No doubt you have heard about it by now. Above is the link to the Reddit discussion, while this is the link to the Coursera specialization. So much to study, so little time!! Testing our agents in games that are not specifically designed for AI research, and where humans play well, is crucial to benchmark agent performance. That is why we, along with our partner Blizzard Entertainment, are excited to announce the release of SC2LE, a set of tools that we hope will accelerate AI research in the real-time strategy game StarCraft II. This includes an API for machine learning which hooks into a given game, a dataset of anonymized game replays (increasing to 500K in the coming weeks), and an open source version of PySC2, DeepMind's toolset.

In June on /r/MachineLearning we learned of funding to a popular (and essential) Python project, are treated to a collection of machine learning cheat sheets, see how deep learning is done on premium cable television, read about Andre Karpathy's new job, and are introduced to a new machine learning "IDE." This is good news for the project. For the first time ever, NumPy -- a core project for the Python scientific computing stack -- has received grant funding. The proposal, "Improving NumPy for Better Data Science" will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science. The principal investigator is Dr. Nathaniel Smith.

While feats of Deep Learning has been gathering much attention, there were also breakthroughs in a related technology of Recurrent Neural Networks (RNN). RNNs hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation. RNN is learning to paint house numbers (Andrej Karpathy) See a fantastic post by Andrej Karpathy, "The Unreasonable Effectiveness of Recurrent Neural Networks" where he uses RNNs to do amazing stuff like paint house numbers in this image, or generate text in the style of Paul Graham, Shakespeare, and even Latex. See below an excellent tutorial "General Sequence Learning using Recurrent Neural Networks" by Alec Radford, Indico Head of Research, who led a workshop on general sequence learning using recurrent neural networks at Next.ML in San Francisco, Feb 2015. Alec introduces RNNs and sketches how to implement them and cover the tricks necessary to make them work well.

AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a self driving car, but then crashed in cases a human would have easily handled. AlphaGo beat the human champion Go player years before most experts possible, but completely collapsed after its opponent played an unusual move.

In June on /r/MachineLearning, there were free videos, free books, free courseware, and a quality curriculum made up of free offerings. The word of the month for June is clearly a four letter word starting with'F'. This lot of videos covers a wide range of topics, from general AI, to design issues, to cloud computing, to a variety of machine learning topics and beyond. Microsoft Research has added heavily to these offerings on what seems to be a daily basis since this Reddit post as well. Free knowledge from a top research institute in the field is always welcome.

In May on /r/MachineLearning we get jokes, more jokes, bad news about freely-available study material, good news about some other freely-available study material, some videos, news from Google, and a walkthrough for setting up a deep learning machine. This bit of news has made the rounds over the past week, so you may have already heard: Andrej Karpathy has been forced to take down the previously publicly-available videos for his Convolutional Neural Networks course at Stanford. This is a link to the tweet announcing it. Long-time Python tutorial make sentdex has shared his latest series of machine learning video tutorials, aimed at beginner to intermediate programmers. The most recent series is an in-depth machine learning course, aimed at breaking down the complex ML concepts that are typically just "done for you" in a hand-wavy fashion with packages and modules.

April on /r/MachineLearning brings top posts in deep learning video tutorials and books, the TensorFlow Playground, deep conversation centered on an xkcd comic from 2014, Microsoft cognitive APIs, and a meta-conversation on the subreddit's direction. The Google Developer YouTube channel has launched a new video series, titled Machine Learning Recipes. There are 3 videos in the playlist, as of this writing. The series, hosted by Josh Gordon, consists of video topics such as "What Makes a Good Feature?" and "Visualizing a Decision Tree." This link is directly to the first of the videos.

April on /r/MachineLearning brings top posts in deep learning video tutorials and books, the TensorFlow Playground, deep conversation centered on an xkcd comic from 2014, Microsoft cognitive APIs, and a meta-conversation on the subreddit's direction. The Google Developer YouTube channel has launched a new video series, titled Machine Learning Recipes. There are 3 videos in the playlist, as of this writing. The series, hosted by Josh Gordon, consists of video topics such as "What Makes a Good Feature?" and "Visualizing a Decision Tree." This link is directly to the first of the videos.