This is fantastic news for software engineers. Turns out that all those execs who've decided to incorporate AI into their product strategy have already tried it out and ensured that it will actually work.
> Turns out that all those execs who've decided to incorporate AI into their product strategy have already tried it out and ensured that it will actually work.
The 2-4-6 game comes to mind. They may well have verified the AI will work, but it's hard to learn the skill of thinking about how to falsify a belief.
MATCH
2, 4, 6
8, 10, 12
12, 14, 16
20, 40, 60
NOT MATCH
10, 8, 6
If the answer is "numbers in ascending order", then this is a perfect illustration of synthetic vs. realistic examples. The numbers indeed fit that rule, so in theory, everything is fine. In practice, you'd be an ass to give such examples on a test, because they strongly hint the rule is more complex. Real data from a real process is almost never misleading in this way[0]. In fact, if you sampled such sequences from a real process, you'd be better off assuming the rule is "2k, 2(k+1), 2(k+2)", and treating the last example as some weird outlier.
Might sound like pointless nitpicking, but I think it's something to keep in mind wrt. generative AI models, because the way they're trained makes them biased towards reality and away from synthetic examples.
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[0] - It could be if you have very, very bad luck with sampling. Like winning a lottery, except the prize sucks.
That's the one. Though where I heard it, you can set your own rule, not just use the example.
I'd say that every black swan is an example of a real process that is misleading.
But more than that, I mentioned verified/falsified, as in the difference between the two in science. We got a long way with just the first (Karl Popper only died in 1994), but it does seem to make a difference?
