Hell, Microsoft issued an Education Edition of the popular PC game earlier this year, s targeted at use in a classroom setting. Turns out the game could also prove a useful tool for helping artificial intelligence be more, well, intelligent. Back in March, Microsoft Research showcased the work it was doing with Project Malmo, a platform designed to leverage Minecraft as a means of helping improve AI problem solving, using machines to accomplish tasks and create items in the blocky game. Now the company is bringing Malmo to the GitHub-using masses, courtesy of an open-source license in a private preview. Katja Hoffman of MS's Cambridge, UK lab highlighted the key of teaching AI fundamental connections that go build simple pattern recognition.

The White House wound up a nationwide series of workshops on artificial intelligence today on a cautionary note: Yes, AI promises to ease many of humanity's ills, but humanity needs to make sure that flesh-and-blood policymakers are firmly in charge. Latanya Sweeney, director of the Data Privacy Lab at Harvard's Institute of Quantitative Social Science, said AI programs should be made to reflect the norms agreed upon by human society. "I want the people we elect controlling those norms, not the technology itself. Those norms should include supporting social equity and diversity, said Alicia Glen, New York City's deputy mayor for housing and urban development. "At its best, artificial intelligence can be a tool to promote equity, and it obviously can create huge economic opportunity for a lot of people," she said. "But it can also have discriminatory effects, whether they're intended or unintended.

This is a relatively small (read: exclusive) workshop (less than 30 attendees), fostering close interaction and collaboration between attendees. The workshop is based on a challenge-response format where a series of (shorter than typical) presentations outline important challenges (rather than results), which are then collaboratively addressed by several small teams which subsequently present their results to the whole group. Presentations are 25-minutes long, followed by 45-minute teamwork. Results of teamwork is subsequently presented to the whole group in a 45-minute session. The workshop concludes on the third day (optional) with a half-day trip (9:00 - 14:00) to the Icelandic countryside (depending on sufficient sign-up).

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Sephora is doubling down on augmented reality and artificial intelligence sales tactics by enabling shoppers to virtually try on false lashes, watch tutorials using their own image and engage via a chatbot to trial and purchase lip color. With Sephora's customers virtually trying on more than 70 million lip shades using the Virtual Artist in-app functionality that was introduced earlier this year, false lashes are being added to expand the program. Users of the Sephora application can also now experience live step-by-step makeup application tutorials using their own uploaded images and augmented reality technology. "This is a significant expansion because we are adding elements that we know will help empower and educate our clients' purchase making decisions, and they're done in a way that is fun and engaging," said Bridget Dolan, vice president of Sephora Innovation Lab. "The new Live Tutorials especially are a game changer for our users," she said.

There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been shown to be particularly effective when stacked into a deep configuration, achieving state-of-the-art results on a diverse array of problems from language translation to automatic captioning of images and videos. In this post you will get a crash course in recurrent neural networks for deep learning, acquiring just enough understanding to start using LSTM networks in Python with Keras.

The United States Air Force wants robots and service members to be best buds on the battlefield. Last Friday, the Air Force announced a grant of 7.5 million for research on ways to make humans trust artificial intelligence (AI) so that people and machines can collaborate on missions. Soon service members in every branch of the Armed Forces will be working with AI on a daily basis--be it unmanned ariel vehicles, underwater drones, or robot soldiers (the U.S. Military had to shelve the Boston Dynamic L3 "robotic mule" because it was too loud, but last week the company revealed the much stealthier SpotMini). On November 1, 2014, (one week after Elon Musk compared developing AI to "summoning the demon") Undersecretary of Defense Frank Kendall issued a memo asking the Defense Science Board to study what issues must be solved in order to expand the use of AI "across all war-fighting domains." But robotic weapons and soldiers wont be as effective if their human counterparts don't trust them.

Machine learning is a branch of artificial intelligence concerned with the design of data-driven programs which autonomously demonstrate intelligent behavior in a variety of domains. Machine learning systems are all around us. When you deposit a check, scan your fingerprint, or post a picture on social media, autonomous algorithms are deployed on the spot to sift through and make sense of your constant interactions with our technology. Machine learning silently underpins the fabric of our digital infrastructure, discriminating spam e-mail and banking fraud, making light-speed transactions in the global financial market, recommending music and films for customers to buy, deciding what search results are relevant to your queries, and countless more of the daily interactions with electronic media that we take for granted. Machine learning is the backbone that powers self-driving cars, content recommendation in social media, face identification in digital forensics, and countless other high-level tasks.

Machine learning is responsible for a great deal of our tech advancements, from speech recognition to self-driving cars, smart homes and beyond. This developing field of study explores the techniques used to give computers the ability to learn without being explicitly programmed, creating a new world of technological opportunities. If breakthrough tech fascinates you, you can develop the skills required to deep-dive in an industry that's on track to change the world with the 10-course instruction provided in the Complete Machine Learning Bundle, now available at a 94 per cent off from Pocket-lint Deals. The Complete Machine Learning Bundle offers high-level learning through 10 comprehensive courses and 63 hours of training, putting you in command of an exciting field with high-value applicability everywhere from the startup world to Fortune 500 companies and beyond. Develop your skills in Python programming and Python libraries for Machine Learning, utilizing Hadoop and MapReduce to process and manage enormous amounts of data efficiently.

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.