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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.

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OpenAI Guards Its ML Model Code & Data to Thwart Malicious Usage

The San Francisco-based AI non-profit however has raised eyebrows in the research community with its unusual decision to not release the language model’s code and training dataset. In a statement sent to Synced, OpenAI explained the choice was made to prevent malicious use: “it’s clear that the ability to generate synthetic text that is conditioned on specific subjects has the potential for significant abuse.”

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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.

AI Interview

Google Brain Research Scientist Quoc Le on AutoML and More

The Synced Lunar New Year Project is a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this second installment (click here to read the previous article on Clarifai CEO Matt Zeiler), Synced speaks with Google Brain Researcher Quoc Le on his latest invention, AutoML, Google Brain’s pursuit of AI, and the secret of transforming lab technologies into real practices.

AI Interview

David vs Goliath: Clarifai CEO Matt Zeiler Takes On the Tech Giants

This is the first installment of the Synced Lunar New Year Project, a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this article, Synced chats with Clarifai Founder and CEO Matt Zeiler on recent progress in computer vision and his company’s plans for the future. Founded in New York in 2013, Clarifai produces advanced image recognition systems.

AI Study Guide

Papers With Code Adds State-of-the-Art Features

Papers With Code is a unique and useful resource that presents trending ML research along with the code to implement it. The site was created by Atlas ML CEO Robert Stojnic, aka “rstoj” on Reddit’s machine learning board. The latest version of Papers With Code has added 950+ unique machine learning tasks, 500+ State-of-the-Art result leaderboards and 8500+ papers with code.

AI Conference

AAAI-19 Announces Best Papers

The 33rd AAAI Conference on Artificial Intelligence (AAAI-19) is now underway in Hawaii, USA. The program chairs are Pascal Van Hentenryck (Georgia Institute of Technology, USA) and Zhi-Hua Zhou (Nanjing University, China).

AI Asia Japan News

Meet Fujitsu’s AI Gymnastics Judges

The International Gymnastics Federation (FIG) recently approved the use of a “Judging Support System” developed by Fujitsu for a series of FIG gymnastics events in 2019. The system will be tested at the 2019 FIG World Cup Series, then officially launched for the 49th Artistic Gymnastics World Championships in Stuttgart, Germany this coming October.

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GG! DeepMind Struts Its StarCraft Strength; Humans Strike Back

DeepMind bot AlphaStar has scored a convincing 10/10 victory against pro human players in a special series of StarCraft II matches. Plucky 26 year-old Polish gamer Grzegorz “MaNa” Komincz however salvaged a bit of human pride, snatching a surprise win yesterday in a live rematch at the DeepMind and Blizzard Entertainment Starcraft II Demonstration live stream event hosted in London.

AI

You Can’t Keep an RL-Powered ANYmal Down

ANYmal does not have an easy life. One of the four-legged robot’s main tasks is to learn how to stand up again — no matter how many times it is kicked, pushed or otherwise tumbles to the ground. A research team from Switzerland’s ETH Zurich University trained ANYmal using reinforcement learning (RL) and published their work last Wednesday.