Learnings from employing ChatGPT as a ML Engineer for a day
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These examples make me think, "ChatGPT for form completion" is the killer app as it applies to law, real estate, medicine, etc.
Also, depending on what you're using ChatGPT for, this is no worse than Googling something which doctors do a lot as well.
“Write an appeal letter to a medical insurance company for a patient who needs a biopsy for a bone lesion given prior unclear diagnosis.”
Add an arbitrary ip address and timestamp and you are very far away from anything personally identifying. (Where does your computer suggest you are right now?)
So you know the individual was rejected prior to that. Probably. Maybe.
And maybe roughly where the individual is located, because ChatGPT sees an IP off of… something.
So somewhere, someone asked ChatGPT about a rejected treatment or procedure appeal.
Now if the doctor provides a poorly written appeal and asks for it to be fixed, that is another case entirely, especially if they left in patient info. But there is a very large gap between these two situations, and the first one isn’t nearly as much as you suggest.
feed to ChatGPT
find replace ABCEDFG HIJKLMNOP with name?
Then we'd make sure that our org/administration understood the risk, through whatever channels are appropriate.
>Can ChatGPT be used to improve an AI system? Yes.
>Would we hire it as our next standalone ML engineer? No.
> Let’s wait until GPT4.
The arms race for AI that can erase the human from cognitive work is really here.
...well, maybe if someone live streamed the ditch digging it could earn a few dollars.
I've always found arguments of these sort to be weak sauce--our brains are stochastic parrots.
Emergent behaviour + the bitter lesson (scaling laws) hits hard.
And while there may be objections to defining intelligence with respect to the Turing Test, I haven't heard of any definitions that are obviously and significantly better.
Our brains are stochastic parrots at least when it comes to communicating with text. People may digress into things like physical abilities, emotions, feelings, subjective experiences etc., but if we're talking about text, I'm not aware of a higher bar. In fact we probably don't know how to tell if an entity is "more intelligent" than the most intelligent humans because we won't be able to assess their intelligence.
Spreadsheets revolutionized bookkeeping. They did not eliminate the need for accountants. Robots automated much of the manual labor in manufacturing. They did not eliminate the need for process engineers, etc. Same story for autocad, protools, avid, etc.
GPT4 won't be an AGI of course, but it still could have a massive impact on the job market. A single engineer with GPT4 could potentially do the work of 10 engineers right now.
Edit: To be clear, I used GPT4 as a placeholder for "some near future LLM". I'm not making any bets on GPT4 in particular.
[1] See "The employment test" https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
And the potential mistakes of several engineers too, so you would need more than one engineer to double-check the output. Remember that, even if they are truly amazing, LLMs are basically incredibly powerful predictive keyboards.
I've actually used ChatGPT and copilot and I can tell you it's not even 10% more productive, so I'm curious how you ended up with your estimate.
Or, it could be like how assemblers didn’t get rid of software engineers but helped fuel the fire. Who knows.
"Potentially" we could explore the galaxy or have nanobots and Star Trek fabricators. What does it matter? ChatbotGPT is no closer to AGI than my keyboard autocomplete, and copilot is useless for any task whose answer cannot be googled in 20s.
A single engineer with [some near future LLM] could potentially do the work of [X] engineers right now.
Use your imagination.
Do you think that LLMs have hit their plateau? Not likely I'd say. With all the billions suddenly rushing into the space, you really think that ChatGPT v.X isn't going to replace you? Risky bet.
Mmmm, I don't know.
I think LLMs are running up against fundamental limitations in the approach (hallucinations in particular) that will not be fixed unless truly new techniques are discovered.
So I do actually think we're likely hitting a plateau.
So LLMs fundamentally do not encode the nature of facts and data. They're simply (well, no, extremely sophisticated) text prediction engines. There's no embedded comprehension. And that's basic to the approach companies like OpenAI are taking.
That's why it's so easy to find factual errors in the generated text from these models: they can create text that's pleasing, but that's as much as they can do.
The next leap, to generate language that's also accurate, will require new techniques to bake in actual understanding.
Until then, these models will always have issues with hallucinations.
Or, at least, that's my expectation. Certainly nothing we've seen in GPT 3, 3.5, or Bing, which is rumoured to be based on something close to GPT 4, indicates any advances in this area.
The second AI only needs to confirm correct, provide a correct fact instead, or some other pre-agreed solution.
USER: How many people live in Germany?
CHATGPT: About 83 million people live in Germany.
TRUTHAI: Correct.
USER: How many people live in France?
CHATGPT: There are 12 people who live in France.
TRUTHAI: Wrong. There are about 68 million people who live in France.
CHATGPT: There are about 68 million people who live in France.
USER: What is the answer to the ultimate question of life, the universe, and everything.
CHATGPT: The answer to the ultimate question of life, the universe, and everything is 42.
TRUTHAI: Insufficient data for meaningful answer.
CHATGPT: The answer to the ultimate question of life, the universe, and everything is 42.I'm not convinced that will work, but let's say it could. First, you'd have to build that oracle and the problem is, as far as I'm aware, we don't know how to do that. That'd be one of those new techniques I was referring to.
Obviously, there's work to be done still, and I'd be wary of making Google that 'oracle', but the development there would seem to be incremental (teach it more facts), rather than creating some other completely new concept.
But hey, I guess we'll see!
It's not that ChatGPT is incapable... it's that it's not inherently going to make me significantly more productive.
Big assumption, but if that's possible then businesses will fill that time. What used to take teams of people and big iron can now be done by a single person on a VPC. Did that take away jobs? Nope, requirements only expanded.
Modern high level languages didn't eliminate engineers. They suddenly meant one engineer could do what it would have taken 10 to do in COBOL or C and so software became more complex and capable.
Of course there can be industry shifts (MS was the industry for a long time and IBM before them. Google wasn't always dominant), but the pie grows to fit the available resources.
They did not eliminate mathematicians, but I do not believe mathematicians were ever doing arithmetic for a singificant portion of their workday.
Before it was a device, "Calculator" used to be a job description. It isn't any more. Every one of those people lost their job.
Even if the technology does evolve faster than people, governments can make sure people don't get impacted. Coal plants are more expensive to operate, worse for the environment, and less efficient but the coal industry is still kicking along for better or for worse.
This is the key distinction. Bandwidth. Humans only have so much of it, and if the rate at which they need to change their careers / find a new line of work exceeds their ramp-up time to become productive in that career / line of work then we can start to expect non-linear behavior from those humans. Maybe that means some sort of mental breakdown.
PageRank was essentially an efficient way to compute / approximate the leading eigenvector of a very large matrix.
It was computer, not calculator.
An AGI would be a "purposeful system" the same as humans. None of the other things you mentioned are purposeful systems. In terms of their structure and function they all have limitations on their own ability to change depending on the environment they are in. The key dimensions with which to think about this problem is "can this thing be structured in only one way for all environments? Or can it be structured in only one way in any given environment, but structured differently in different environments? Or can it be structured in more than one way in any environment, and also more than one way in different environments?" and then the same for functionality "can this thing only perform one function in all environments? Or can it perform only one function in a given environment, but different functions in different environments? Or can it perform more than one function in a given environment, and more than one function in different environments?"
Only things which have both properties of being "multi-multistructural" and "multi-multifunctional" meet the definition of a "purposeful system". They can change their structure to adapt to the environment, and they can change their goals within and between environments.
Humans currently use computers as instruments, and when an AGI hits it will be capable of saying no.
Thinking it through, I would say that "autocomplete" would be uni-multistructural and uni-unifunctional given that autocomplete is always structured only one way for the given environment that it's in, but that structure differs between environments (e.g. a phone, or a form in a web-browser), and that regardless of what environment its in it's only function is to predict the next token of output for a given input.
LLMs like ChatGPT on the other hand I would imagine to be uni-multistructural and multi-multifunctional, given that for any given environment you deploy an LLM into it's always structured the same, but there are many different structures it can take given there are lots of different LLMs. And I believe it to be multi-multifunctional because even within a single environment it can be used as an instrument to perform a wide variety of extrinsic functions, and this is true of other environments it could be deployed in.
So... it's actually significantly closer.
That is, we've had a huge hollowing out of all sorts of "middle" jobs in the US (e.g. someone mentioned secretaries used to be the most populous job in the US) so now we're largely (not exclusively, but largely) left with 2 categories of jobs: (a) dead-end jobs that are at present difficult to automate because they often involve manual labor: housekeepers, security guards, massage therapists, waiters, truck drivers (but, we see where that's headed...), etc. and (b) high level jobs that "manage the machines", e.g. tech jobs.
It's not that hard to see where this ends up with AI being capable of more complicated tasks - I wrote about an anecdote recently where ChatGPT had essentially already obviated some positions https://news.ycombinator.com/item?id=34862450. There are a slew of very high paying jobs that AI is coming for next, e.g. there is currently a shortage of radiologists because many folks in med school see the writing on the wall and don't go into radiology residencies. While full diagnostic radiology AI may not be there yet, it certainly will be by the time folks in med school finish their career in 40 years.
That's the job of new inventions. Nothing to complain about, in my opinion.
Those however who think that radiologists (/lawyers/secretaries) will get automated away, don't really understand what a radiologist's job really is.
The boring bureaucratic part of a radiologist's job will get automated away, yes, but that's because automatable/auto-completeable tasks should get automated at some point.
Think of it as a healthy organism eating away the boring part of a dream job that you, as a kid, imagined doing when you'll grow up.
As someone intimately familiar with the job of a radiologist, please then tell me what "a radiologist's job really is".
A diagnostic radiologist's job is to take a series of images, and output a set of findings and diagnoses. There is essentially no better task suited to modern AI. Yes, radiologists can do procedures like biopsies and potentially other interventions, but diagnostic radiologists often solely read images or at least earn the vast majority of their RVUs by reading images.
we should be more careful on how we use them. in my opinion, ChatGPT and similar will be horrible for search on the web, as we are flooded with text that looks like a human wrote it but it does not add any knowledge or new insight.
This would make me conclude that the models we have today can in fact read, and typically rather well.
Never say never
When we are talking about THE MODELS WE HAVE TODAY, then yes, we can say never.
They already do read.
To me, ChatGPT clearly displays degrees of understanding. Often when I ask it to do things like make a certain modification to a piece of code, I'll ask it "why did you choose to do it like that?" and it gives a coherent explanation. That it correctly did what I asked and that it's explanation of why it did that way comport with one another implies understanding in my view. How could it not? What else would that imply? And if I ask a human to do the same thing and get the same result you'd have no problem with saying "well it's able to do that because it understands, and if it didn't understand then it would have zero capability of doing that". So, what's with the double standard?
For me, it's matrix multiplications on data made to output things that are approximations to what a human has written before on that question or text you're prompting it.
I know how training a deep neural network works and there's no reading involved for me :)
It's just estimating what a person would write (being trained on a really large data set of what people wrote). If it's trained on gibberish, it will just output gibberish. If I ask you to read a book with gibberish words would you do it? ChatGPT would (according to you) "read it" and recite the gibberish I gave it without a problem.
The matrix multiplications used to build these models don't show any comprehension, agency, or consciousness. Their point is to estimate the next data point given the previous data point, they will perform the same task independent from which training data we give them. That's not reading.
I don't say this might not change in the future, yet treating ChatGPT like a human using words like "reading," "understanding" etc. or intelligence for that matter is just anthropomorphizing for me.
One thing that stands in my mind from having read that is that the structural properties of the system matter in terms of its possible function, and I'm referring to the exact structure that includes the model trained on the non-gibberish text and treating that as the specific system I'm referring to. It seems when you say ChatGPT you're referring to the entire functional class of objects that implement the transformer architecture, which I'm not.
After thinking about it for a while the second thing I would note is that you're focused only on it's intrinsic function of predicting the next token, which as you say it always performs that function regardless of the training data, and I agree with that. Where we differ, I think, is I'm considering the specific structure which includes the model trained on non-gibberish data as an instrument (not a human!) that we use for it's extrinsic functions. Normally an individual or system's extrinsic functions are so narrow they're a lot easier to define, and we've never interacted with a system until now that the extrinsic function is like "understand what I say and reply accordingly". Think about it from the perspective of would you use or have any utility for GibberishGPT? No, same way I wouldn't read a book of pure gibberish.
the specific model you are referring to does nothing, it's not acting (it's not reading anything). you have to press enter for it to work. so it's not reading, you (and OpenAi engineers) are doing its actions for it.
Reading and understanding are obviously different things. I can read a paper on quantum physics, but I can't understand it.
Would be interesting to see how well it could 'understand' a paper, or if more layers etc. would be needed.
In the article, they mention using the new labels to build a "more balanced" dataset -- is this a realistic possibility in practice when most teams still have a dearth of data?
In terms of if it’s realistic in practice, the answer is yes. Some teams have a dearth of data, but many AI companies we work with have more data than they can use, and it’s more a question of how to sample, curate, and correct the data and labels they have to improve their models rather than collect new data. Great questions!
Btw we don't say "drip" and "lit" anymore, get with the times!!!!
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The people who weren't already good can't figure out how to use it properly.
I also see a bunch of people online, most?, using it incorrectly, writing prompts that would give bad info. Maybe they're doing it on purpose?
To those who have everything, more will be given.
The citation thing is a good example of this, just because chatgpt makes up citations today doesn't mean that it will always make up citations, accurate citations is just the wrong use case today. Already there are extensions that make accurate citations work. Still, people point at inaccurate citations as some kind of proof that chatgpt won't work.
Probably people want it to fail or don't want to believe how transformative it is.
Ego defense is a very real, and very powerful driving force in human behavior and it is coming out in full force.
Curious to know some solid use cases of productivity improvements in real world?
Also, naming things.
Apple's Metal API suffers from a weak ecosystem of third-party documentation and examples (unlike OpenGL), and Apple's documentation is barebones, so having a tool like ChatGPT that can interact with that entire space conversationally is a huge boon.
Sometimes it suggests erroneous approaches. When that happens, I can ask it for alternatives. It's not actually too troublesome to vet the information since it has to actually work on the hardware, and honestly, I feel like I'm learning it more deeply by trying the different ways anyway.
For specific questions, I find (and trust) the results faster on Stack Overflow. The code that ChatGPT produces is fairly good and my boss is impressed when I showed him the answer of How to parallel filter a List in Java. Yet, filtering a List is a far cry from building an app (which my boss thinks ChatGPT can do, given enough explanation what to do).
And any other general questions, while again, impressive...are too general for someone with a specific knowledge and useful only for somebody not familiar with the subject. Maybe the trick is how the answers are presented with confidence and giving the feeling that the converser actually knows something about the subject, yet I know it just machine generated babbling without any real understanding...