It begs the question, though: is the development of AGI contingent on some magical breakthrough, or is it a matter of endless tinkering and cobbling until we've got something that works?
It's probably both. There's going to need to be a series of breakthroughs, but there's also going to be a lot of engineering required. I believe that AGI won't happen suddenly. It will require putting a lot of pieces together. We'll get systems that have an incrementally better and better model of their environment.
Personally, I find it kind of offensive how scientifically-minded people believe they have the monopoly on generating ideas and making the world progress. That's clearly not true. Deep learning research wouldn't be where it is without GPUs, and compilers like TensorFlow and PyTorch. Engineers are huge drivers of change, they make things happen.
Deep learning research already involves a lot of trial and error. Tinkering and cobbling as you put it. People can't really tell, just writing things on a whiteboard, whether it's going to work or not. There might be some mathematical intuition, but a lot of it is throwing things at the wall and seeing what sticks, empirical testing. Some very high percentage of the research being done is basically thrown away.
Furthermore, I would personally say, as someone who works in deep learning, that we're collectively getting a little myopic. We finally got neural networks to do cool things. People are very excited, but they're forgetting neural nets are not the only kind of machine learning technique around. It actually works really poorly for some things. We're just largely disregarding every other approach because we have this one cool new toy. So, I don't know, maybe the next huge breakthrough will come from someone who's a deep learning outsider, and who's not completely locked into this paradigm and unwilling to look at anything else.
