THE world is a confusing place, especially for an AI. But a neural network developed by UK artificial intelligence firm DeepMind could help bring it into focus by giving computers the ability to understand how different objects are related to each other. Humans use this type of inference – called relational reasoning – all the time, whether we are choosing the best bunch of bananas at the supermarket or piecing together evidence from a crime scene. The ability to transfer abstract relations – such as whether something is to the left of another object or bigger than it – from one domain to another gives us a powerful mental toolkit with which to understand the world. It is a fundamental part of our intelligence, says Sam Gershman, a computational neuroscientist at Harvard University.

The Georgia Tech Center for Music Technology, led by Gil Weinberg, has a reputation for doing incredible musical things with robots, with a mix of creativity and technical expertise in robotics and AI. We've seen projects like a cybernetic second arm for a drummer, a cybernetic third arm (!) for a drummer, and a bunch of interesting research on ways that robots can dynamically collaborate with humans in the context of improvisational music. That last thing usually features Shimon, a four-armed expressive robotic marimba player, which can analyze music in real time and improvise along with human performers. It's an impressive thing to watch, but Shimon's talents were mostly restricted to riffing on what other human musicians were doing. Now, Shimon has leveraged deep learning to create structured and coherent and totally unique compositions of its very own.

AI isn't going to be a threat to humanity because it's evil or cruel, AI will be a threat to humanity because we haven't properly explained what it is we want it to do. Consider the classic "paperclip maximizer" thought experiment, in which an all-powerful AI is told, simply, "make paperclips." The AI, not constrained by any human morality or reason, does so, eventually transforming all resources on Earth into paperclips, and wiping out our species in the process. As with any relationship, when talking to our computers, communication is key. That's why a new piece of research published yesterday by Google's DeepMind and the Elon Musk-funded OpenAI institute is so interesting. It offers a simple way for humans to give feedback to AI systems -- crucially, without the instructor needing to know anything about programming or artificial intelligence.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities. The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used a branch of AI called reinforcement learning to play the Atari 2600 version of Ms. Pac-Man perfectly. Using that method, the team achieved the maximum score possible of 999,990. Doina Precup, an associate professor of computer science at McGill University in Montreal said that's a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Ms. Pac-Man among the most difficult to crack. But Precup said she was impressed not just with what the researchers achieved but with how they achieved it.

Artificial intelligence is not a contact sport. Currently, algorithms mostly just compete to win old Atari games, or accomplish historic board gaming feats like owning five human Go champions at once. These are just practice rounds, though, for the way more complicated (and practical) goal of teaching robots how to navigate human environments. Vicarious, an AI company, has developed a new AI that is absolutely slammin' at Breakout, the paddle vs. brick arcade classic. Its AI, called Schema Networks, even succeeds at tweaked versions of the game--for instance, when the paddle is moved closer to the bricks.

Element AI, an artificial intelligence company that delivers groundbreaking AI solutions, announced today it has raised $102M USD, representing the largest Series A funding round for an artificial intelligence company in history. With this funding, Element AI will accelerate its capabilities and invest in large-scale AI projects internationally, solidifying its position as the largest global AI company in Canada and creating 250 jobs in the high tech sector by January 2018. Element AI solves impossible problems for global organizations that urgently need to use AI in combination with their proprietary and valuable data to leap ahead of their competitors. Serial entrepreneurs Jean-François Gagné and Nicolas Chapados, Real Ventures, and Yoshua Bengio, a co-father of deep learning technology, co-founded Element AI in October 2016 to empower industry with the massive scale of academic AI innovation Bengio was driving at the world-leading Montreal Institute of Learning Algorithms (MILA). Together with MILA, one of the three leading centers of AI research in the world, Element AI pioneered a unique, non-exploitative model of academic cooperation they have now replicated to many other institutes.

The method used to master Ms. Pac-Man could help demonstrate the benefits of a divide-and-conquer approach in real-world applications. A team of researchers have managed to develop an artificial intelligence capable of mastering the arcade classic Ms. Pac-Man. Maluuba -- a Canadian deep learning startup that was acquired by Microsoft in January 2017 -- used a divide-and-conquer technique to empower its system to complete the Atari 2600 version of the game with a perfect score of 999,990. Maluuba's approach is interesting, because it breaks down the strategies and maneuvers required to beat the game into their component parts. Various different agents focus on one job and one job alone, while an agent put in charge of managing from the top makes high-level decisions about what actions should be prioritized.

This article is part of a special insideHPC report that explores trends in machine learning and deep learning. The complete report, covers how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started and more. Artificial intelligence (AI) and machine learning-- decades-old technologies that are now electrifying the computing industry--for all intents and purposes, seem to be in the process of transforming corporate America. But why is AI so hot right now? Many experts believe it's because, after 50 years of promises that AI was going to solve critical problems, it's finally working. We now have applications where everyone who uses a mobile phone or search on the Internet realizes something has changed.