The same machine learning technology Google uses to anticipate the next word in a search query is in tests to advance predictive modeling in health care, and the tech giant is teaming up with three universities this year to research its ability to do so. Katherine Chou, head of product for Google's efforts in machine learning for health, has been working on using machine learning for preventive care since 2015, when health care systems reached out to Google for help. Part of the breakthrough now is that "our deep learning technology is finally mature enough to handle the unstructured data that you see in the records," she says. An example of unstructured data is pictures and notes from a physician or nurse that make up the majority of an electronic health record. Google's machine learning can now include those data in a predictive modeling algorithm.

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. MIT researchers have demonstrated an algorithm that has effectively learned how to predict sound: When shown a silent video clip of an object being hit, the algorithm can produce a sound for the hit that is realistic enough to fool human viewers. Researchers envision future versions of similar algorithms being used to automatically produce sound effects for movies and TV shows, as well as to help robots better understand objects' properties The team used techniques from the field of "deep learning," which involves teaching computers to sift through huge amounts of data to find patterns on their own. Deep learning approaches are especially useful because they free computer scientists from having to hand-design algorithms and supervise their progress.

These and many other insights are from the McKinsey Global Institute's study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics.

The answer probably lies in how we handle the almost limitless power of this still nascent technology. Though one thing is for sure - AI will have a profound impact on our jobs and future economic structures. AI has been thrown into the limelight in recent days thanks to the much publicized recent spat between Mark Zuckerberg and Elon Musk - two tech titans who hold differing views on the future of AI and its impact on humanity. Does AI hold only positive outcomes as Mr. Zuckerberg argues? Or is there a potential downside as Mr. Musk warns.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Local representation vs distributed representation l Local representation each concept is represented by one symbol e.g. Giraff 1, Panda 2, Lion 3, Tiger 4 no interfere, noise immunity, precise l Distributed representation each concept is represented by a set of symbol, and each symbol participates in representing many concepts Generalizable less accurate interfere Giraff Pand Lion Tiger Long neck four legs body hair paw pad 61. High dimensional vector vs low dimensional data l High dimensional vector Random two vectors are always almost orthogonal many concepts can be stored within one vector u w x y z, Same characteristics as local representation l Low dimensional vector Interfere each other Cannot keep precise memory Beneficial for generalization l Interference and generalization are strongly related 62. Two layer feedforward network memory augmented network [Vaswani 17] l Memory augmented network a V Softmax(Kq) K is a key matrix (i-th row corresponds to a key for i-th memory) V is a value matix.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

If there is one subset of machine learning that incites the greatest excitement--that seems most like the intelligence in artificial intelligence--it is deep learning. Deep learning frameworks, also known as deep neural networks, power complex pattern-recognition systems that provide everything from automated language translation to image identification. Deep learning holds enormous promise for analysing unstructured data. There are just three problems: it is hard to do, it requires large amounts of data, and it uses lots of processing power. Naturally, great minds are at work to overcome these challenges.

Data analytics are fast becoming the lifeblood of IT. Big data, machine learning, deep learning, data science -- the range of technologies and techniques for analyzing vast volumes of data is expanding at a rapid pace. To gain deep insights into customer behavior, systems performance, and new revenue opportunities, your data analytics strategy will benefit greatly from being on top of the latest data analytics trends. Here is a look at the data analytics technologies, techniques and strategies that are heating up and the once-hot data analytics trends that are beginning to cool. From business analysts to data scientists, everyone who works with data is being impacted by the data analytics revolution.

This contrasts with R and Python code which is executed by an interpreter. Theoretically, native machine code should be quicker than interpreted code. NumFOCUS (2017) gives a set of benchmarks that indicate the language has comparable performance with C for a number of functions such as matrix multiplication and sorting lists. So far we have focused on imperative languages.