ARTIFICIAL INTELLIGENCE: Exclusive: Why Microsoft Is Betting Its Future on AI Casey Newton The Verge "Microsoft's historical instincts about where technology is going have been spot-on. But the company has a record of dropping the ball when it comes to acting on that instinct...The question looming over the company's efforts around AI is simple: Why should it it be different this time?" THE FUTURE: Why We Need to Pick Up Alvin Toffler's Torch Farhad Manjoo The New York Times "But even though these and bigger changes are just getting started -- here come artificial intelligence, gene editing, drones, better virtual reality and a battery-powered transportation system -- futurism has fallen out of favor. Even as the pace of technology keeps increasing, we haven't developed many good ways, as a society, to think about long-term change." Spiders, Slime, and Fungus Meg Miller Fast Company "A jacket grown from microbes. A chair made of the fungus mycelium. Perfume concocted from'designer' baker's yeast. As one-offs, these projects may seem far flung and wildly experimental, but together they point to a new movement within the design world...a field that marries the scientific know-how of biologists with the big-picture thinking of artists and designers."

The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent networks and purpose and structure of a prominent variation, LSTMs. Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. They are arguably the most powerful type of neural network, applicable even to images, which can be decomposed into a series of patches and treated as a sequence. Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain.1 To understand recurrent nets, first you have to understand the basics of feedforward nets. Both of these networks are named after the way they channel information through a series of mathematical operations performed at the nodes of the network.

In July 2016 was the first case where Police officers in Dallas, United States, used a robot to kill an armed suspect during a Black Lives Matter protest. The device was not autonomous, but in the future it could be. And although there are many cases of remote warfare within militaries, such as the case with drones, this was the first occasion where such technology was used in public. There are real concerns around artificial intelligence causing chaos like the scenarios depicted in Hollywood movies such as Terminator, Robocop, Iron Man and iRobot in the future.

Remember last summer's influx of convolutional neural network art, which took the form of hallucinogenic-like DeepDream images, like the one above? Prompted by a blog post and code release by a team of Google engineers, haunting composite generation--also known as inceptionism, as a nod to the movie-related internet meme "we need to go deeper"--became the poster child for artificial neural networks. In "A Neural Algorithm of Artistic Style" Leon Gatys, Alexander Ecker and Matthias Bethge describe it as a system that "uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Imagine your vacation photos rendered in the style of Pablo Picasso, or Leonardo da Vinci's Mona Lisa painted in the style of Vincent Van Gogh's Starry Night. You can see that example directly, in "Machines and Metaphors," a blog post by artist and programmer Gene Kogan.

Personalizing the News Feed: A Large-Scale Recommendation Problem Personalization is a key component in ensuring user satisfaction, and at Yahoo, personalization is at the heart of several user-facing products. This talk will focus on how Yahoo built one of the largest news recommendation engines in the world: the Yahoo stream, which personalizes the news feed for several hundreds of millions of users on millions of content items. Beyond the scale, the success of the news feed also depends on whether it is able to engage the user long term. In this session, Yahoo's director of research will present the challenges and issues in designing an engaging stream, and attendees will also learn how to cope with sparsity of explicit feedback, how user behavior changes with context of the device, how to build machine learned models for each user, and the metric that allows Yahoo to optimize for long term user-engagement.

Could Artificial Intelligence be the key to brewing the perfect beer? In a world first, IntelligentX is creating beer using a combination of data science and Artificial Intelligence. So far it's created Golden, Amber, Pale and Black variants -- and it is using some cutting-edge technology to do so. "We have created a beer which uses AI to improve itself from customer feedback," Hew Leith, IntelligentX's CEO, told Digital Trends. "In a traditional brewery, the brewer makes decisions on what to brew based on trends, their own intuition, or anecdotal feedback from people who tried previous batches. They may or may not take onboard this feedback for the next batch. With our beer we use machine learning to give the brewer superhuman skills, enabling them to test and receive customer feedback more quickly than ever before. This means we can respond to consumers' changing tastes faster than traditional brewers."

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As part of my Data Science class with General Assembly, we each gave a presentation about a real-world application of data science. My talk was about using machine learning to detect fraud on Skype, and was based upon an excellent paper by Microsoft Research published in November 2013. Although Skype already had measures in place to detect fraud (e.g., credit card fraud, spam instant messages), the research team's goal was to improve the detection of "stealthy fraudulent users" that evade Skype's defenses for a prolonged period. They built a machine learning classifier that flagged potentially fraudulent users, and was able to detect 68% of these users with a false positive rate of 5%. The novelty in their approach was the fusing of disparate data types (profile information, Skype product usage, and Skype social activity) into a single classifier.

When in 2012 a computer learned to recognize cats in YouTube videos and just last month another correctly captioned a photo of "a group of young people playing a game of Frisbee," artificial intelligence researchers hailed yet more triumphs in "deep learning," the wildly successful set of algorithms loosely modeled on the way brains grow sensitive to features of the real world simply through exposure. Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition -- core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work. Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.