Machine learning is being used by companies such as Netflix, Facebook, and Spotify for automated data analysis. The results are then used to create recommendations based on past consumption habits. This approach is based on algorithms that learn and adapt to the usage data and patterns that emerge, not the hard-coded rules used in traditional analytics. Netflix in particular has successfully leveraged analytics for years, and their strategy serves as an example of how machine learning can help you gain a competitive advantage. Big Data analytics, being based on manual processes to search for patterns in data, has a major flaw--humans.

NEWS ANALYSIS: The sadly embarrassing end to Microsoft's public experiment with a chatbot that would mirror millennial attitudes provides some critical lessons about designing machine learning systems.

Machine learning is not new. SAS has been doing it for over 20 years and some early machine learning papers date back to the 50's. So why is it one of the hottest topics at the Strata Hadoop World conference later this week? Clearly, Hadoop is playing a major role in the increased focus on machine learning. Powerful, low-cost distributive computing environments coupled with Hadoop give data scientists the ability to run iterative models (like neural networks) they may not have been able to in the past.

Tech giant IBM has developed a new technology that can speed up the training for deep neural networks (DNNs). Though DNNs can be taught to perform almost any task, training them is time consuming and complex. Training artificial intelligence (AI) systems involves the usage of supercomputers or data centres for a significant number of days. In a research paper titled'Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices', authors Tayfun Gokmen and Yurii Vlasov said:"In recent years, DNNs have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. "Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days," But the scientists at IBM's T.J. Watson Research Center have come out with "resistive processing units," that can reduce the horsepower and learning time sharply. "We proposed a concept of resistive processing unit (RPU) devices that can simultaneously store and process data locally and in parallel, thus potentially providing significant acceleration for DNN training.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

The platform will be the integral part of Image Search Engine for Image Referral Network and Image Ad Network, to automate generation and placement of highly-relevant targeted ads based on images in a large scale for the first time in the industry. ZAC's AI Discovery platform can also be used for other types of images, data, or objects, e.g., clothing, purse, accessories, medical images, satellite images, and biometrics. ZAC has an impressive team of scientists and developers. The software development is headed by Saied Tadayon, a veteran software developer and scientist, who got PhD from Cornell at age 23. One of ZAC's inventors is Prof. Lotfi A. Zadeh ("The Father of Fuzzy Logic"), a pioneer computer scientist at U.C. Berkeley.

While CIOs are in the midst of transforming their companies into digital enterprises, experts have begun to sound a new alarm bell: The next wave of disruption has already started. Rick Davidson, founder and CEO at Cimphoni, a consultancy focused on the intersection of business and IT, is one such expert. He calls the next wave "the cognitive enterprise," where technology systems that can understand, learn and even reason will be able to meet customer expectations before customers know they have an expectation that needs to be met. He pointed to technologies such as Apple's Siri, Microsoft's Cortana and Amazon Echo -- the first generation of virtual concierges -- as examples. "We're going to move through the digital enterprise pretty fast," Davidson said during his presentation on the cognitive enterprise at the recent Fusion CEO-CIO Symposium in Madison, Wis.

McCann has appointed an articificial intelligence creative director AI-CD?, who will be attending McCann Worldgroup's new employee welcoming ceremony on April 1, along with 11 new college graduates. AI-CD? was actually created by the agency under its'Creative Genome Project', the first in a series of projects undertaken by the agency's'McCann Millenials taskforce'. The artificial intelligence can give creative direction on commercials because the data that forms the basis of the algorithm includes deconstructed, tagged and analysed TV shows, as well as data on the winners of the All Japan Radio & Television Commersion Confederation's CM Festival. President & CEO of McCann Japan, Yasuyuki Katagi said: "Artificial intelligence is already being used to create a wide variety of entertainment, including music, movies, and TV drama, so we're very enthusiastic about the potential of AI-CD ß for the future of ad creation. The whole company is 100 percent on board to support the development of our A.I. employee."

IBM is about to deliver the foundation of a brain-inspired supercomputer to Lawrence Livermore National Laboratory, one of the federal government's top research institutions. The delivery is one small "blade" within a server rack with 16 chips, dubbed TrueNorth, and is modeled after the way the human brain functions. Silicon Valley is awash in optimism about artificial intelligence, largely based on the progress that deep learning neural networks are making in solving big problems. Companies from Google to Nvidia are hoping they'll provide the AI smarts for self-driving cars and other tough problems. It is within this environment that IBM has been pursuing solutions in brain-inspired supercomputers.

We live in an era of accelerating change, when scientific and technological advancements are arriving rapidly. As a result, we are developing a new language to describe our civilization as it evolves. Here are 20 terms and concepts that you'll need to navigate our future. Back in 2007 I put together a list of terms every self-respecting futurist should be familiar with. I reached out to several futurists, asking them which terms or phrases have emerged or gained relevance since that time. These forward-looking thinkers provided me with some fascinating and provocative suggestions -- some familiar to me, others completely new, and some a refinement of earlier conceptions.