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esikich | on: Domain expertise has always been the real moat
My friend is an electrical engineer and just passed a FIDE chess rating of 2000. Has played for 30 years, started the chess club in high school. Knows a little programming from the stuff he had to do with microcontrollers in college.

I'm an infra/admin jack of all trades with a comp sci degree and have been a hobby programmer for 30 years. I have a Lichess rating of 1000 on a good day.

We tried doing a chess bot competition (open book, use AI to program it, pull in opening books, end game tables, whatever, free for all) and I absolutely stomped him, but I've only beat him in real life over the board twice in 20 years.

He will beat 99% of random players in real life, and I will beat maybe 20%.

I'm not sure what I'm trying to say, but it seems to me that maybe domain knowledge isn't everything anymore? Or the domain itself has shifted?

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johnfn | parent | next
I think a charitable interpretation is that from the perspective of AI, some domains are shallow (like chess), and some are deep (you can fill in the blank here).
esikich | root | parent
What would fill in the blank be? Because this was actually kind of a test for me to address the question of "does AI just amplify domain knowledge?" In this case, it seems it didn't.
rhines | root | parent | next
Stuff where all the info isn't available online.

For example, I used to do integrations for sports betting sites. AI is going to help with the basics, like understanding the default puck line is 1.5 in hockey. AI is not going to realize that Bet365 changes their API endpoints for each season, so you need to be ready to fetch the updated ones before the new season starts, whereas most other sportbooks have consistent endpoints that you don't need to keep updating.

How much domain knowledge is actually unavailable to AI is going to vary by domain, as will the value of that. Chess is probably one extreme, where all knowledge is public, whereas something like military R&D might be the other extreme where domain knowledge is tightly guarded.

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Toutouxc | parent | next
I think this might be one of the worse examples of the dynamic, for the reasons already mentioned by others (programming a chess bot is really more of a programming exercise than a chess exercise), but it’s food for thought, so thanks for posting it. Some IRL domains are definitely more chess-y than others.
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layer8 | parent | next
Chess is a precisely defined mathematical problem in a fairly simple artificial world. Most domains aren’t like that.
runeks | parent | next
Writing software for which a full spec is available before you begin — in this case the rules of chess — was an easy problem even before AI.
esikich | root | parent
There's no chess spec, it's not "solved" like it's just a matter of implementation. Chess bot programming wouldn't be a thing otherwise. I think it's really quite funny that you think this super complicated game is just some trivial programming problem. Try it.
oldsecondhand | root | parent
It's easy to make a chessbot that only makes valid moves. Making a chessbot that plays optimally is hard.

But OP wasn't talking about solving optimalization problems, but understanding the rules of a business domain.

skulk | parent | next
what does actually playing chess have to do with writing an efficient game tree search algorithm beyond a few simple principles? You challenged him to a programming contest and won, as the vastly more experienced programmer. Even though he could use AI, your domain knowledge here proved to be the deciding factor.
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thenoblesunfish | parent
A terrible example, because chess is very, very simple - deterministic, rules fully specified in a few pages. We're talking about how you operate in a "games" which, among other complexities, involve the economy and human social dynamics. Billions of other agents. Find a plumber and try to beat them when someone calls you with a clogged toilet. Find a teacher and try to beat them when a student is repeatedly acting out.