There is absolutely no fundamental reason we build AI the way we do today. There certainly is a radically different approach that is orders of magnitude more energy efficient. I’m going to find it before I die
SF is probably the only place a company like Extropic could be birthed. Thanks so much to our early investors for believing in us. I truly believe we are addressing a deep problem with the way computing is done. Love this country 🇺🇸🇺🇸🇺🇸
1. You clearly don't know anything about probabilistic machine learning. That's ok; it's a graduate-level topic, and I wouldn't expect you to.
2. The 10,000x claim is actually ultra-specific and backed by a paper with a ~40-page appendix of machine learning and physics
Since we’re posting fun stuff that was never intended to see the light of day: here’s the result of a rabbit hole where I found mechanical analogies to superconducting qubit designs. The Zero-Pi qubit analog involves differential gearboxes (like from an automotive drivetrain).
Fun little paper to appear tonight on the arXiv.
How to do Hamiltonian Monte Carlo on digital Quantum Computers.
As physics-based probabilistic ML accelerators are on the horizon, important to test how QC's could try to compete.
Best way to predict future is to invent it.🙂
Come work for us if you want to try and figure out how to re-build machine learning on top of an entirely new hardware primitive (EBM sampling instead of matmuls)