DeepMind Alberta will turbo-charge the research ecosystem, mirroring the partnerships DeepMind has nurtured with the top academic institutions in the UK,

The bot by OpenAI demonstrated its skills at the annual live tournament "The International" this past weekend. It was up against a crowd favorite, the pro player "Dendi" who is one of the best in the world when it comes to Dota 2. He was defeated by the bot twice, before forfeiting any more future matches. After the game, Dendi stated that the bot "feels a little like [a] human, but a little like something else." - many of its moves looked familiar and human. He was surprised that it was possible for the AI machine to beat a human player - and therefore human intelligence. 'Bot is really fun and challenging to play against.

Welcome to the IoT weekly round-up – your link to the latest from the connected world. This week, customers in Canada and the U.S. can use Google Home to make phone calls, Microsoft's been testing autonomous gliders, and machine learning could help unearth new species of plant life from among centuries of unclassified data. Google has got its hands on AIMatter – a startup that has created a neural network AI platform to see and process images, which it then uses to run programs. The Belarus company has also built an app for photo and video editing, so that users can apply masks, backgrounds, colour-swaps and other creative wizardry to their photos. Early information about the deal suggests that most of AIMatter's employees will move over to Google, though it's not clear whether that will mean they have to relocate.

Welcome to the Lenet tutorial using TensorFlow. From being a long time user of Theano, migrating to TensorFlow is not that easy. Recently, tensorflow is showing strong performance leading to many defecting from theano to tensorflow. I am one such defector. This repository contains an implementation Lenet, the hello world of deep CNNs and is my first exploratory experimentation with TensorFlow.

The Hype Cycle for Emerging Technologies, 2017 provides insights gained from evaluations of more than 2,000 technologies the research and advisory firms tracks. From this large base of technologies, the technologies that show the most potential for delivering a competitive advantage over the next five to 10 years are included in the Hype Cycle. The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms.

My aim with this research is to allow me to quickly find the most relevant and appreciated articles about the topic of Artificial intelligence (A.I), Machine Learning(M.L.) and Deep Learning(D.L), so that I can improve my knowledge about the subject, without having to read 100 articles to find the 4–5 of them that are interesting and exciting for me. After scrapping 6 million posts from medium.com in the winter of 2016, I was able to filter just the posts that are tagged with either AI/ML or Deep Learning. In total, i got 6,546 articles, written by 3,528 users. All of this articles have a total of 153,567 recommends. So, to do this, I extracted the TOP 100 articles posted on medium, related to AI/ML/DL and created a list with all of them, so that I can read them in the coming weeks.

Today only, get this amazing ebook for just $0.99. Machine learning is currently one of the most talked about concepts in the world of technology and computers. A highly promising topic, machine learning is also quite controversial among people who are not aware of its nature and benefits. Therefore, to do away with such myths and apprehensions, it has become essential for everyone to find out and read about the concept. This book will help you with this mission, as you will find all the required and relevant data regarding machine learning gathered in one single text.

Quotage: "We are still decades from Star Trek-style artificial general intelligence that could pass the Turing test or outperform humans on a gamut of unrelated cognitive tasks. In the meantime, a promising compromise would be the ability to automate model selection and tuning based on the problem and available data, and then select the best options from a portfolio of deep learning software each designed for different applications." Thus the theme of Marko's useful offering, which gets into the practicalities of "automated algorithm selection," where the proper algorithm for a narrower use case is machine-determined. I can see why Marko argues that helping companies sort the right algorithms could make up for lack of internal data science expertise. More APIs and "metadata taxonomies" are needed.

Machine learning, deep learning and the connected home have reached the peak of the Hype Cycle, while blockchain and commercial drones have begun the slide into the'trough of disillusionment'. According to Gartner's 2017 Hype Cycle for Emerging Technologies, augmented reality and virtual reality have begun to climb the'plateau of productivity' as they approach mainstream adoption. The Hype Cycle for Emerging Technologies report is the longest-running annual Gartner Hype Cycle, providing a cross-industry perspective on the technologies and trends and how long it will take for them to reach mainstream adoption. Gartner is tipping artificial intelligence technologies will be the most disruptive class of technologies over the next decade, giving organisations the ability to harness data insights to adapt to new situations and solve problems. But for certain areas mainstream adoption of AI is still a decade away.

Deep multi-task learning with low level tasks supervised at lower layers.