I missed the part, though, where there was some "learning"/"adjusted predication" in the interpolation function(s), rather than just a fixed calculation such as a literal linear interpolation.
I was happy just to be able to tease apart the big equation before the the python code sample, but was too lazy to drill down into what the "delta-x"/"delta-y" factor-functions were.
Still, this was a good presentation: somebody with little to no knowledge of the field, but some math, could get the gist of it. Kudos to the author.
I would expect AI to include some sort "emergent behavior", so in a sense you are correct. If a program does exactly what we expect it to, exactly how we tell it to, it almost certainly isn't AI.
Unless we are telling it to "be intelligent" whatever that means.
> As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities currently classified as AI include successfully understanding human speech,[4] competing at a high level in strategic game systems (such as Chess and Go[5]), self-driving cars, and interpreting complex data.
I missed the part, though, where there was some "learning"/"adjusted predication" in the interpolation function(s), rather than just a fixed calculation such as a literal linear interpolation.
I was happy just to be able to tease apart the big equation before the the python code sample, but was too lazy to drill down into what the "delta-x"/"delta-y" factor-functions were.
Still, this was a good presentation: somebody with little to no knowledge of the field, but some math, could get the gist of it. Kudos to the author.