Waymo Teams Up With DeepMind; Lyft Shares Large-Scale Dataset

Published in

SyncedReview

4 min readJul 28, 2019

Subscribe to Synced Global AI Weekly

Waymo and DeepMind Mimic Evolution to Develop A New, Better Way to Train Self-Driving AI
Alphabet’s autonomous driving and robotaxi company Waymo teamed up with fellow Alphabet company and AI specialist DeepMind to develop new training methods that would help make its training better and more efficient. DeepMind says in a blog postthat using PBT decreased by 24% false positives in a network that identifies and places boxes around pedestrians, bicyclists and motorcyclists spotted by a Waymo vehicle’s many sensors.
(TechCrunch) / (DeepMind)

Moving Autonomous Vehicles Forward, Together
Lyft shares a comprehensive, large-scale dataset featuring the raw sensor camera and LiDAR inputs as perceived by a fleet of multiple, high-end, autonomous vehicles in a bounded geographic area. This dataset also includes high quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map.
(Lyft)

GM Cruise to Delay Commercial Launch of Self-Driving Cars to Beyond 2019
General Motors Co’s self-driving unit, Cruise, said on Wednesday it was delaying the commercial deployment of cars past its target of 2019 as more testing of the vehicles was required. Cruise Chief Executive Officer Dan Ammann said the company would expand testing in San Francisco.
(Reuters)

Technology

Lookahead Optimizer: K Steps Forward, 1 Step Back
Researchers propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by \emph{looking ahead} at the sequence of “fast weights” generated by another optimizer.
(Vector Institute)

Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution
Uber AI researchers built EvoGrad, a Python library that gives researchers the ability to differentiate through expectations (and nested expectations) of random variables, which is key for estimating NES gradients. The idea is to enable more rapid exploration of variants of NES, similar to how TensorFlow enables deep learning research.
(Uber Engineering)

Learning Better Simulation Methods for Partial Differential Equations
Researchers explore a potential path for how ML can offer continued improvements in high-performance computing, both for solving PDEs and, more broadly, for solving hard computational problems in every area of science.
(Google AI)

You May Also Like

Meet Your AI-Generated Dream Anime Girl
Do you dream of Asuna Yuuki? Do you long to escape to a fantasy world with a beautiful anime partner? If so there’s a new artificial intelligence system just for you — the “Waifu Vending Machine” can create a highly customized anime companion in minutes.
(Synced)

Alibaba Is Open-Sourcing Its Powerful New RISC-V Processor for 5G and AI
Alibaba’s chip subsidiary Pingtouge (平头哥) yesterday introduced its first product, an RISC-V (Reduced Instruction Set Computer) processor. The Xuantie 910 will be used as a core IP to produce high-end edge-based microcontrollers (MCUs), CPUs, and systems-on-chips. It is tailored for 5G, artificial intelligence, and Internet-of-things (IoT).
(Synced)