My sense is that modern AI has more to owe Fukushima’s neocognitron, Hubel and Wiesel, and the connectionists than any intellectual descendant of the work mentioned here.
All theoretical foundations are trivial by comparison. The basic math can be understood by an interested high school student, but unless you are able to do billions of matrix multiplications per second, it doesn't mean anything. AI was going nowhere before Nvidia.
Calling these people "the minds that launched AI" is like calling Archimedes the father of hot-air balloons, because he recognized the principle of buoyancy.
In the mid sixties Minsky and Papert published a paper/book called "Perceptrons" which explored the limit of perceptrons, though it said those limits could be overcome by multilayer networks, if they were ever computationally feasible. And, thanks to Moore's law, they now are.
Almost every one of those attendees is a well known scholar whose work you depend on every day outside AI (as well as within it, though nobody really depends NNs yet). Of them, the least known in computing, Ray Solomonoff, might be the smartest of the bunch, though he was never univerity affiliated as far as I know.
Not thanks to "Moore's law". Thanks to the countless engineers who made that happen, and whose work is much more important for today's AI systems than the theory that was cooked up in the 60s and 70s.
Your comment is a typical example of the hero-worship towards theorists, and the casual disregard towards engineers, that is so common in today's science culture.
Any above-average grad student could reinvent the perceptron network from scratch. Good luck having a grad student (or even a Nobel laureate) redesign the H100 GPU from scratch.
Using error backpropagation for training multilayer networks was what overcame that problem, not Moore's law, or anything else.
People like Rumelhart kept at it, and eventually the hardware caught up with the requirements.
Still, the decisive algorithmic breakthrough for the Perceptron was applying BP to MLPs. Without multiple layers you can't solve problems which aren't linearly separable, and without error backpropagation you can't train multilayer networks.