Tech’s hottest new job: Prompt engineer(washingtonpost.com) |
Tech’s hottest new job: Prompt engineer(washingtonpost.com) |
This is just the next incarnation of trying to shift the output of someone else's algorithm in your favor. Be wary of building a career on top of that. It's very easy for the algorithm owner to change things up and obviate any value you used to provide.
>https://sites.google.com/view/automatic-prompt-engineer
Not exactly a "toaster go brrrr" job, but it could be obsolete one day
WaPo does need to chill though. There’s barely any Prompt Engineer jobs
Edit: If anyone's curious, I've been following this for prompt stuff: https://github.com/dair-ai/Prompt-Engineering-Guide
You've got a huge blind spot if you think prompt engineer isn't already a thing.
It may be a "thing", because generating BS is a viable business model and ChatGPT makes it more efficient.
..but I submit as a working hypothesis, that it is completely impossible to gain knowledge you do not already possess from a language model, no matter how clever your prompting.
I'm very interested in counter-examples, but I have seen a few that turn out to be fake already.
People are graduating watered-down educations, earning inflated cash, with inflated titles. It all helps people believe they're higher status, that they have a university degree and are a manager earning $80k, surely they're getting close to the top of the totem pole now. But they have a worse standard of living and education equivalent to high school in the '60s.
[1] https://news.ycombinator.com/item?id=34641549
[1] https://www.cbsnews.com/news/salary-manager-jobs-fake-titles...
-mlsu: https://news.ycombinator.com/item?id=34884683
At this point the word "engineer" has lost its original meaning. Until there's a formal theory of how we can interact with LLMs and you make use of that in a systematic fashion, "prompt engineering" is really closer to "prompt artist."
Interesting angle. Are you saying there are rarely any "software engineers" out there, that they are all merely "software artists"? Cause none of these uses a formal theory for their craft. If they were then all those highly opinionated discussions of whether to use goto in C or what are the greatest flaws of node.js would just not exist.
I don't see this in this prompt engineering. In my limited experience (I played a few hours with Stable Diffusion and more hours with the OAI davinci-003 model), you can get good at it within a few days.
Well, you do you. That's old world thinking for a field that's going to dramatically morph into something that barely resembles what we have today.
I'm hiring a contract prompt engineer for my startup.
If you want to help us achieve better "TV replacement" results, send me an email (see profile).
https://fakeyou.com/news (early demo, more coming soon!)
Lawyers have a bad reputation, sure, but there’s a lot of education about the interpretation of our law and the absurdly large corpus of legal documentation that must be read in order to even become a lawyer is far and above anything you describe.
Crazy.
https://arxiv.org/abs/2302.06541
That is not to say, that integrating LLMs won't create a lot of jobs. Think of it as systems engineering. Knowing how computers work, as well as a software engineer does, will always be useful.
(This was before JS, before CSS, etc. Mostly just your original HTML simplified LaTeX article.cls elements, plus `A` and `IMG`, and maybe a `FONT`.)
HTML was easier to use than many word processors, but because it was new and unfamiliar, yet looked like it might be huge... for a brief period, practically anyone who could spell "HTML" or "WWW" could posture as a whiz kid, and make big bucks.
I'd guess that "prompt engineer" will evolve into real careers soon, but the nature of the technology and the role will be very different than it is this quarter.
I've been playing around with generating stories with ChatGPT for a while and...English (or any natural language) is really bad at being specific. I've made progress by learning some specific words to describe the type of scene I want and how much of it I want ChatGPT to generate (such as a scene for just that evening verses a few paragraphs describing weeks of traveling). I've also started getting some intuition for when I've given ChatGPT too much info (it'll cram all the facts in in weird ways) and too little info (it'll get really random and start inserting new characters and stuff).
Having a way to manage the meta aspects of story generation would be a big help.
Edit: maybe I should have kept reading.
> Anthropic, founded by former OpenAI employees and the maker of a language-AI system called Claude, recently listed a job opening for a “prompt engineer and librarian” in San Francisco with a salary ranging up to $335,000. (Must “have a creative hacker spirit and love solving puzzles,” the listing states.)
In the end what we all value is what solves problems. Those who embrace AI tech and learn to use the tool and work around its flaws will solve more problems than those who don't. This includes coming up with a system to validate the work. Those who use the tool recklessly will create more problems than they solve.
What side are we on here? I've been in the industry for over two decades and I for one cannot wait to command the computer in complex ways in my natural language. I am not threatened by other people being able to do the same. The tool is just a tool. What you build with it is what will separate the "professionals" from the "hobbyists".
Maybe they realized that too many jumped on the blockchain BS train.
We will see.
As mind viruses operating on human brains, they do not seem completely different technologies.
Did crypto crash?
Last I looked BTC was at 20K USD a pop ... strange definition of a crash for something that used to trade below a dollar.
If the dollar (or any other currency)lost 70% of its value in less than a year then we would certainly say it crashed
There are other narrower senses of "software engineer" such as "person who optimizes code" and to me those more qualify as engineering because we not only have a decent enough theoretical background (see Agner Fog's work) but also can experimentally verify things. On the other hand it's a lot harder to quantitatively say if one design is better than another.
I think there's also some work in terms of rigorously modeling concurrent/distributed systems (Lamport's TLA+) work which I'd like to see more of.
I'd imagine that being a "prompt engineer" entails finding out and mapping the structures that gives you the desired result. Think of it as a novice user of search engines VS expert user of search engines.
People training and releasing custom models that can replace entire workflows of disparate steps needed to produce an image that would normally result from that workflow.
There was that video or maybe it was an article where the guy made it so he could just use natural language to describe the edits he wanted made and it would make them etc.
Oh like hell it requires no skill.
You tell me how you'll generate better photos, improve dialogue coherence across multiple speakers, and control camera direction and movement (something we're using LLMs for too as we experiment with special-purposed models).
All of this is not known a priori, by the way. And I won't accept building a database or lookup table as an answer.
I also want to know how you'll test, benchmark, and refine.
You also need to budget for inference complexity.
I'm waiting :)
I can do this myself, but it is a full time job. I am so busy with all other aspects of my business I'm looking for people to bring on board.
Epic 4k HD photo, high res and epic, cool extra awesome photorealistic 5k or 6k, realistic, in the style of a really good photographer.
At the top left of a 4k picture, place a dot with the RGB value of (0.5, 0.8,
0.2), where color components are expressed on a scale of 0-1 inclusive.
Then, on the top line, second position from the left, place a
dot with the RGB value of (0.7, 0.3, 0.4).
(approx. 8 million more to follow...)
At this point, BTC is very much known for its extremely high volatility (source: look at the price history since inception).
There hasn't been a single year since it launched where it hasn't displayed outrageously wild swings: at this point, it's pretty clear that the wild volatility is an intrinsic attribute of this particular asset class.
Therefore: not a crash, just Bitcoin's business as usual.
you can start by seeing some of the the accepted definitions here [1]
While you may have expected crashes in bitcoin to be that hard (good for you) most investors, dozens of high profile funds/exchanges/crypto businesses did not expect bitcoin to fall to 20k USD and have failed.
Same thing it's been doing every year since 2011.
Yet BTC is still trading at 20k USD ... I'm not sure we have the same definition of the word "crash".
But whatever floats your boat, man.
No.
A LLM has more opportunity itself to replace a Lawyer, the person typing the prompt is not necessarily required to be as educated. Though a case can be made that you need to validate the information.
As it happens we have an opportunity to tell how this works. Software engineering has seen many abstractions of which each comes with its own complexity in verification.
What tends to happen is that people don't really do a lot of verification, we are just "mostly right" very fast and leave an immense amount of inefficiency and indirection behind us.
If I need someone to help me interact with a legal LLM I will want to (and probably be able to, for 300k) hire someone with a law degree. In fact I anticipate many lawyers in the future will effectively become “prompt engineers” for legal LLMs.
How do you use this as a lawyer?
I mean, as a stereotypical evil lawyer in a world of naïve people who don't learn from experience, you could maybe use it to win cases until you destroy the justice system.
But other than that...
Sure, there are matters I would only trust a lawyer to handle, but there are a great many I wouldn't.
Further, the average quality of a human lawyer will likely remain the same tomorrow as it is today, while AI will only get better. LLM today, perhaps some hybrid stack tomorrow, it's only a matter of time before an AI lawyer is the way to go for just about any legal matter. And let me be clear, that time might be 10 years, or may be 100+, but it is coming.
This is a strange statement. No one is training LLMs to generate “misinformation”. It’s the opposite - it’s trained to generate the most likely next word, given the preceding 2000 words - using billions of examples from a real world training corpus. So it will try to generate as much information as what’s present in the corpus. Maybe even more, but that’s debatable.
That is phrased like it is stating a fact about the training process, but it is a statement about the intent of the training, isn't it? So I don't see it as rebutting my comment.
>It’s the opposite - it’s trained to generate the most likely next word
Sure, of course, what else? But if you take any correct statement about something and modify it slightly, it's not very likely it will still be correct.
It seems intuitive to me that there are going to be a million billion (understatement) wrong things next to anything correct in the inputs. As a sort of combinatorial, mathematical thing. You just (in principle) count all the ways to be wrong that are similar to being right.
Nobody trained it to get anything right! It doesn't matter what people expect if they don't have a procedure to do it.
If a statement is adjacent to things that are also "correct", that almost implies a lack of information in the original statement. It seems born out in the impressive BS'ing - the key to BS'ing is saying things that can't really be wrong.
Not true. Emergent abilities is an active research area in LLMs [0]. They even have pretty graphs on the topic.
[0] https://ai.googleblog.com/2022/11/characterizing-emergent-ph...
Is Art Director just a "BS" job? I don't get it.
Checking some of the facts it gives me against other sites it’s all correct, but better organized and more accessible. There’s your counter-example. This works for basically any well-documented process.
I think I understand the sense in which you claim it produces relevant facts not in the prompt.
It's not that we differ on easily observable behavior of the system.
It's that I question if GPT-3 is "producing" these identifiable facts, and if the user is "producing" them instead, whether they can possibly be "relevant".
I'm not sure what you're trying to say. That GPT-3 is just vomiting stuff up out of its training set and not producing any new knowledge? But that's totally irrelevant to the issue of whether it can transmit knowledge to a user, who presumably hasn't memorized the entire training set.
It not only spit out the model but also the casts/fillable attributes on the model, as well. It even helped me work through an idea, that I didn't know what it was called, I was thinking it was EAV but instead it's metaform/metafields, to basically create something like how wordpress has the ability to dynamically create content 'types', django/wagtail can do this to, w/ chatgpt I think I've nailed down how to do this using polymorphism with the least amount of headache.
I'm wanting to create a CRM/CMS/ERP solution that can be very 'moldable' to different use cases, and this looks to be a good use, either way just being able to discuss with the ai my 'options', was like a major brain dump and increased the power of my flow.
YMMV, but if you can't get it to work like this, doesn't mean it doesn't, just means it doesn't for you, and while I can save 2-3 hours for every hour previously worked, that's valuable to me, esp as a freelancer who charges per project, not hourly.
I reached the same conclusion as yourself, but do see a totally different path to take regarding information propagation (how GPT works). For example, cells merge information monotonically. This is how neural networks balance too, but could be applied in new/undiscovered ways.
It doesn't know what works.
Hmm. Seems obvious to me that it's producing new output, but that output isn't knowledge and it can't be.
Sometimes ChatGPT tells me something that turns out to be correct and relevant. And I get excited, and then I Google it and what it told me is the first hit on Stack Overflow.
There's a subtle point here, that other people might say "well, ChatGPT is ok, but no better than Google" or something like that. But I differ on that. The key is that I don't know it's Stack Overflow until I check independently. So it's giving it too much credit to say it's as good as Google, and the amount of information it can output is not lower bounded by its training set, but is actually zero due to being adjacent to an infinite amount of BS that by its nature always requires external mechanisms to separate out.
You might synthesize new knowledge.
When ChatGPT produces new output, it's not synthesizing new knowledge. It can't even output the knowledge it was trained with, as long as it lacks the ability to tag it in a trustworthy way.
It's not that it's always BS, it's that it's almost always BS and if you don't know the answer in advance or independently, you can't distinguish it from anything within the model.