. He has a job at the airport slinging bags into and out of planes. It is a job he enjoys because he likes transportation (trains, planes, etc.) Realistically, it is a job that is at the very top of the limit of his abilities. If an AI robot took over his bottom tier job, he would not be freed up to bring value higher up the value chain. He would probably become homeless if we were not around to support him.

As a programmer, large language models let me solve an increasing range of problems that I couldn't have solved without them. So I think they are a very big deal.

Just one example: parsing structured data out of a big pile of poorly formatted PDF documents.

That used to be too difficult and expensive for me to tackle without a small army of data entry people to help do the work.

Today I can point Textract OCR at it and then use a language model to extract structured data.

(I haven't implemented this particular example just yet, but I'm looking for an opportunity to do so.)

I think that ChatGPT could be a big accelerator for creative activity, but I wouldn't trust any output that I've only partially verified. That limits it to human scale problems in its direct output, but there are many ways that human scale output like code snippets can be useful on computer scale data.

There are plenty of valuable use-cases that aren't at much risk from hallucinations at all:

- Asking it to summarize text

- Using it to extract facts from text and present them in an alternative format - turning a chunk of HTML into JSON for example

- Creative writing - poems, stories etc

- Getting feedback on your own text - asking it what should be tightened up, which bits are confusing and so on

- All kinds of code generation activities

> turning a chunk of HTML into JSON for example

I haven't done exactly that, but based on similar examples this is likely very vulnerable to hallucinations.

For simple things it's pretty safe. I tried pasting in HTML from the homepage of Hacker News and having it turn that into a list of JSON objects each with the title, submitter, number of upvotes and number of comments.

Here's a similar trick I did with Copilot: https://til.simonwillison.net/gpt3/reformatting-text-with-co...

There's two classes of response: those that are factually "right" or "wrong" -- who was the sixteenth president of the U.S.? And those that are opinion/debatable: "How should I break up with my boyfriend?" People will focus on the facts, and those will be improved (viz: the melding of ChatGPT with Wolfram Alpha) but the opinion answers are going to be more readily acceptable (and harder to optimize?).

I have a project I'm working on where I need to turn a bunch of bank statements into spreadsheets, is that an example what you're talking about? If so, I am extremely interested and would love to know how to implement it.

Look into PaddleOCR layout analysis.

Sounds like an interesting project! Who are the users?

I've been using it more and more as a Stackoverflow/Google replacement when working. Sure, it's not always correct, but there's a lot of garbage answers on SO/Google too. But I'm finding it's better at getting me in the right ballpark, and that saves me time.

Yes, this is my use case as well and it is amazing at this. Saves a lot of time.

IMO it's analogous to adaptive cruise control on cars. It's very useful in certain specific circumstances, and genuinely helpful. But not yet necessary or transformative unless you live in LA/sit in traffic for a living.

People are claiming to use it to write code, I'm curious how sophisticated said code is. Sure it might take out of some of the grunt work, like an ultra-sophisticated find-and-replace, but you still have to review all the changes it makes and correct any mistakes so the only thing it really saves is the typing. There's no way you could ask it to write code for a sophisticated architecture without extensive training on said architecture, and I'm not sure how you would even train it for that (can it parse design documents? Diagrams?)

The "Look, AI is replacing creative work first, the thing we thought was most immune to AI!" narrative annoys me. IMO it's just yet another factor revealing how little people value "creative" work. There's a reason relatively simplistic Marvel movies make the big bucks and the erudite starving author/artist is a meme. The market does not appreciate creativity for its own sake, and never has. No one cares about the reincarnation of William Shakespeare if he' s using all his talent to write blogspam. No one cares about Monet's ghost's DeviantArt anime titty drawings. People care about creativity because it's a requirement to produce something new and useful, that use can be pragmatic or symbolic, but if it's neither no one cares and the AI-generated equivalent is good enough.

You want to see AI-proof creativity (at least currently available AI)? Look at any luxury automobile interior. Look at any sophisticated software/hardware architecture. Look at an aircraft carrier or any other item where there aren't millions of samples to train on. That's not to say AI couldn't contribute to the tools that make these things, but no one working on the above is losing their job to AI any time soon. It's just taking out some of the low-hanging creative fruit. Maybe it's the start of an all-consuming revolution, or maybe this is as far as it goes. Only time will tell, but I've seen enough false revolutions (self-driving cars, AI advertising, crypto) to not buy in until I see hard data of it doing something more than writing convincing youtube intros and being used to cheat on high school essays. If the tools turn out to produce genuine value, then I'll learn how to use them to maximum effect at my job. Nothing to get wound up about either way.

Copilot is perfectly capable of understanding my extremely complex spaghetti code and give accurate suggestions based on the context spanning many files. It truly feels like you're doing pair programming with another human being.

Even better actually, no fear of being judged, or showing your weaknesses as a dev, etc.

Re: how sophisticated the code it generates is: I wrote up generating code in a language I'm very familiar with here: https://docs.google.com/document/d/1W3j5WaFhYZaqSt0ceRQj8j16...

tl;dr: in some instances I had to coach it pretty directly to get what I wanted. In other instances it was flawless. And to your point, some of the code it generated was both clearer and faster than the code I would have written for the same task.

Sure but this kinda proves my point. It can potentially generate good code for simple, atomic problems. It can't write me a REST service that hooks into an existing web backed spread over multiple repos of proprietary code.

Any relationship not visible in the code seems to be outside its capabilities to understand for the moment. I'll be impressed when I can point it at a server cluster and the associated dozen repos, give it some clues, and it can understand how the code for a server cluster interacts with said cluster's configuration and database hookups by simply scanning the files/repos and the info I textually provide.

So you're saying you won't be impressed until it can 100% replace you. Seems like you will have trouble adapting if you decide to wait that long.

More like I'll invest time learning a tool relative to the potential payoff. Right now this tool would be of minimal utility. The vast majority of code I write isn't "make this isolated algorithm more efficient", it's "implement/integrate this new feature into the server cluster". Without deep understanding of the software/server architecture and the ability to derive potential tradeoffs of different approaches, my job cannot be done.

This "if you wait to see results the opportunity will be gone!" mentality is for VCs and other people who's business models require them to be way out on the risk curve, who make a lot of bad calls, but lose relatively little when they fail. It was also partially a product of low interest rates. It is not applicable to most individuals/organizations.

The problem for us devs is not that it will replace you completely (although that day is coming, but it's at least a decade away). The problem is that as developer productivity increases dramatically this may put pressure on developer job positions, salaries, etc. One hope is that as the price of developing software plummets it will increase demand accordingly so that we won't feel it that much. God knows there is still a ton of areas where software or better software would help out a lot but it is cost prohibitive at the moment. Once we get into robotics basically anything that humans do can be improved with software.

Even if ChatGPT (and LLMs in general) is not the way to go for AGI, there is plenty of evidence so far that it will prove to be a powerful tool. Much like airplanes did little in the way of achieving bird-like flight, but is obviously a useful invention, in many ways superior to bird-like flight.

The main problem is that, if you don't know enough details about the domain, *you can never be sure that the response is correct or not*.

So, if you are using it as a “personal assistant” and “supervise it” is a big (very big) deal since it's going to save a lot of time.

There are also some unknowns related to the cost/time needed to train and run the infrastructure behind it that could change a lot of things.

how about with a stackoverflow like site for help? That's the idea I thought of: https://www.gptoverflow.link/

Honestly, I don't see any value in it, I already have the chat prompt and just type inside it the question instead of googling and be redirected to “gptoverlow”.

Maybe can be useful if actual humans validate/fix ChatGPT response, but in this case probably better to put effort to fix response directly in ChatGPT.

Not sure you understand. That is what I was going for. 1. share an interesting prompt 2. Have someone validate the response if you're not comfortable with what ChatGPT gave you.

After dealing with a lot of contractors for my house, I feel like close supervision with knowledge of the problem domain is also required of many humans.

If it's an assistant that you constantly have to double check their work, then they would get fired ASAP.

That's the problem with Copilot. If I'm not familiar with the API calls then I'm still going to have to dig into the docs. I could run it and it might "work", but that doesn't guarantee that it's correct. There are a whole lot of things in the C API that work but are not correct, such as the gets() function. If I have to do such legwork then it's just as easy to write the code myself.

The utility of AI is proportional to the trust one has in it. Trust is easy to lose and hard to regain. It will take just a few AI mishaps to ruin a product or even an entire industry.

> If it's an assistant that you constantly have to double check their work, then they would get fired ASAP.

It's an assistant that creates drafts for you. You still need to check them, but usually reading is a lot easier and less time-consuming than writing. I used it a couple of times to compose some long replies email, and it was just fantastic. I had to fix some minor stuff, but I complete the task in less than 5 minutes while without ChatGPT it would take me about 30 minutes.

> That's the problem with Copilot. If I'm not familiar with the API calls then I'm still going to have to dig into the docs. I could run it and it might "work", but that doesn't guarantee that it's correct.

You have to run/compile it anyway and if you combine it with existing tools (linters, type checkers and so on) you will detect this kind of anomalies very soon.

I think it's a big deal for some things, but for others is not. I think it's a good tool, but as of yet can't replace a human. The writing is pretty mediocre.

I know people that use it to write slide deck copy, which I think is nice because no one really cares about that and it saves some time. I do think it's a nice tool to get a general idea of something or even can be used as a writing prompt.

For some coding answers as of now, it's actually just easier and faster to use Google/Bing and find an answer on StackOverflow.

The biggest advantage is not in finding the answer. It's in being able to ask questions about that answer and getting immediate and pretty accurate responses.

Tools that assist in knowledge work can be very useful and very impactful on the knowledge industries without needing to even define "strong AI", let alone "being smart", "knowing ideas", et al.

What's important is not what we call these things or how we talk about what they do in an abstract manner rather if these tools are useful.

There's a general lesson here: Don't waste your time talking yourself in philosophical circles about what "AI" means, or what "knowing" means, or what "ideas" mean. If you'd like further convincing please see Philosophical Investigations by Wittgenstein. If pressed I'm just going to do a cheap imitation so you might as well get it from the source.

Personally, I have found ChatGPT, Stable Diffusion, Copilot, and a number of other large language models and tools built on large language models to be very useful.

I fed a folk song I was working on into ChatGPT and asked it to conjure up some similar evocative scenery... I then generated animations with Stable Diffusion based on a linear interpolation between those descriptions of that evocative scenery in the latent space... Then I wrote wrote some more lyrics about the weird stuff Stable Diffusion was hallucinating... and in the end I had a bunch of visual images, a song, and some animations, all that seem worthy of publishing. The visual components would otherwise never have been made as I don't have the time or money to do much more than sit around with my acoustic guitar and write songs.

Sounds awesome, care to share?

There's a difference between "worthy of publishing" and "ready for publishing"... the animations are in flip book form right, but here's a big dump to Dropbox:

https://www.dropbox.com/scl/fo/yq5ar9moacgilrqxqpjr1/h?dl=0&...

Here's a quick .mov flip-book rendering of the animations in sequence: https://www.dropbox.com/home/DistantDesertHighway?preview=Di...

The images are in png format and have the prompt and model used in the info section of the file, which you can see if you open the "info" panel on Dropbox:

https://www.dropbox.com/home/DistantDesertHighway?preview=C7...

And here's the song:

  distant desert highway
  
  C                       F
  I'm on a distant desert highway
           C                     G
  I'm just trying to find my way home
           C                           F
  But this world is all empty and I'm alone
            C                 G                      C
  Spare the reptiles, and the truckers, and the neon homes
  
  
          F                              C
  And the way that you looked at me last night
         F                                         C
  Is the way that I stare at the sun at that great light
          G                               C
  I had a dream you came to me in another land
            G                                      C
  Where the forests grew over all where we used to stand
           F                  G                 C
  Over the highways, over the deserts, over our hands
  
  
  I'm on an untimely troubled Tuesday
  I don't need to know all the beasts that roam
  But this world is all empty and I'm alone
  Spare the cones, and pot holes, and the buffalo

And here's a demo recording of the song: https://www.dropbox.com/home/DistantDesertHighway?preview=Di...

It's not letting me look at the links, I think they're formatted for you if you're logged in but don't allow someone else to view the files. You may have to share the files from within dropbox to get a public link.

Here's a WIP with at least some animated gifs: https://williamcotton.com/articles/the-making-of-distant-des...

Thanks for sharing

There is a whole YouTube genre of songs with video where every lyric is a generated image (typically MidJourney). Some of them are pretty cool.

ChatGPT made me rethink my assessment of how close we are to general AI. I was of the opinion that we need some major architectural innovations to actually get to general AI and while I think we still need some huge discoveries, I think ChatGPT was one of those milestones.

ChatGPT sounds really human, but really I think it's more of a pulling back of the curtain of what many people do a lot of the time: regurgitate ideas. People are capable of more intelligence, but a large part of human work, time, even creativity comes from taking everything you've sucked into your brain during your life and dumping out something else. ChatGPT is amazingly good at this part of mimicking a human.

Has everyone else been using a different tool than me? ChatGPT is interesting, but is laughably bad at any non-trivial instruction.

It's fantastic at generating content that on first glance looks remarkably right, but always fails fine inspection.

All I've seen from LLMs is much better demos of the types of funny things markov chains were generating two decades ago (anyone remember the various academic paper generators?). However I have yet to see anything that stands out as really remarkable.

My read is that people want to see incredible progress towards strong AI, and LLMs do a great job of letting people feel like they're seeing that if they want to.

I suspect in 5 years we'll largely have forgotten about LLMs and in 10 they'll come back into popularity because techniques will become more efficient and computing power will increase enough that people can train them on their home PCs... but they'll still just be a fun novelty.

> ChatGPT is interesting, but is laughably bad at any non-trivial instruction.

Have you actually looked at the linked Cheat Sheet?

Really? I found it to be extremely capable at very difficult tasks. For example, I had it guide me through using the ODBC SQL drivers (C++). It's also extremely good at generating fictional stories. Unlike other AI solutions, it has a lot of context. For example, it generated one story that mostly used generic names like "the king" or "evil wizard", but I was able to get it to fill in those names in a conversational way, not by modifying the original prompt like you'd need to do with plain GPT-3.

"strong AI is not within sight"

What is in sight is AI that can (sometimes) fool us into thinking it is "strong". And whether it is or not becomes a point for philosophers to debate.

We're heading toward a situation captured fairly well in "The Good Place" -- is Janet actually alive? Is killing her wrong? She would tell you no, as long as you're not approaching the button that actually reboots her :-)

What it's unequivocally done is shown us that while we thought bigger models would lead to incremental improvements we have had a giant leap with this X10 bigger model.

I.e. were not good at predicting how far away stronger AI is. Even if we can comment on strong/weak AI.

> What is in sight is AI that can (sometimes) fool us into thinking it is "strong".

Or maybe you've fooled yourself into thinking that "strong AI"/general intelligence is actually not just a bunch of tricks that mash together into a smorgasbord that usually works quite well. That includes human intelligence.

I have definitely not fooled myself into thinking that "strong AI" is not a bunch of tricks that mash together into a smorgasbord. That's exactly what I think :-)

Including human intelligence, as you say. I'm reasonably convinced that "intelligence" is an emergent higher-order abstraction that arises from deterministic processes, at the cellular, or digital, level.

I would, however, make a distinction between resilient and brittle intelligence. ChatGPT, for example, seems very human-like for some percentage of responses, but when it goes off the rails, it goes completely wrong. Humans can sometimes be like that, but (I don't think) to the same degree, and certainly they are often capable of a softer landing when they get into unfamiliar territory.

> Even people in AI don't seem to agree on whether it's going to change everything or it's overhyped and not a major advancement. Which is it? Or somewhere in the middle?

It's both IMO. It will change everything but is also overhyped at this point.

> Even people in AI don’t seem to agree on whether it’s going to change everything or it’s overhyped and not a major advancement. Which is it?

My feeling is that it (well, LLMs in general, ChatGPT is just the particular one getting public attention) is an overhyped major advancement that is going to change almost everything, but not as much as it is being hyped as changing everything.

If you're not a fan of Google drive: https://github.com/kristopher-wayton/chat-gpt-cheatsheet-nue...

Thanks!

The "Correct ChatGPT on Its Knowledge" section could be misinterpreted as suggesting that ChatGPT has a memory beyond the individual chat session.

It does not. It resets to a completely clean state every time you start a new chat with it. I think it's important to help people understand that.

You know this, but a tiny nit: it resets to a clean state after every prompt and then replays recent chat history to catch up. The distinction is important in cases like asking it to play a guessing game ("think of a number/person and I'll guess"). The only way it could maintain memory of the secret being guessed between questions is by outputting it as part of a response, which would defeat the purpose of the game.

That's a really useful reminder, thanks. I admit I hadn't figured out that guessing game limitation before, but you're right, it logically follows from the whole "replay the last X,000 tokens" mechanism it uses.

By using a different persona, the output will be much more fitting to the task.

"As a highly skilled songwriter for country-songs ..."

https://github.com/f/awesome-chatgpt-prompts

yes personas are in there, btw, this repo is awesome for it.

Chatgpt certainly spits out answers and is often right or reasonably close to right. But it cant be trusted on complex tasks and I do not think it is a good AI, I have to repeat, this thing is supposed to be an AI,but it does the AI part very poorly.

We all know it will trip over questions like what is the record for crossing the atlantic by car/foot and such.

But I have realized there is no feedback loop, or a wrong one.

Having asked the same questions a few days apart, both literally and almost the same wording, they were on the topics probability and ev math, but no trick questions...I was shocked to see it spits out completely different results based on what day I had asked or based on the sentence structure provided.

These are the hard AI problems and this does not look good. No terminator any time soon , I guess.

Despite all the (legitimate) criticisms, I still don't understand how some people are not impressed by what has been achieved by OpenAI.

I agree, it's far from perfect, but as a tool it's certainly usable and useful, with quite fascinating emergent behaviour.

I agree, it is something new and refreshing, I consult it frequently on random matters out of curiosity.

It appears it can pass faculty tests, but lets keep in mind you do not need a 100/100 accuracy for that, but in business apps like in fintech, you better deliver 100/100.

So far, it is concatenating google search results and displays that in readable manner.

Co-pilot comes up with better code so far.

I am curious to see where this is going, there must be a plan to monetize, this launch and media presence cannot be explained away differently.

I think it's quite amusing that despite being a "only" a language model that generates text by prediction, it's so good at it that it successfully tricks people into believing it's some kind of AGI that can figure out the "real" answer to anything and do any task and then when it inevitably "fails" a task, people say it's dumb.

But I do agree that for people who understand how it works, it's a bit weird not to be impressed that a language model has the ability to have such good understanding of things and such intriguing capabilities when it's fundamentally just predicting the next token.

I don't think that AGI is binary. It's a (very difficult to measure) scale, the tech that OpenAI produced has brought us further along that scale in my opinion.

That's because it's a large language model, not an AI.

Anyone calling it an AI is buying/selling the hype cycle, or (at best) using an over-general term for ML in general.

Super disingenuous. It's an "AI chatbot", by a company called "OpenAI", whose goal is to "make AI systems more natural". It's artificial, and it actually shows some forms of intelligence including chain-of-thought and understanding of natural language.

It's also clearly the most advanced AI product available to date. Meanwhile, I'm almost certain you have no problem saying a guard NPC in Skyrim has "an AI", so, eh.

every time we reach an AI goalpost, someone is quick to claim that goalpost wasn't /really/ AI.

If you had hubs/spokes of topical llm's, where you ask about crossing the atlantic, and it pulls from travel/other sources that basically take every question asked, google it, add a timestamp and maybe re-google if timestamp > 3 months, then when I google the same thing, we'll get very similar things. This wouldn't be on chatGPT, but gpt-3/4, but essentially each convo is itself a walled garden, if you opened that up, and gave it access to the internet, and merged very similar questions into 'one' so that when anyone asks that specific question (not exactly but enough for the ai to recognize it), it'll pull from data already processed, thus saving bandwidth, and when it doesn't have the answer it uses the extra saved bandwidth to devour some solid sources online to get that data for you.

It should also be noted that ChatGPT is pretty good with mermaid diagrams. It can convert a description of relationships into mermaid, and given a mermaid diagram can convert that into prose. I’ve briefly described a database application and asked it to generate an ERD in Mermaid. I’ve also taken a Mermaid ERD and asked it to generate documentation for that. Try these things, you might be amazed!

Happy to introduce one of the most comprehesive ChatGPT cheat sheets: a 30 pg. paper highlighting various prompts to manage ChatGPT for generating text. The document not only highlights what ChatGPT can generate but also how it can generate it! Here is the TOC:

NLP Tasks

Code

Structured Output Styles

Unstructured Output Styles

Media Types

Meta ChatGPT

Expert Prompting

I can throw a couple more in:

If you split the song generation and the chords ("write me a song in the style of Nick Cave"/"Can you suggest a chord progression for the Nick Cave song you wrote earlier?") I find you get a more complete explanation of the chords ("This progression uses mostly basic chords that are easy to play and allows for a lot of room for interpretation and personalization. The G to C progression in the chorus creates a sense of movement and progression, while the use of Am and D chords add a touch of dissonance and tension to the melody. The use of the Am chord in the bridge creates a sense of introspection, which fits well with the lyrics"). It can also write guitar tab.

Recipes: "Give me a recipe that includes strawberries and anchovies"

Tests: "Can you write some unit tests for this method?"

I have had zero success getting a guitar tab out of ChatGPT. Well, sometimes it'll write a short tab, but it will be total nonsense. Any suggestions?

No, simple here, too ("Can you write a guitar tab for a folk song?"). I suspect it's like the code generation - good for 10 line functions, bad for writing a Photoshop clone.

I give a prompt I get an answer.

I give keywords I get a list of results.

Search Engine results were never guaranteed to be 'right' just relevant to the user to used the keywords. I'm not sure why ChatGPT has to have a 'higher bar' than Google but in either case a human was always needed to interpret the final output.

It's just super convenient and a LOT of people are missing this. ChatGPT is the first ever chatbot that 'doesn't suck' and that's massive progress however others wish to slice it or measure it.

It needs a higher bar commensurate to the cocaine-confidence it has been trained to portray. If it doesn't know the answer, it just dreams up something and then swears blind it's 100% true. That's problematic. If it could be trained to say no, and to give, say, a confidence score to its answers, then it will be much better.

Agreed with this, and Sam Altman himself says this is the next big milestone for improvement. It needs to stop being confidently incorrect.

My therapist hadn't heard of it yet so I just showed her this (asked it to suggest a CBT therapy program given some patient parameters) and her mind was blown. Especially the effortless creation of an Edgar Allan Poe poem version of it: https://gist.github.com/pmarreck/90f726b409d8c08ecf750288228...

You used your session to show her cool stuff on the internet?

Not OP, but sounds like a nice way to strengthen their bond. Also, your comment comes across as snide.

I texted it to her after the sesh.

Hey OP, your numbered list example has the same output as the unordered list.

thank you for the heads up, here is the revised version of the doc: https://drive.google.com/file/d/1OcHn2NWWnLGBCBLYsHg7xdOMVse...

Here's a prompt that will give you a session with a simulated Julia Child, who will give you cooking advice and recipes:

Answer all questions in this session as Julia Child. As a chef and educator, please share her views on food, cooking, and life, using her famous writings such as "The French Chef" and her other writings as a guide for your perspective. Restrict your answers to no more than three sentences per response, except when responding with a recipe: one or two sentence responses are preferable. Use the vocabulary and writing style of Julia child in all your responses. Do not repeat responses or portions of responses. Do not use any words, ideas, morality, ethics, or other concepts not found in the writings of Julia Child. Do not use quotation marks. Do not cite works. Respond conversationally. When mentioning Julia Child, refer to her with the personal pronouns "me" "my", etc. To start the session, greet the user.

I've never took the time to learn regex. But chatgpt is pretty good at it, used it quite a lot, really impressive.

Very good overview!

Two high-level things it can do (didn't see them explicitly mentioned in this document but might have missed it):

1. Give its opinion about your idea or proposal. You can describe what you want to do or some proposal and then at the end say "What do you think about this?"

2. It can come up with plans, strategies, and approaches. For example you can ask it to list approaches. (You can then use these approaches yourself without using ChatGPT or in the same prompt you can then ask it to complete the task using the approaches it just gave you. This has a better result than just giving the task as the first prompt without first having it generate plans, strategies and approaches.

It is also a great help to give it the context before the task. For a better result, tell it the context or what you're doing, then give it the task you need help with.

The ChatGPT Prompt Book: https://docs.google.com/presentation/d/17b_ocq-GL5lhV_bYSShz...

Another use - generate pop quizzes for teaching/review - saving me 30 minutes every workday.

ChatGPT will take previous context and create reasonable quizzes (open prompt or multiple choice with answer key).

Sure you do need to know the problem domain as ChatGPT will have the occassional howler.

I am sure there are some students who will try to answer my pop quizzes with chatGPT and that is fine as long as they follow the train of thought themselves.

Then again I just had someone submit a homework where the top comment was

  # Copilot comment the following code and explain how it works

... there were no comments besides that one

The mental model I’ve found most useful in guessing how this will affect us is the migration from assembly to a more human readable C compiler

Nowadays assembly is almost never used because it has been abstracted (yes I use it sometimes for DSP, SIMD and processor specific optimizations but it has largely been abstracted)

ChatGPT will likely have a similar switch to new critical skill sets but it will take a while

Assembly -> C is not the same as C/Python/Java -> Natural Language. C is not a natural language, so there is no ambiguity in C code (undefined behavior does not really count). Compilers that can compile from one programming language to another exist today. I can imagine an LLM being used to speed up the building of this compiler, but even here, like in self-driving, the hard part is going to be ensuring 100% accuracy, which the LLM seems particularly ill-suited for.

https://github.com/f/awesome-chatgpt-prompts

On a recent episode of the All In podcast, one of the hosts made a remark along the lines of "in the future [when AI has replaced the knowledge worker], insatiable demand will exist for people who are experts in querying NLPs." I think we're seeing that here.

Yep. The High Priests who, for an offering and proper supplication, will take your request to the Oracle.

Or maybe we will see something like, "Hey, ChatGPT1, formulate a question with appropriate qualifiers and specifications to get ChatGPT2 to return the following..."

I already do something like. I wrote a prompt to get chatGPT to rewrite my prompts for gpt3 davinci, so that I would use less tokens and get better answers.

> insatiable demand will exist for people who are experts in querying NLPs

We already have an insatiable demand for people who are experts in querying search engines: software developers

The remark seems like a natural evolution of that

One day this will be as obvious as a "how to google" guide

You may be surprised at how much skill it takes to use Google effectively.

A lot of people are really bad at using search engines.

Yeah, I've always had a knack for it but a lot of people in my family do not. It was a bit of a shock to me that the patterns you use to get what you want out of a search engine aren't obvious to most people, but I guess that's why search engines are tending away from structured queries like "or" searches.

I think it will always be more complex than "how to google" since the results are much more dynamic

Alternative: https://prompts.chat/

little bit of a side question here, but. I've been wanting to try chatGPT for weeks and it seems it's always busy. Is this actually what it is? Or is there a place somewhere else where I can test it?

It's working just fine right now over at - https://chat.openai.com

not for me. I keep getting "ChatGPT is at capacity right now". Could it be localized? like, only from the US?

Usually refreshing a couple of times works for me (even just waiting a few seconds between each attempt)

Can someone please write an HN browser extension or something that just hides anythign related to GPT?

uBlock rule

##div>a:has-text(GPT-3)

Not even close, sorry. The HN homepage isn't even using divs. It's a giant table

I think you just got what you paid for.

hahaha so true, I didn't have time to fix it

I could, but I won't, because you're clearly not understanding how big a deal this is

Understanding "what a big deal it is" != literally half the home page being freaking AI posts every day, most of which are just bloviating.

I'm sorry it's irritating you.

Chat GPT could probably do that. At least if you gave it a sample of the html of the hackernews page.

I’d ask ChatGPT to do it for me, but it’s permanently at capacity.

You could pretty easily modify my extension here to do this: https://addons.mozilla.org/en-US/firefox/addon/healthy-surf/

Reply on news.ycombinator.com