This is humorous (and well-written), but I think its more than that.
I'm always making the joke (observation) that ML (AI) is just curve-fitting. Whether "just curve-fitting" is enough to produce something "intelligent" is, IMO, currently unanswered, largely due to differing viewpoints on the meaning of "intelligent".
In this case they're demonstrating some very clean, easy-to-understand curve-fitting, but it's really the same process -- come up with a target, optimize over a loss function, and hope that it generalizes, (this one, obviously, does not. But the elephant is cute.)
This raises the question Neumann was asking -- why have so many parameters? Ironically (or maybe just interestingly), we've done a lot with a ton of parameters recently, answering it with "well, with a lot of parameters you can do cool things".
I'm always making the joke (observation) that ML (AI) is just curve-fitting. Whether "just curve-fitting" is enough to produce something "intelligent" is, IMO, currently unanswered, largely due to differing viewpoints on the meaning of "intelligent".
In this case they're demonstrating some very clean, easy-to-understand curve-fitting, but it's really the same process -- come up with a target, optimize over a loss function, and hope that it generalizes, (this one, obviously, does not. But the elephant is cute.)
This raises the question Neumann was asking -- why have so many parameters? Ironically (or maybe just interestingly), we've done a lot with a ton of parameters recently, answering it with "well, with a lot of parameters you can do cool things".