DeepMind Has Quietly Open Sourced Three New Impressive Reinforcement Learning Frameworks by @jrdothoughts via @Medium

DeepMind has been releasing a series of open source technologies to help streamline the adoption of deep reinforcement learning (DRL) methods including the recent release of the OpenSpiel, SpriteWorld, and bsuite DRL stacks.

This is great news for anyone who wants or needs to try DRL  Definitely something to look at.

How DeepMind's biggest AI project is fixing bad Android batteries by Matt Burgess via @WiredUK

Google's Android Pie operating system uses DeepMind's AI in a bid to improve your phone's battery life. But is it making any difference?

This sounds great and of course over time it will get even better.

Will Democracy Survive Big Data and Artificial Intelligence? via @sciam

Everything is becoming "intelligent." Soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet?

This is a great article that is very well written and thought out. Worth the time to read.

Using Machine Learning to predict parking difficulty via @googleresearch

Google released a feature for Google Maps for Android in 25 US cities that predicts parking difficulty close to your destination so you can plan accordingly. To build it, Google had to overcome obstacles like the lack of real-time information about parking spots, high variability (day, time, work day, events, etc.), difficult to graph parking structures, and illegal parking. Google used a combination of crowdsourcing and machine learning to address those issues. Here's how they did it.

Posted by James Cook, Yechen Li, Software Engineers and Ravi Kumar, Research Scientist

Interesting feature that could be very useful in London or any other busy city. Lets hope they are able to roll it out.

App Discovery with Google Play Parts 1,2 and 3 via @googleresearch

This is a multi part blog on the Google Research Blog:

Part 1: Understanding Topics by Malay Haldar, Matt MacMahon, Neha Jha and Raj Arasu, Software Engineers

Part 2: Personalised Recommendations with Related Apps by Ananth Balashankar & Levent Koc, Software Engineers, and Norberto Guimaraes, Product Manager

Part 3: Machine Learning to Fight Spam and Abuse at Scale by Hsu-Chieh Lee, Xing Chen, Software Engineers, and Qian An, Analyst

These are great posts and this blog is well worth following.

The world’s best Go player says he still has “one last move” to defeat Google’s AlphaGo AI by @pingroma via @qz

Over the past few days, Google’s Deepmind machine-learning team secretively put its AlphaGo artificial intelligence system onto two Chinese online board-game platforms to test its skill in fast-paced games against several of the world’s best Go players. It won every game it played. Go has become the province of AI, and DeepMind further proves that GANs are an extremely promising approach.

Great progress by the Google Team.

Google DeepMind Makes AI Training Platform Publicly Available by @jeremyakahn via @technology

DeepMind is open-sourcing the source code for its training environment (previously called Labyrinth, now DeepMind Lab). You can download the code and customise it to help train your own artificial intelligence systems—or create new game levels for DeepMind Lab and upload these to GitHub.

Something fun to have a play with and see what you can achieve.

What To Expect from Deep Learning in 2016 and Beyond by Sophie Curtis via @kdnuggets

This post is a collection of the opinions of many of deep learning's foremost researchers. I'll share two great nuggets here. From Andrej Karpathy of OpenAI: "We're learning what the lego blocks are, and how to wire and nest them effectively to build large castles." From Christian Szegedy of Google: "algorithms will become so efficient that they will be able to run on cheap mobile devices, even without extra hardware support or prohibitive memory overhead".

Interesting views from big players in the area.

We need to talk about AI and access to publicly funded data-sets by @riptari via @techcrunch

This is a thoughtful article about who should own the value that's locked up in our data.

This is really interesting and points out a few things that I never know or thought about it.

Should the NHS share patient data with Google's DeepMind? via @Wired

Should the NHS share patient data with Google's DeepMind? by Subhajit Basu via +WIRED - In giving Google access to the healthcare data of nearly 1.6 million patients, the NHS has used a loophole around "implied consent".

I have no contact with that NHS Trust but it is a good point as to if the people gave specific permission for it.