An interesting feature of this approach is that the proposed hardware doesn't rely on non-linear elements, memristors, or even active elements (besides an optional noise source). It is simply a passive network of oscillators with a DC bias on each cell. That said, the hardware to implement this at scale does not currently seem to exist. To my knowledge, the state of the art is https://app.normalcomputing.ai/composer
From what I see, it's like mimicking the annealing process and the "derivative" automatically drives you to the solution. If that's the case, implementing such hardware should be not that hard except for the programmable coupling part. A bit off-topic, this reminds me of the duality between any deep forward network and a modern Hopfield network with some special energy functions, in which the duality is based on the fact that the forward running process can be seen as an energy minimization process.
The relationship with Hopfield networks sounds fascinating, would love to discuss further. As you mentioned, there is a connection to annealing in that we are encoding the solution to our problem in the minimization of a physical system's energy. Indeed, the all-to-all coupling is the hard part!
I'd say similarity, but any and all kinds of similarity you could ever think of - as long as you have enough dimensions and training data to feed it.
My current belief is that a hundred thousand+ dimensional latent space, as in SOTA LLMs, seems to have enough dimensions to reduce a large array of cognitive skills into a proximity search.
It's pretty arbitrary. For example, you can define order as the product of all order relationships along each single dimension (binary product). As for connectivity, they can also be defined as boolean functions under certain radius, like the persistent homology spectrum.
you can just use hammerspoon to get the same effect, turning off the bluetooth/wifi right after entering sleep mode and turning them back on when the Mac is unlocked.
