Computational scientific discovery is at an interesting juncture. While we have
mechanistic models of lots of different scientific phenomena, and reams of data
being generated from experiments - our computational capabilities are unable to
keep up. Our problems are too large for realistic simulation. Our problems
are multiscale and too stiff. Our problems require tedious work like
calculating gradients and getting code to run on GPUs and supercomputers.
Our next step forward is a combination of science and machine learning, which
combines mechanistic models with data based reasoning, presented as a unified
set of abstractions and a high performance implementation. We refer to this as
scientific machine learning.

Scientific Machine Learning, abbreviated SciML, has been taking the academic
world by storm as an interesting blend of traditional scientific mechanistic
modeling (differential equations) with machine learning methodologies like
deep learning. While traditional
deep learning methodologies have had difficulties…

Со всем этим бардаком/кризисом/сингулярностью/апокалипсисом/правительственным террором/(подставьте сами какой-нибудь экзистенциальный риск) может справиться только достаточной силы интеллект, ибо интеллект это и есть решатель новых классов проблем (мы тут идём по линии, предлагаемой François…

I am excited to announce AlphaZero.jl: a generic, simple and fast implementation of Deepmind’s AlphaZero algorithm.   The core algorithm is only 2,000 lines of pure, hackable Julia code. Generic interfaces make it easy to add support for new games or new learning frameworks. Being between one and two orders of magnitude faster than competing alternatives written in Python, this implementation enables you to solve nontrivial games on a standard desktop computer with a GPU.  Why should I care abou...