This seems to be the current consensus.
A very similar quote from another recent AI article:
One host compares AI chatbots to “a very smart assistant who has a dozen Ph.D.s but is also high on ketamine like 30 percent of the time.”
https://lithub.com/what-happened-when-i-tried-to-replace-mys...
Suppose A solves a problem and writes the solution down. B reads the answer and repeats it. Is B reasoning, when asked the same question? What about one that sounds similar?
If a human does B, that didn't require reasoning. Same for AI.
Believe it or not, people do make an effort to test their AIs on problems that they could not have seen in their training data.
I am saying that if the outputs and inputs are equivalent, then that's enough to call it the same thing. It might be different internally, but that doesn't really matter for practical purposes.
If an AI assistant was the equivalent of “a dozen PhDs” at any of the places I’ve worked you would see an 80-95% productivity reduction by using it.
they are the equivalent.
there is already an 80-95% productivity reduction by just reading about them on Hacker News.
https://en.m.wikipedia.org/wiki/Ketamine
Because of its hallucinogenic properties?
But you're right.
This property is likely an important driver of ketamine abuse and it being rather strongly 'moreish', as well as the subjective experiences of strong expectation during a 'trip'. I.e. the tendency to develop redose loops approaching unconsciousness in a chase to 'get the message from the goddess' or whatever, which seems just out of reach (because it's actually a feeling of expectation and not actually a partially installed divine T3 rig).
I don’t think models are doing that. They certainly can retrieve a huge amount of information that would otherwise only be available to specialists such as people with PhDs… but I’m not convinced the models have the same level of understanding as a human PhD.
It’s easy to test though- the models simply have to write and defend a dissertation!
To my knowledge, this has not yet been done.
There's the old trope that systems programmers are smarter than applications programmers but SWE-Bench puts the lie to that. Sure, SWE-Bench problems are all in the language of software, applications programmers take badly specified tickets in the language of product managers, testers and end users and have to turn that into the language of SWE-Bench to get things done. I am not that impressed with 65% performance on SWE-Bench because those are not the kind of tickets that I have to resolve at work, but rather at work if I want to use AI to help maintain a large codebase I need to break the work down into that kind of ticket.
Except the documentation lies and in reality your vendor shipped you a part with timing that is slightly out of sync with what the doc says and after 3 months of debugging, including using an oscilloscope, you figure out WTF is going on. You report back to your supplier and after two weeks of them not saying any thing they finally reply that the timings you have reverse engineered are indeed the correct timings, sorry for any misunderstandings with the documentation.
As an application's engineer, my computer doesn't lie to me and memory generally stays at a value I set it to unless I did something really wrong.
Backend services are the easiest thing in the world to write, I am 90% sure that all the bullshit around infra is just artificial job security, and I say this as someone who primarily does backend work now days.
Are they a constant source of low level annoyance? Sure. But I've never had to look at a bus timing diagram to understand how to use one, nor worried about an nginx file being rotated 90 degrees and wired up wrong!