While this article is a little overenthusiastic for my taste, I think I agree with the general idea of it - and it's always kind of been my pet peeve when it comes to ML. It's a little depressing to think that's probably where the industry is heading. Does anyone feel the same way?

A lot of the stuff the author says resonates deeply, but like, the whole deterministism thing is why I liked programming and computers in the first place. They are complicated but simple; they run on straightforward, man-made rules. As the article says:

> Any good engineer will know how the Internet works: we designed it! We know how packets of data move around, we know how bytes behave, even in uncertain environments like faulty connections.

I've always loved this aspect of it. We humans built the entire system, from protocols down to transistors (and the electronics/physics is so abstracted away it doesn't matter). If one wants to understand or tweak some aspect of it, with enough documentation or reverse engineering, there is nothing stopping you. Everything makes sense.

The author is spot on; every time I've worked with ML it feels more like you're supposed to be a scientist than an engineer, running trials and collecting statistics and tweaking the black box until it works. And I hate that. Props to those who can handle real fields like biology or chemistry, right, but I never wanted to be involved with that kind of stuff. But it seems like that's the direction we're inevitably going.

ML doesn't work like programming because it's not programming. It just happens to run on the same computational substrate.

Modern ML is at this hellish intersection of underexplored math, twisted neurobiology and applied demon summoning. An engineer works with known laws of nature - but the laws of machine learning are still being written. You have to be at least a little bit of a scientist to navigate this landscape.

Unfortunately, the nature of intelligence doesn't seem to yield itself to simple, straightforward, human-understandable systems. But machine intelligence is desirable. So we're building AIs anyway.

I feel similarly to you.

Even amidst the pressure of "client X needs feature Y yesterday, get it done with maximum tech debt!" it felt like there were still chances to carve out niches of craft, care and quality.

The rise of "probabilistic" software feels like it is a gift to "close enough is good enough" and "ship first, ask questions later".

If only we all became scientists. We'll more likely become closer to tabloid journalists, producing something that sounds just truthy enough to get clicks.

> They are complicated but simple; they run on straightforward, man-made rules.

The trouble is that many problems simply cannot be solved using rules. They're too complex and undefinable and vague.

We need systems that are more flexible and expressive to handle the open-ended complexity of the real world.

The deterministic and probabilistic paradigms will likely coexist rather than fully replace each other - we'll build deterministic interfaces and guardrails around probabilistic cores to get the best of both worlds.