The speaker describes a result that uniform accuracy for neural networks is possible only with complexity exponential in the number of parameters, with identifying deviations from a reference also requiring exponential data.

“And they basically show that for radio networks, it's not even possible. Or basically it is possible, but it's exponential in the number of parameters. Because basically what they say is when we have the the radius, we have two, effectively form like a hat. Something like this. And just figuring out kind of how to place and how to scale these hats is already like an N_P_ complete problem. Like oh, like rather you need an exponential amount of data to figure out kind of these these deviations from your reference. And”