In this interview (https://www.youtube.com/watch?v=4Otcau-C_Yc), Geoff Hinton responded to a question about the next big thing in AI with an answer the *one* potential big thing is a better learning algorithm for spiking neural networks. This question will resolve positively if, a year from now, someone has proposed or I've seen something that qualifies as a big breakthrough in learning algorithms (or results) from spiking neural networks. If I can't come to a binary conclusion about the question, I'll resolve to the probability that best captures my uncertainty about the answer. 

Examples of potential big breakthroughs:
* Discovering a learning algorithm for spiking neural networks that appears to have scaling properties and hardware compatibility analogous to current NNs (transformers in particular) with backprop.
* A competitive or state-of-the-art result with spiking NNs on a popular benchmark.

Please leave more ideas for potential qualifying "big breakthroughs" in the comments!