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Huge Investment In Self-Driving Companies: These Startups Are Noteworthy

Synced Global AI Weekly February 17th

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Amazon Dives into Self-Driving Cars with A Bet on Aurora
Amazon Wednesday made perhaps its most significant move yet into the self-driving car space, announcing an investment in autonomous tech developer Aurora. For a company with one of the largest logistics operations on the planet, it’s about time.
(Wired)


Robot Delivery Startup Nuro Raises Nearly $1 Billion from SoftBank
Driverless delivery startup Nuro just got a rocket-fueled boost from SoftBank. The company announced Monday that it has raised $940 million in financing from the Japan’s tech company’s Vision Fund, an gigantic sum that will help accelerate Nuro’s mission to put driverless delivery vehicles on the road en masse.
(The Verge) 


First Robo-Trucking Unicorn? TuSimple Delivers $95 Million Funding Round
TuSimple, a U.S.-Chinese tech startup racing to commercialize self-driving capability for long-haul semi-trucks, just raised a further $95 million in a funding round that the company estimates moves it to unicorn status with a valuation now reaching $1 billion.
(Forbes) 


Ford And VW Discuss Autonomous Car Team-Up at A $4 Billion Valuation
Ford Motor Co. and Volkswagen AG are progressing toward a potential agreement to join forces on self-driving cars and have overcome earlier obstacles, according to people familiar with the talks. Breakthroughs in the deliberations Thursday include a possible framework for Volkswagen to work with and invest in Argo AI, the Ford-backed autonomous-vehicle startup.
(Bloomberg) 

Technology

Better Language Models And Their Implications
We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization — all without task-specific training.
(OpenAI) 


Uber’s Code-Free Ludwig: Deep Learning for Dummies?
Uber has unveiled Ludwig, a new TensorFlow-based toolkit that enables users to train and test deep learning models without writing any code. The toolkit will help non-experts understand models and accelerate their iterative development by simplifying the prototyping process and data processing.
(Synced) / (Uber Engineering Blog) / (GitHub) 


Introducing PlaNet: A Deep Planning Network for Reinforcement Learning
In collaboration with DeepMind, Google AI presents the Deep Planning Network (PlaNet) agent, which learns a world model from image inputs only and successfully leverages it for planning. PlaNet solves a variety of image-based control tasks, competing with advanced model-free agents in terms of final performance while being 5000% more data efficient on average.
(Google AI)


Under The hood: Portal’s Smart Camera
A powerful feature of Portal, Facebook’s new video-calling device, is an AI-powered system called Smart Camera. Smart Camera frames shots much as an experienced camera operator would, so that people using Portal feel like they are right beside each other. Instead of relying on dedicated servers typically used for advanced machine learning tasks, Smart Camera does this by performing complex computer vision (CV) modeling entirely on-device, using processing power similar to that of a high-end mobile chipset.
(Facebook AI)

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IBM AI Debater Vs Human Champ
Debating is a hallmark of human civilization, and few do it better than World Debating Championship Finalist Harish Natarajan. Last night at Yerba Buena Center for the Arts in San Francisco, Natarajan stepped up against an AI-empowered debating machine from IBM.
(Synced)


Federated Learning: The Future of Distributed Machine Learning
In 2017 Google introduced Federated Learning (FL), “a specific category of distributed machine learning approaches which trains machine learning models using decentralized data residing on end devices such as mobile phones.” A new Google paper has now proposed a scalable production system for federated learning to enable increasing workload and output through the addition of resources such as compute, storage, bandwidth, etc.
(Synced)

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