The author of the article researches far right extremism and has been targeted by certain groups in the past IIRC, I read this part as a concern about physical safety, but this is just my speculation.
I was fortunate enough to take this course with Ted Shifrin at UGA, he is an incredible professor (now retired), and it was one of my favorite courses when I was in undergrad.
I strongly disagree with this. “Computers” would have not been replaced with the machines that replaced them if those machines routinely produced incorrect results.
One could argue that for applications where correctness is not critical my position does not apply, however this is not the analogy that the article is making.
The trajectory of LLMs "routinely producing incorrect results" is heading downwards as we are getting more advanced reasoning models with test-time compute.
I don't know whether you used some of the more recent models like Claude 3.5 Sonnet and o1. But to me it is very clear where the trajectory is headed. o3 is just around the corner, and o4 is currently in training.
People found value even in a model like GPT 3.5 Turbo, and that thing was really bad. But hey, at least it could write some short scripts and boilerplate code.
You are also comparing mathematical computation - which has only 1 correct solution - with programming, where the solution space is much broader. There are multiple valid solutions. Some are more optimal than others. It is up to the human to evaluate that solution, as I've said in the post. Today, you may even need to fix the LLM's output. But in my experience, I'm finding I need to do this far less often than before.
Wait what? Human programmers produce incorrect results all the time, they are called bugs. If anything, we use automated systems when correctness is important - fuzzers, static analyzers, etc. And the "AI" systems are improving by leaps and bounds every month, look at SWE-Bench [1] for example. It's pretty obvious where this is all going.
Sure, people make mistakes all the time. But would you prefer those mistakes be sprinkled randomly throughout your data crunching, or be systematic errors?
The point that that post is making is that a machine isn't going to make a mistake in adding two numbers. It reduces arithmetic errors to 0 (unless you count overflow which can be detected), and if it didn't it would only be useful in the rare case you don't care about accuracy.
AI in it's current state does not do for logical accuracy what computers did for arithmetic accuracy; You still need to verify every output from an LLM, which I doubt you've done for the many billions of arithmetic operations that happened this second on the computer you're on right now.
Think of electronic calculators. They have a significantly lower error rate than human calculators. Both statistically significant and practically significant.
My fear is that if I were to start doing this, I would stop engaging in the difficult aspects of building something. And I think the skills I have now would atrophy.
Not in my experience. You can still do everything as diligently and as manually as you find necessary or useful.
On the other hand, the bar to start exploring something in the first place (quite possibly sloppily, quite possibly containing a lot of bugs) has significantly gone down, and I personally find it amazing.
I've heard of this but didn't use it because all of my email newsletters are on substack and substack supports RSS. Also, with substack RSS, you get a feed per newsletter whereas Kill The Newsletter gives you one feed for all newsletters going to their address.
I use this for a set of applications that I self host that I give my friends access to. I use it as a quick and easy place to dump a couple links, and I can put it behind a reverse proxy.
It's not a new idea at all or especially insightfully written. However, I'm not upset by it being posted; I still enjoyed reading it and found it was tastefully short.
It skips over the difficult question of "how big is too big?"
Easy to complain about in the abstract. Hard to answer how we restructure society to determine when a company or individual salary is too large, without allowing each company or individual make that decision for themselves.
