Excited to share FloatLib, our verified arbitrary-precision floating-point arithmetic library in Lean.
We’ve spent several months trying to bring together the best of both worlds: arithmetic we can prove correct and implementations that run efficiently.
We built FloatLib to support verified machine learning and scientific computing, where rounding, overflow, and accumulation can change a program’s result.
FloatLib supports IEEE binary and decimal, arbitrary-width posits, P3109, and small ML formats. You can also define your own formats and rounding rules.
Each certified software backend comes with a Lean proof that it computes the specified result, including signed zeros and exceptional values.
A lot of the work went into making those implementations faster, with lookup tables for tiny formats, machine-word kernels, and limb algorithms for wider arithmetic. They share the same specifications, so each optimization must come with a proof that it preserves the result.
We also put FloatLib through extensive numerical checks and speed comparisons with established libraries, including MPFR, Flocq, FLoPS, Berkeley SoftFloat/TestFloat, and the posit libraries SoftPosit and Universal, across a range of formats, operations, and precisions. @Robertljg
Project & Paper: https://t.co/OdTmKnT0Yy
Code: https://t.co/aTq7jCsVLD
Chatgpt and alphafold already literally have a record of being used to heavily assist people to create new mRNA vaccine targeting cancer (for dogs). (This was last year when chatgpt wasnt able to solve any hard conjectures remember?)
I am certain that it can be used to massively accelerate creation of vaccine for chimps / humans. It will take time until it reaches end patients until safety and passes trials and stuff obviously, but we are clearly beyond making progress.
How a robot arm is controlled, explained for ML people new to robot learning. First of the explainers from my own speedrun.
Next one will be on ACT. Follow me to catch it. https://t.co/g25ayJDVhe