I'm excited by the potential of memristors, which are already being used in the lab for toy deep learning tasks -- for example, see https://www.nature.com/articles/s41467-017-02337-y . Over time, I would expect memristors to be used for increasingly larger, more challenging AI tasks.

The big deal about memristors for AI is that they have memory and therefore produce different readout outputs for different input sequences over time -- out-of-the-box, without requiring any training -- and in a way that can make sequences linearly separable under fairly general conditions. For instance, in the Nature paper I linked to above, the researchers took a network of memristors, which they call the "reservoir," added a linear layer with a SoftMax on top of the reservoir, trained this hybrid network on a lower-resolution variant of MNIST (feeding pixel values over time, as varying voltages), and achieved classification accuracy superior to a tiny neural net despite having only 1/90th the number of neurons.[1] Note that they only trained the added layer; they did not have to train the reservoir. Figure (a) in this image has a simplified diagram of the reservoir + added layer architecture: https://www.nature.com/articles/s41467-017-02337-y/figures/1 -- only the matrix Θ had to be learned. The potential, over time, is for having highly scalable hardware neural-net components for learning to recognize and work with sequences.

PS. For clarity's sake, I'm ignoring a lot of important details and playing fast and loose with language. If you're really curious about this, please read the IEEE article and the Nature paper.

[1] https://news.engin.umich.edu/2017/12/new-quick-learning-neur...