I used ChatGPT for my work as a writer, and it's pretty nice.
I wouldn't let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.
Generative AI and The Future of Work
21–30 of 106 posts
Re: Generative AI and The Future of Work
#22Earlier quoted context omitted.
I've been asking friends in non-programming engineering fields how ChatGPT does in their area of expertise, and I believe programming is the area that ChatGPT is the most accurate. Finding solution to general engineering problems seems blatantly wrong in almost all cases, whereas in programming, it seems to be able to generate mostly correct code for simple, boiler-plate like tasks.
yes, but why? Why is GPT so much better at programming than other tasks? can it be that programming itself can be so easily predicted in a generative way, while others require more ingenuity and real world model to be solved? In this case I would totally offload programming to a GPT /LLM AI, while my job is simply to specify largely the business case.
Is it because programming is a more limited and specific language than the ones people speak? There's less room for double-meanings, slang, meaning, or even sentence structure.
Re: Generative AI and The Future of Work
#23> The results are often wildly creative and spookily accurate, giving these models a human-like feel. or wildly inaccurate, particularly in fields such as programming
Yup. What seems to be largely missed is that these models have zero understanding, and are actually destroyers of information, not creators. In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance. This highly averaged output is very recognizable and so very striking, but it is not a…
> In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance.
Forgive me, but isn't this kind of moving-the-goalposts? Information is the surprise value from the recipient's point of view, which meas the recipient's bayesian prior probability is "expected". Saying "these "AI" systems put out the most expected subset in each instance" assumes that the recipient's priors exactly equal those of the model which would only be the case when the model is talking to itself. (or I suppose to an even more complex model with perfect knowledge of ChatGPT's weights)
The fact that no information is transferred when the model talks to itself should not be surprising and would apply to any AI. (even including a superhuman post-singularity god-like AI)
Re: Generative AI and The Future of Work
#24Earlier quoted context omitted.
I've been asking friends in non-programming engineering fields how ChatGPT does in their area of expertise, and I believe programming is the area that ChatGPT is the most accurate. Finding solution to general engineering problems seems blatantly wrong in almost all cases, whereas in programming, it seems to be able to generate mostly correct code for simple, boiler-plate like tasks.
yes, but why? Why is GPT so much better at programming than other tasks? can it be that programming itself can be so easily predicted in a generative way, while others require more ingenuity and real world model to be solved? In this case I would totally offload programming to a GPT /LLM AI, while my job is simply to specify largely the business case.
I have to imagine its because so much of its training data is readily available programming docs, tutorials, and general Q&A that there is an amazing abundance of online. How many times have you just pasted an error into google and hoped someone else has asked the exact same question on stack overflow?
Re: Generative AI and The Future of Work
#25I used ChatGPT for my work as a writer, and it's pretty nice. I wouldn't let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.
Can you elaborate more on your process, and the venue/focus of the writing?
Re: Generative AI and The Future of Work
#26ChatGPT help me solve a refactoring bug today. I had spent hours messing around trying to figure out what the issue was until I realized, via asking ChatGPT, that I had misunderstood a piece of the code and the docs. It was able to answer and provide examples (until it had error and crashed) in a way a senior engineer might have been able to. The funny thing is I had tried just pasting in code and saying "find the bu…
That works for a really small amount of code (like 100 lines).
Re: Generative AI and The Future of Work
#27Earlier quoted context omitted.
"but the actual number of novel spreadsheet applications remains small." That's not my experience, I am continuously amazed by the amount of tasks worker bees manage to do in excel. I kind of wish MS access was more of a thing, because when eventually it doesn't scale and you need a "proper" system, it takes a rewrite.
That's fair enough, I've seen some pretty cool things in spreadsheet software too. My larger point, though, is that most people end up using spreadsheets to do the same thing. It's fun to imagine novel uses for a spreadsheet, like a DAW or video game, but ultimately it's not very useful for that. Similarly, ChatGPT is great for writing convincing text - that's what everyone uses it for. Can it solve math though? Not…
I think we knew from the get-go that spreadsheets would be used for pretty much anything to do with numbers. That there aren't any new applications past that understates their general applicability.
I agree though, chatGPT isn't a real flying car. Imagine if someone revolutionized the paper clip. The day-to-day of millions would be forever and irrevocably changed; and almost nothing would happen.
Re: Generative AI and The Future of Work
#28Earlier quoted context omitted.
I've been asking friends in non-programming engineering fields how ChatGPT does in their area of expertise, and I believe programming is the area that ChatGPT is the most accurate. Finding solution to general engineering problems seems blatantly wrong in almost all cases, whereas in programming, it seems to be able to generate mostly correct code for simple, boiler-plate like tasks.
why is "mostly correct" ok for programming? also, i don't believe that good programmers want to have boiler-plate in their code.
Because that's what most programmers achieve too.
You can iterate from there by taking advantage of the last 50 years of software engineering wisdom.
Re: Generative AI and The Future of Work
#29Earlier quoted context omitted.
yes, but why? Why is GPT so much better at programming than other tasks? can it be that programming itself can be so easily predicted in a generative way, while others require more ingenuity and real world model to be solved? In this case I would totally offload programming to a GPT /LLM AI, while my job is simply to specify largely the business case.
I have to imagine its because so much of its training data is readily available programming docs, tutorials, and general Q&A that there is an amazing abundance of online. How many times have you just pasted an error into google and hoped someone else has asked the exact same question on stack overflow?
True. Also there's a lot of commented open-source code out there.
Re: Generative AI and The Future of Work
#30I used ChatGPT for my work as a writer, and it's pretty nice. I wouldn't let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.
Can you elaborate more on your process, and the venue/focus of the writing?
I write educational technical articles for a living. Dev tools, frameworks, security, APIs, infrastructure, web3, etc.
I talk to the AI as if I would interview an expert on a subject matter.
This usually gives a good starting point for an article, if the subject is general enough, and not too new.
It's also good at structuring and rewriting texts. If you already have all the correct data, you can use it to write an outline or something like that.
The problems I saw were that it can't follow a coherent thought for more than a few paragraphs, and the writing style is generally a bit boring.
Also, the system uses sampling of results to sound more interesting and to prevent overfitting, it happens regularly that it tells you crap. One time you get a good answer, then you change one word in your prompt and the results isn't accurate anymore.
But I worked for years as a developer, so I usually notice when things are off, and I also fact check manually with Google when I want to be sure.