Where do we stand with neuromorphic hardware?

I will only quote:

> Mike Davies at Intel [...] says the real bottleneck actually lies in the layers of software needed to take real-world problems, convert them into a format that can run on a neuromorphic computer and carry out processing

> James Knight at the University of Sussex, UK [...] points out that current models like ChatGPT are trained using graphics cards operating in parallel, meaning that many chips can be put to work on training the same model. But because neuromorphic computers work with a single input and cannot be trained in parallel, it is likely to take decades to even initially train something like ChatGPT on such hardware, let alone devise ways to make it continually learn once in operation, he says

From a very recent https://www.newscientist.com/article/2426523-intel-reveals-w...

I can tell you about the software side on which I have some experience. Spiking neural networks are strictly more powerful than conventional neural networks which in turn are strictly more powerful than hand-coded rules engines. So the idea is that neuromorphic systems will some day supplant conventional neural networks in the same way they supplanted rules engines (e.g., for machine translation). However, as it stands the theoretical benefits of neuromorphic hardware has not yet been proven. Which is perhaps because hardware and software needs to mature... Like how neural nets were thought of as toys for many decades before they became practical. More brainlike doesn't necessarily translate to higher performance.

I'd say the big catch is the huge advantage conventional neural nets have. In hardware and software support.