I haven’t heard about thermodynamic computing before, but it sounds pretty interesting. As IEEE Spectrum explains , “the components of a thermodynamic chip begin in a semi-random state. A program is fed into the components, and once equilibrium is reached between these parts, the equilibrium is read out as the solution. This computation style only works with applications that involve a non-deterministic result … various AI tasks, such as AI image generation and other training tasks, thrive on this hardware.” It sounds almost like quantum computing to my layperson ears. [edit: fixed link]
It makes some sense to handle self-discovered real numbers of infinite precision using analog methods, though I’m curious about how they handle noise, since in the real world and unlike the mathematical world all storage, transmission and calculations have some error.
That said, my experience way back with a project I did at Uni with Neural Networks in their early days, is that they’ll even make up for implementation bugs (we managed about 85% rate of number recognition with a buggy implementation) so maybe that kind of thing is quite robust in the face of analog error.