A machine-learning technique that has already given computers an eerie ability to recognize speech and categorize images is now creeping into industries ranging from computer security to stock trading. If the technique works in those areas, it could create new opportunities but also displace some workers. Deep learning, as the technique is known, involves applying layers of calculations to data, such as sound or images, to recognize key features and similarities. It offers a powerful way for machines to recognize similarities that would normally be abstruse to a computer: the same face seen from different angles, for instance, or a word spoken in different accents (see "10 Breakthrough Technologies 2013: Deep Learning"). The mathematical principles that underlie deep learning are relatively simple, but when combined with huge quantities of training data and computer systems capable of powerful parallel computations, the technique has resulted in dramatic progress in recent years, especially in voice and image recognition.

Machine Learning sounds like science fiction, but it's already present in our daily lives. From Siri's voice recognition to Tesla's self-driving cars, Machine Learning is behind many of today's innovations. If you're looking to take your programming skills to the next level, the Analytics, Machine Learning & NLP in Python Course can help you add Machine Learning to your repertoire, and it's on sale for just 45. Taught by a team of seasoned SIlicon Valley experts, this course will train you in Machine Learning's essential tools like NLP and Python. You'll learn in a fun and exciting manner as animations break down complex programming topics for you.

Great, you've just identified some of the characteristics of a good generalization: a relatively simple, abstract, less detailed, model that is consistent with (or fits, or explains) the observations, and is more broadly applicable beyond the cases you have seen. That's how humans think, but how does this help us design a computer algorithm that generalizes well? This helps us in a couple of ways. Firstly, it gives us a framework for designing an ML algorithm. Just like humans build mental models based on their observations, an ML algorithm should ingest training data and output a model that fits, or explains the training data well. A model is the mathematical analog to the human idea of a "concept" or "mental model"; it's the mathematical formalization of what we have been informally calling "rules" until now. A model is essentially a function takes as input the characteristics (i.e.

If you're a virtual reality enthusiast, you probably read Kevin Kelly's April Wired cover story on Magic Leap, "The World's Most Secretive Startup." Kelly is one of the few people who've seen the much-hyped mixed reality technology being produced by the Fort Lauderdale company and was suitably impressed by it. "While Magic Leap has yet to achieve the immersion of The Void," he wrote (referring to the Utah-based immersive experience company), "it is still, by far, the most impressive on the visual front -- the best at creating the illusion that virtual objects truly exist." As the co-founder of Wired, publisher of the Cool Tools website, and former publisher and editor of The Whole Earth Review, Kelly has always been prescient about these things. In his new book, The Inevitable: Understanding 12 Technological Forces That Will Shape Our Future, he compellingly outlines a set of behaviors and trends that will change the way we live. We recently spoke with Kelly about the themes of the book, and of course, the latest developments in VR. There Is Only R: The first question I have to ask: what do you think of the Pokemon Go phenomenon? Given what you've written about the VR and AR, what's your take on it? Kevin Kelly: I think it's just wonderfully thrilling to see -- I think perhaps the only unexpected thing about it is its apparent suddenness.

The gift of 1.75 million will create the Scotiabank Disruptive Technologies Venture at the Rotman School of Management and support programs and initiatives including: "Successful businesses in the digital economy are creating new products and services, and reimagining how existing ones are delivered. This requires a new generation of talent that has the knowledge to understand the possibilities of a digital world and the skills to seize them," says Tiff Macklem, Dean, Rotman School of Management. "This investment by Scotiabank will help us scale up our very successful experiential learning programs in technology-based entrepreneurship and design thinking." "Consumers expect the same frictionless experience whether they're paying for a coffee, booking a hotel room, ordering a taxi or signing up for a credit card," says Brian Porter, President and Chief Executive Officer, Scotiabank. "As we work to deliver the best experience to our customers, we recognize the tremendous value of working closely with post-secondary institutions and their students. It also provides students hands-on experience to prepare them for job opportunities in the digital world. Scotiabank's partnership with Rotman is an important piece of our digital strategy, and we are optimistic that it will produce great results."

Bots will one day sweet-talk their way into our good graces, but that day is not today. Judging from recent debacles such as Microsoft's Tay and the fembots of Ashley Madison, AI-assisted chatbots still have a long way to go before gaining genuine and socially acceptable conversational skills. Just visit YouTube and you'll realize that giving bots an eerily human-like appearance is much easier than granting them the gift of gab. That means you'll have to wait just a bit longer before a real-life version of either Jarvis (The Avengers) or Samantha (Hers) will arrive to stir your intellect or your emotions. Nonetheless, chatbots have certainly made huge strides since they were first used decades ago.

The home feed, a collection of Pins from the people, boards and interests followed, as well as recommendations including Picked for You, is the most heavily user-engaged part of the service, and contributes a large fraction of total repins. The more people Pin, the better Pinterest can get for each person, which puts us in a unique position to serve up inspiration as a discovery engine on an ongoing basis. The home feed is a key way to discover new content, which is valuable to the Pinner, but poses a challenging question. Given the ever increasing number of Pins from various sources, how can we surface the most personalized and relevant Pins? Pinnability is the collective name of the machine learning models we developed to help Pinners find the best content in their home feed.

With 100 million users active on its platform every month, Pinterest is increasingly relying on machine learning to help guide the company to new online discoveries. People come to Pinterest to explore, save, and share images and posts from around the internet. Finding content they like naturally keeps them engrossed in the platform: The company says 30% of engagement and 25% of in-Pinterest purchases are driven by the platform's recommendations of related content. To get those recommendations right, the company relies on cutting-edge, data-driven techniques and lots of experimentation. "A lot of what I"m doing here is trying to shape what direction we go in approaching the discovery problem," says Pinterest's lead discovery science engineer Mohammad Shahangian.

Think back to when you were a child. You were in a data-rich environment, but only starting to make sense of it all. You barely knew the names for things, much less how one thing related to another. By this point, most of your experiences fit into webs of different but familiar contexts. You understand your co-workers' roles, their strengths and weaknesses, where they come from, what their families are like, who they work best with.

Shannon Walker, chief operating officer of the chief data office at Deutsche Bank, described how the bank is building a data science platform mainly focused on organizational complexity and making sense of what functions are consuming data, in what locations, and for what purposes. Meanwhile, BNP Paribas is using machine learning "in a systematic and statistical way.... digging up a trading system's...