This may not be the best deep learning framework, but it is a deep learning framework. Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. Support the simple basic ops, and you get SOTA vision models/efficientnet.py and language models/transformer.py We are working on support for the Apple Neural Engine and the Google TPU in the accel/ folder. Eventually, we will build custom hardware for tinygrad, and it will be blindingly fast.

JD.com, one of China's largest e-commerce companies, is launching a new Beijing-based accelerator program for artificial intelligence and blockchain startups. Called AI Catapult, its first batch includes six companies: Bankorus, CanYa, Bluezelle, Nuggets, Republic Protocol and Devery. In an announcement, JD.com said startups will work with its operational teams to "test real-world applications of their technologies at scale." This includes its logistics unit, which recently raised $2.5 billion and claims to run the largest last-mile logistics network in China. Though Alibaba Group is probably better known outside of China, JD.com is a formidable rival.

When the artificial intelligence program AlphaGo defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into the AlphaGo servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that define how humans approach the game.