>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.
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...
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.
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.
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.
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.
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.
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...
Curious to know some solid use cases of productivity improvements in real world?
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.
Also, naming things.
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.
These examples make me think, "ChatGPT for form completion" is the killer app as it applies to law, real estate, medicine, etc.
Then we'd make sure that our org/administration understood the risk, through whatever channels are appropriate.
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.
They already do read.
Btw we don't say "drip" and "lit" anymore, get with the times!!!!
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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!
