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Inside Microsoft's AI Comeback

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But while his peer scientists Yann LeCun and Geoffrey Hinton have signed on to Facebook and Google, respectively, Bengio, 53, has chosen to continue working from his small third-floor office on the hilltop campus of the University of Montreal. Shum, who is in charge of all of AI and research at Microsoft, has just finished a dress rehearsal for next week's Build developers conference, and he wants to show me demos. Shum has spent the past several years helping his boss, CEO Satya Nadella, make good on his promise to remake Microsoft around artificial intelligence. Bill Gates showed off a mapping technology in 1998, for example, but it never came to market; Google launched Maps in 2005.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

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A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


How Microsoft wants us all to get creative with Artificial Intelligence

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So by adding a few lines of code, developers can mix, match and customize AI functionalities to suit their needs, spanning functions such as translation, video deconstruction and search, gesture recognition and real-time captioning. Broadly speaking, therefore, Cognitive Services are plugin functionalities that developers can use to enable systems within their apps to hear, speak, understand and interpret human needs. For example, LUIS (Language Understanding Intelligent Service) helps developers to integrate language models to understand users using either prebuilt or customized models. While the Custom Vision Service makes it easy to create your own image recognition service.


Microsoft Infuses SQL Server With Artificial Intelligence

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SQL Server 2017, which will run on both Windows and Linux, is inching closer to release with a set of artificial intelligence capabilities that will change the way enterprises derive value from their business data, according to Microsoft. The Redmond, Wash., software giant on April 19 released SQL Server 2017 Community Technology Preview (CTP) 2.0. Joseph Sirosh, corporate vice president of the Microsoft Data Group, described the "production-quality" database software as "the first RDBMS [relational database management system] with built-in AI." Download links and instructions on installing the preview on Linux are available in this TechNet post from the SQL Server team at Microsoft. It's no secret to anyone keeping tabs on Microsoft lately that the company is betting big on AI, progressively baking its machine learning and cognitive computing technologies into a wide array of the company's cloud services, business software offerings and consumer products. "In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R)," stated Sirosh, in the April 19 announcement.


Competition in AI platform market to heat up in 2017

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Intel's Nervana platform is a $400 million investment in AI Back in November, Intel announced what it claims is a comprehensive AI platform for data center and compute applications called Nervana, with its focus aimed directly at taking on Nvidia's GPU solutions for enterprise users. The platform is the result of the chipmaker's acquisition of 48-person startup Nervana Systems back in August for $400 million that was led by former Qualcomm researcher Naveen Rao. Built using FPGA technology and designed for highly-optimized AI solutions, Intel claims Nervana will deliver up to a 100-fold reduction in the time it takes to train a deep learning model within the next three years. The company intends to integrate Nervana technology into Xeon and Xeon Phi processor lineups. During Q1, it will test the Nervana Engine chip, codenamed'Lake Crest,' and make it available to key customers later within the year.


What's Machine Learning? It's Expensive, Slow and Exclusive -- For Now

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AI and NLP are two acronyms many in the world of chatbots toss around glibly, sometimes without understanding themselves what these terms mean. There's a third acronym that's an essential component beneath these two: ML, which stands for machine learning. Machine learning is a lot easier to explain in one tweet than AI or NLP: It's the process by which an advanced software system trains itself from a massive set of examples, rather than being explicitly programmed with rigid algorithms devised by human coders. Over time, it gets better and better as it acquires more data to train on. An ML system is still programmed with standard one-and-zero logic, but it's programmed to modify its behavior to meet specified goals based on patterns it discovers in the sample data.


4 Trends In 2017 That Every Developer Needs To Understand - InformationWeek

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In traditional application programming, these programs run locally (e.g. As computing power grows in devices and services (along with growing broadband availability), companies are increasingly creating integrated hybrid systems. The amount of information available for big data calculations is increasing, and powerful cloud computing tools and machine learning algorithms will allow developers to take more nuanced and valuable actions with that data. I encourage my fellow developers to prepare for the opportunities that quantum computing, big data, and mixed reality might bring in 2017.


The End of Digital Tyranny: Why the Future of Computing Is Analog

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Most of us rarely think about it, but when we turn on our smartphones and PCs, we're giving ourselves over to machines that reduce every single task to a series of 1s and 0s. But according to Doug Burger, a researcher with Microsoft's Extreme Computing Group, this may be coming to an end. Burger thinks we could be entering a new era where we don't need digital accuracy. To hear him tell it, the age of really big data may well be an age of slightly less-accurate computing. We could drop the digital straightjacket and write software that's comfortable working on hardware that sometimes makes errors.


Now Anyone Can Tap the AI Behind Amazon's Recommendations

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Amazon helped show the world how machines can learn. As far back as the late '90s, the company's online retail site would track every book, CD, and movie you purchased. As time went on, it would develop a pretty good sense of what you liked, serving up product recommendations its code predicted would catch your eye. And in the years since, the field of so-called machine learning has evolved in enormous ways, with the likes of Google, Facebook, and Microsoft training enormous networks of machines to identify faces in photos, recognize the spoken word, and instantly translate conversations from one language to another. Now, as these tech giants advance the state of the art, there's a movement afoot to bring machine learning to the business world at large.