Using the Rust Language for Code Safety

In a recent dev oriented conversation someone mentioned Rust as a programing Language to improve code safety.   I heard about it again today re Facebook's usage.   Also the Rust Foundation seems to have support from major players.   Here is an overview and history of its use.  

A brief history of Rust at Facebook

Facebook is embracing Rust, one of the most loved and fastest-growing programming languages available today. In addition to bringing new talent to its Rust team, Facebook has announced that it is officially joining the nonprofit Rust Foundation. Alongside fellow members including Mozilla (the creators of Rust), AWS, Microsoft, and Google, Facebook will be working to sustain and grow the language’s open source ecosystem.

For developers, Rust offers the performance of older languages like C++ with a heavier focus on code safety. Today, there are hundreds of developers at Facebook writing millions of lines of Rust code. And while it’s clear that Facebook is increasingly invested in the future of the language, it’s important to understand how we grew to this point.  ... '

Think to Type Getting Closer

Though I remember this was close years ago.  Introducing anticipatory completion may help, but also with some of the usual errors. 

Facebook is inching closer to a think-to-type computer system
There's a long way to go to 100 word-per-minute mental typing, though.  By Steve Dent, @stevetdent in Engadget

Elon Musk isn't the only one who wants us to communicate via brainwaves. Facebook also has ambitious plans to interface with computers using wearables and one day let us type rapidly with our brains. Now, neuroscientists from the University of California, San Francisco (backed by Facebook's Reality Labs) have demonstrated a system that can translate speech into text in real time using brain activity only. While impressive, it shows that the technology still has a long ways to go.

Brain-computer interface systems already exist, but require users to mentally select a letter at a time on a virtual keyboard, a process that tends to be very slow. The UCSF researchers, however, tried to use context to help the machines translate entire words and phrases.   .... " 

Facebook Open Source AI

What Facebook is Doing for Open-Source AI.   Technical details and detailed pointers to resources.  Announces what they call Tensor Comprehensions.

Announcing Tensor Comprehensions

By: Nicolas Vasilache, Oleksandr Zinenko - Inria & DI ENS, Theodoros Theodoridis - ETH Zürich, Priya Goyal, Zachary DeVito, William S. Moses - MIT CSAIL, Sven Verdoolaege, Andrew Adams, Albert Cohen - Inria & DI ENS & FAIR

Today, Facebook AI Research (FAIR) is announcing the release of Tensor Comprehensions, a C++ library and mathematical language that helps bridge the gap between researchers, who communicate in terms of mathematical operations, and engineers focusing on the practical needs of running large-scale models on various hardware backends. The main differentiating feature of Tensor Comprehensions is that it represents a unique take on Just-In-Time compilation to produce the high-performance codes that the machine learning community needs, automatically and on-demand.  ....

What to expect next

This release will allow researchers and programmers to write layers in a notation that is similar to the maths they use in their papers and communicate concisely the intent of their program. They will also be able to take that notation and translate it easily into a fast implementation in a matter of minutes rather than days. As the toolchain grows, we expect usability and performance to increase and benefit the whole community.

We will release PyTorch integration for Tensor Comprehensions at a later date.

We are grateful for frequent exchanges with and feedback from the frameworks teams and are looking forward to bringing this exciting new technology to your favorite ML framework.

FAIR is committed to open science and working with the machine learning community to push AI research further. Tensor Comprehensions is already a collaboration between Facebook, Inria, ETH Zurich and MIT. Our work is in the early stages and we’re excited to share it early and look forward to improving it with feedback from the community.

Get started:
Tensor Comprehensions is available under the Apache 2.0 license.
Documentation
On ArXiv
On Slack
Email: tensorcomp@fb.com