Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied See the License for the specific language governing permissions and limitations under the License. This repo uses an approach based on creating many small patches of the original input images and then performing machine learning tasks on the image patches. The combination of Python and SAS files enables you to conduct different learning tasks in different orders. Use threaded_tile.py to create many small uniform patches of the input images.

What do you think of when someone says "AI" or "Artificial Intelligence"? For most of us, it conjures up an image of the future. It doesn't much evoke the here and now. Artificial intelligence is already out of the box. And while it might not be as slick as the movies, it has vast applications in almost every field, from business to medicine, traffic jams to Facebook photos. Most of us use or benefit from artificial intelligence every day.

At the heart of a classification model is the ability to assign a class to an object based on its description or features. When we build a classification model, often we have to prove that the model we built is significantly better than random guessing. How do we know if our machine learning model performs better than a classifier built by assigning labels or classes arbitrarily (through random guess, weighted guess etc.)? I will call the latter non-machine learning classifiers as these do not learn from the data. A machine learning classifier should be smarter and should not be making just lucky guesses!

MIT researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs. The researchers tested their system on a set of programming errors, culled from real open-source applications, that had been compiled to evaluate automatic bug-repair systems. Where those earlier systems were able to repair one or two of the bugs, the MIT system repaired between 15 and 18, depending on whether it settled on the first solution it found or was allowed to run longer. While an automatic bug-repair tool would be useful in its own right, professor of electrical engineering and computer science Martin Rinard, whose group developed the new system, believes that the work could have broader ramifications. "One of the most intriguing aspects of this research is that we've found that there are indeed universal properties of correct code that you can learn from one set of applications and apply to another set of applications," Rinard says.

Global technology leaders and senior executives from around the world spoke on a range of topics, including Digital India, Smart Cities, Make in India, Skill India and cutting-edge technologies like artificial intelligence, machine learning, 3D printing, drones, robotics, robotic surgeries and genomics, at the two-day EmTech India 2016 event, held in New Delhi on 18 and 19 March. The event was organized by Mint and MIT Technology Review, published by the Massachusetts Institute of Technology (MIT). The speakers included R.S. Sharma, chairman of the Telecom Regulatory Authority of India; John Chambers, executive chairman of Cisco Systems Inc. and chairman of the US-India Business Council; Una-May O'Reilly, principal research scientist, AnyScale Learning For All Group, MIT Computer Science and Artificial Intelligence Laboratory; and Harsh Mariwala, chairman of Marico Ltd. The full list can be accessed here. Here are edited excerpts from their speeches and discussions that followed. John Chambers, executive chairman of Cisco Systems Inc and Chairman of US-India Business Council (USIBC), reiterated the reason for his bullishness on India in a chat with Mint's R. Sukumar, on the first day of EmTech India 2016. When most of us here read the India narrative, it is not uniformly positive. Yet, you are amazingly bullish on the country. What do you see that others don't? Sometimes when you see what is happening in other countries and other businesses around the world from the outside, you are able to gather data very quickly, and then you can connect the dots on the market transitions. I am very bullish on the country for that very simple reason--follow and connect the dots on transitions. The transition to digitization will be the biggest technology change ever. I don't go into a country unless the leader, he or she, really understands this. Second, I don't go to a country that does not have sustainable differentiation capabilities.

This is the first chapter of a five-part series about machine learning. Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. Despite the popularity of the subject, machine learning's true purpose and details are not well understood, except by very technical folks and/or data scientists. This series is intended to be a comprehensive, in-depth, and non-technical guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques of the field.

Thomas H. Davenport and Julia Kirby, authors of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, offer context for understanding cognitive technology offerings -- and what it will mean for business. So-called "smart" technologies are everywhere, and the level of intelligence in smart machines is increasing over time. Analytics technology is evolving toward cognitive systems, capable of making basic decisions and performing rudimentary and repetitive tasks in data management. In January 2016, MIT Sloan Management Review hosted a discussion on how to understand the bewildering array of cognitive technology offerings. The webinar presenters were Thomas H. Davenport, President's Distinguished Professor of Information Technology and Management at Babson College and a Fellow of the MIT Center for Digital Business, and Julia Kirby, a Boston-based editor and writer.

In the airy, loft-like Microsoft Research lab in New York City, five computer scientists are spending their days trying to get a Minecraft character to climb a hill. That may seem like a pretty simple job for some of the brightest minds in the field, until you consider this: The team is trying to train an artificial intelligence agent to learn how to do things like climb to the highest point in the virtual world, using the same types of resources a human has when she learns a new task. That means that the agent starts out knowing nothing at all about its environment or even what it is supposed to accomplish. It needs to understand its surroundings and figure out what's important – going uphill – and what isn't, such as whether it's light or dark. It needs to endure a lot of trial and error, including regularly falling into rivers and lava pits.

Google's AlphaGo program recently defeated the world's second-ranked player of Go -- a vastly more complex game than chess -- marking an important milestone in the development of artificial intelligence (AI). But equally important was Google's revelation that one of its robots has developed the ability to pick up objects in ways that had previously only been identified in cognitive life forms. Both advances were brought about by the development of computational models based on the human central nervous system, which is particularly well suited to certain aspects of AI, such as pattern recognition and machine and adaptive learning. Research in this area will have significant implications for geopolitics in the future. Automation and AI are already being applied in nearly every economic sector.

Throughout that history, technology has brought comfort, ease, and prosperity. And all along it has taken jobs, disrupted lives, and changed the way people live and think. Each new age of innovation has brought a revolution. First, there was steam, then mass production, and late last century information technology. Now, researchers and thought leaders have declared a Fourth Revolution: an age of Artificial Intelligence (AI), advanced automation, and sophisticated robotics.