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> It reminds me a lot of politics and I'm not sure why we're so resistant to nuance when our whole job is typically composed of nuance.

My theory is the increase in available information is overwhelming everyone's cognitive abilities, and so jumping to reductive conclusions is just a natural defense mechanism, leading to increased polarization and reduced listening/tolerance skills. Certainly software engineers have an above-average tolerance for nuance, but even we have to pick our battles or risk drowning in the flood.

I agree and hold a similar theory. The human mind prefers to put things in boxes. And the simpler the box, the better.

There also seems to be a phenomena where the more complex the system the simpler the box must be. I'm kinda surprised by this as understanding complexities (especially around dynamic environments and future planning) seems to be one of the things that distinguishes humans from other animals. And opposable thumbs. I mean sure, there's other animals with those too but we stand out, literally.

If the box is simple, then it's easier to cast each individual complexity of the system, as needed, into an interpretation that fits and reinforces the box.

But don't you find it odd that my box is complex and that your box is simple? And that this holds for whoever you and I are?

This is probably related to the Fundamental Attribution Error.

https://en.wikipedia.org/wiki/Fundamental_attribution_error

I buy that one, but I think there's a coupling with metric availability.

Where we have measurements for pretty much everything but people don't know measurements are proxies and not always aligned with goals. Like how a ruler doesn't measure meters, but meters according to the ruler and only at the ruler's precision level. I can totally get how people who don't work with these tools don't understand the nuances, but it confuses me with experts. Isn't expertise, by definition, contingent upon understanding nuance?

It seems that the more metrics we have available to us, the less we care about understanding how those metrics work and what their limitations are. That they just become black boxes.

I mean there seems to be a very common belief that you can measure visual image quality by measuring some difference between an intermediate representation of a classification model. Or a belief that entropy measures quality of speech.

I'm really concerned that Goodhart's Law might be one of the Great Filters.

What makes a man turn neutral? What makes a man turn neutral? Lust for gold? Power? Or were you just born with a heart full of neutrality?

-- Zapp Brannigan

> This is always really surprising to me that it appears to be these two camps.

The reasonable camp doesn’t produce good sound bites.

“LLMs are the next disaster” (either by replacing workers or as a fraudulent product leading to a market crash) is more exciting than “another interesting tool that is useful for certain problems that don’t have other tools, and works pretty well but still needs some polish and safety work.”

History says that the things in the middle, usually ones with the clearest computational model beneath them, get a fancy name and we stop calling it AI and we just call it algorithms.

I could see someone making a case for this being that 'middle' group but there's a sour note to this process that I don't know from one week to the next whether I find it sneaky or delightful.

Someone else can make that argument, because I'm so sick of silver bullets and gold rushes that I just. don't. care. Sturgeon's Law applies (90% of everything is crap) and I'll listen just enough to see if anyone is proposing which bits are the 10% to keep a finger on the scale if I think it'll matter. But let everyone else bleed over this, because in another ten years they'll be laughing about how silly they were to think this was going to solve all of our problems or end our profession.

Software is eating most things. If something eats software, your employability will be the least of your existential crises.

>History says that the things in the middle, usually ones with the clearest computational model beneath them, get a fancy name and we stop calling it AI and we just call it algorithms.

I've heard this before, but do we? Which were those this from the past that we "stopped called AI and we just call algorithms"?

I see three categories:

(1) very complex algorithms that we never did call "AI".

(2) stuff we did call AI, and we still do - things like expert systems, or IBM's Watson, or game AI. We knew, and still know, that those weren't AGI.

(3) some stuff promoted as AI (like the "AI assistant" Clippy), but which, marketing materials aside, nobody really considered AI or called them that.

But I don't remember this demoting/relabelling of stuff from AI to "just algorithms". It might have happened with some stuff, but I doubt it was a "classic" development as its portrayed.

Offhand, things that used to just be "AI": Expert systems, markov chains, genetic algorithms, data mining, and identification/classification.

People in the field would probably say they fall under the AI umbrella, but it's not the common viewpoint. Either someone can conceptualize how they'd work (expert systems) or they've been watered down and turned commonplace (markov chains in software keyboards, identification in Facebook images), and either way it disassociates the technology with the "intelligence" part of "artificial intelligence", so is no longer thought of as part of it.

Also Dynamic Programming, and soon, I predict, Monte Carlo simulation.

When ever was Montel Carlo simulation considered "AI"?

Some tool using it marketed as AI, I can believe. But then again, nested IFs have probably also been marketed as AI in some products.

AlphaGo is considered AI and its core component is Monte Carlo Tree Search.

Latest iteration of pretty much every turn based game AI is based upon MC simulation.

Polarisation in general can be well-explained by the business models of social media. You’re shown content that you react to strongest, and that is usually one end of a spectrum of views.

Not only are you shown content that you react the strongest too, people create content that they react strongly too, continuing the cycle.

A user with a balanced interpretation and somewhat neutral feelings about a topic generally won't feel like they want to add something to a discussion. A user with strong opinion will more likely engage with someone with posts with the opposite viewpoint or the same viewpoint.

HN is a bit of an exception because the community is reasonably high quality. But major platforms? The people who bother to write out long and neutral posts learned there is nothing to gain from doing that years ago.

> HN is a bit of an exception

Even here, depending on the felt "hotness" of the topic in the community, you might get a lot of negative sentiment for trying to find a middle ground or daring to look at generalized claims in more detail.

I think, in general, people now seem to require you to signal that you share their identification with a certain thought before an open discussion might become possible.

One important aspect seems to be that the higher your educational level, the more likely you are conditioned to identify with your own thoughts. This amplifies polarization on an intellectual level. There is something in it for the individual thinker taking on a new polarized belief. It adds to their identity.

The ultimate catch then is to take the position that I just outlined as an identity contrary to "all" others who are polarized. This is yet another trap.

Hence, the exercise is to practice not getting polarized while being compassionate to those who are. It's just a tendency of the human mind and nobody should be judged for falling for these traps. It's too easy to fall for it given our current conditioning.

I think that’s mostly spillover from over social media, combined with the very opinionated nature of us nerds :-)

Interesting, usually when I express my opinion in the middle people agree. I've only talked to people about chatgpt, ai etc in real life though, not on the internet

I think the real life aspect changes things a lot.

Zealots on either side communicate at 100 times the rate of people who aren't so heavily invested.

I think it pays to just point out good use cases. For me, it's a superpowered thesaurus, a more concise and relevant Wikipedia, and an excellent listicle generator.

AI is not all-powerful, but those things alone help me a lot when I'm brainstorming.