For china it's DUV+packaging for now, NIL/DSA mid-term, and MoS₂/2D chips long term. But wafer scale, defect free 2D logic is 20–30 yrs out, so no EUV shortcut anytime soon

But one of the early deep learning papers from Alex Graves does this really well with LSTMs - https://arxiv.org/abs/1308.0850

Implementation - https://www.calligrapher.ai/

Often used for horns, violin, guitar cabs - sources where you want to reduce "shrillness" but you can use them anywhere.

All the promises of integrating deeply into the OS and have APIs directly into apps so you can use natural language to get any app to do stuff for you is vaporware

So much potential but none of it delivered yet, I hope it will change soon

beautiful.

AlphaProof does do some kind of neural network guided search and automated theorem proving to validate it https://deepmind.google/discover/blog/ai-solves-imo-problems...

But it's still fairly brute force and inefficient

My intuition is that some people are able to develop abstractions on top of abstractions (compression is a key part of intelligence) that allow them to traverse the search space much, much faster than without these, and with enough repetition this becomes routine, even faster in the brain.

I don't think we have a good theory of how this works, or at least I haven't come across it.

Neural network guided search is somewhat similar but I think we are missing several key pieces

Re:using RL and other types AI assistance for chip design, Nvidia and others are doing this too