Prior to founding CognitiveScale, Matt was the leader of Watson Labs for IBM, and as such he is well versed on cognitive computing and the superconvergence of cloud computing, big data, and artificial intelligence, and how these technologies are disrupting every business process and industry. Q: What are the keys to a successful AI strategy? A: It's a more complex lifecycle than clients may think. We begin by mapping how the AI lifecycle looks and how it fits within the SDLC. We discuss what kind of problems you can solve and how to understand the complexity of the problems we are solving.

Andrej Karpathy has an article "Software 2.0" that makes the argument that Neural Networks (or Deep Learning) is a new kind of software. I do agree that there indeed a trend towards "teachable machines" as opposed to the more conventional programmable machines, however I do have an issue with some of the benefits that Karpathy mentions to back-up his thesis. Certainly Deep Learning is already eating the Machine Learning world with advances across the board. Karpathy mentions several well known ones: visual recognition, speech recognition, speech synthesis, machine translation, robotics and games. This frames his argument about the sea change in computing and perhaps its time to think about a new kind of software (I guess the kind that you teach like a dog instead of programming).

Jaron Lanier has written a book about virtual reality, a phrase he coined and a concept he did much to invent. It has the heady title Dawn of the New Everything. But it's also a tale of his growing up and when you read it, what you really want to talk to him about is parenting. Lanier is 57, but his childhood as he describes it was so sad and so creative and so extreme, it makes him almost seem fated to pursue alternative worlds. Lanier's parents met in New York.

Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolutional neural network to achieve close to state-of-the-art performance on a 51-class activity recognition problem (HMDB-51 dataset); this specific dataset has proved to be particularly challenging for deep neural networks due to the heterogeneity in camera viewpoints, video quality, etc. The resulting architecture is named pillar networks as each (very) deep neural network acts as a pillar for the hierarchical classifiers. In addition, we illustrate that hand-crafted features such as improved dense trajectories (iDT) and Multi-skip Feature Stacking (MIFS), as additional pillars, can further supplement the performance.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

True, everyone born before Aug. 4, 1900, has proved mortal (the world's oldest-known living person, a Japanese woman named Nabi Tajima, was born on that date). But the past is only an imperfect guide to the future, as the effervescent present is ceaselessly teaching us. But our children, our grandchildren -- or if not them, theirs -- may, conceivably, be the beneficiaries of the greatest revolution ever: the conquest of death. Immortality is an ancient dream. A Chinese king of the third century B.C. dispatched a sage, Xu Fu by name, on a quest for the elixir of life.

For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.

BDA Goes … continued • "… using Big and Smart Data as well as methods and tools based on semantic technologies will provide more transparency, enable precise and well-founded decisions and improve planning processes, which will result in more efficient and user-centric processes and systems …" • "Integrating things, data and semantic opens opportunities for knowledge discovery, and further makes it possible to provide advanced and intelligent services."

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

This webinar will demonstrate how to use the new Azure ML Workbench to solve complicated NLP tasks such as entity extraction from unstructured text. The tutorial aims to analyze a large corpus of unlabeled unstructured text records such as Medline PubMed abstracts and trains a word embedding model. The output embeddings are considered as automatically generated semantic features to train a neural entity extractor. We systematically show how to train a word embeddings model using word2vec neural word embedding algorithm with nearly 20 million Medline article abstracts on an HDInsight Spark cluster and then use the auto-generated features to train a LSTM deep recurrent neural network for medical entity extraction on a GPU-equipped Data Science Virtual Machine.