KOLKATA, India -- Across a bustling intersection from one of the first dead-letters offices, a pale blue sign beseeches pedestrians to join the future. Several hundred feet separate old, colonial India from the promise of a new, technologically transformed country. An Uber sign urges traffic safety and a gentle nudge for the ride-sharing service, which is wildly popular here. In Kolkata, capital of the Indian state of West Bengal, there are traces of the future amid decaying buildings and staggering poverty. Samsung Electronics and Dell signs mark the entrance to a dilapidated Victorian structure in the heart of the city. Kolkata is India's untold story in tech.

This blog post is authored by Brandon Rohrer, Senior Data Scientist at Microsoft. The raw stuff of data science is a collection of numbers and names. Measurements, prices, dates, times, products, titles, actions--everything is fair game. You can use images, text, audio, video and other complex data too, as long as you have a way to reduce it to numbers and names. The mechanics of getting data can be quite complex. But this guide is focused on the data science, so I'll leave that topic for another time. Data science is the process of using names and numbers to answer a question.

SAN FRANCISCO--(BUSINESS WIRE)--Executives previously from IBM Watson, GE Digital, Infosys, and MicroStrategy announced today that they have joined forces with TPG Growth to launch Noodle Analytics, Inc. (Noodle.ai), the Enterprise Artificial Intelligence company. Enterprise AI represents a major step forward in merging human learning and machine learning, all fueled by big data. Enterprise AI solutions combine world-class expertise in human-centered design, business process engineering, and artificial intelligence technologies. Today's artificial intelligence technologies include machine learning, predictive data analytics, and data science. "Over the next three to five years, artificial intelligence technologies and big data will be the most significant competitive differentiators in business. We are excited to be a pioneer in Enterprise Artificial Intelligence, offering timely, valuable, and affordable solutions to clients. We have the right team, an optimized business model, and the right partners to create extraordinary value," says Stephen Pratt, CEO of Noodle.ai.

Former top executives from IBM Watson, GE Digital, Infosys and MicroStrategy announced last week that they have joined forces with TPG Growth to launch Noodle Analytics, Inc., the Enterprise Artificial Intelligence company. Enterprise AI represents a major step forward in merging human learning and machine learning, all fueled by big data. Enterprise AI solutions combine expertise in human-centered design, business process engineering and artificial intelligence technologies. Today's artificial intelligence technologies include machine learning, predictive data analytics and data science. "Over the next three to five years, artificial intelligence technologies and big data will be the most significant competitive differentiators in business. We are excited to be a pioneer in Enterprise Artificial Intelligence, offering timely, valuable, and affordable solutions to clients. We have the right team, an optimized business model, and the right partners to create extraordinary value," says Stephen Pratt, CEO of Noodle.ai.

Midway through the first of five recent matches between Lee Sedol, a top-ranked professional Go player, and AlphaGo, a computer program conceived by Google DeepMind, an odd thing happened: Lee's jaw dropped, hanging open for a nigh-cartoonish twenty seconds, and then he laughed. AlphaGo had just mounted an aggressive, and evidently unexpected, attack. The moment was reminiscent of a famous episode in Go history, when Honinbo Shusaku, a future legend of the game, squared off against Inoue Genan Inseki, an older and more experienced player, in 1846. The story goes that a spectator--a local doctor who knew little of Go--correctly guessed that the seventeen-year-old Shusaku was beating Inseki. Asked how he knew, the doctor responded that, after an earlier move, Inseki's ears had flushed red, a clear indication of surprise.

John Hennessy joined Stanford in 1977 right after receiving his Ph.D. from the State University of New York at Stony Brook. He soon became a leader of Reduced Instruction Set Computers. This research led to the founding of MIPS Computer Systems, which was later acquired for 320 million. There are still nearly a billion MIPS processors shipped annually, 30 years after the company was founded. Hennessy returned to Stanford to do foundational research in large-scale shared memory multiprocessors. In his spare time, he co-authored two textbooks on computer architecture, which have been continuously revised and are still popular 25 years later. This record led to numerous honors, including ACM Fellow, election to both the National Academy of Engineering and the National Academy of Sciences. Not resting on his research and teaching laurels, he quickly moved up the academic administrative ladder, going from the CS department chair to Engineering college dean to provost and finally to president in just seven years. He is Stanford's tenth president, its first from engineering, and he has governed it for an eighth of its existence. Since 2000, he doubled Stanford's endowment, including a record 6.2 billion for a single campaign. He used those funds to launch many initiatives--which often cross departmental lines--along with new buildings to house them. Undergraduate applications also doubled, for the first time making Stanford even more selective than Harvard.

We introduce a novel approach to Compressed Path Databases, space efficient oracles used to very quickly identify the first edge on a shortest path. Our algorithm achieves query running times on the 100 nanosecond scale, being significantly faster than state-of-the-art first-move oracles from the literature. Space consumption is competitive, due to a compression approach that rearranges rows and columns in a first-move matrix and then performs run length encoding (RLE) on the contents of the matrix. One variant of our implemented system was, by a convincing margin, the fastest entry in the 2014 Grid-Based Path Planning Competition. We give a first tractability analysis for the compression scheme used by our algorithm. We study the complexity of computing a database of minimum size for general directed and undirected graphs. We find that in both cases the problem is NP-complete. We also show that, for graphs which can be decomposed along articulation points, the problem can be decomposed into independent parts, with a corresponding reduction in its level of difficulty. In particular, this leads to simple and tractable algorithms with linear running time which yield optimal compression results for trees.

Dialog is a useful way for a robotic agent performing a task to communicate with a human collaborator, as it is a rich source of information for both the agent and the human. Such task-oriented dialog provides a medium for commanding, informing, teaching, and correcting a robot. Robotic agents engaging in dialog must be able to interpret a wide variety of sentences and supplement the dialog with information from its context, history, learned knowledge, and from non-linguistic interactions. We have identified a set of nine system-level requirements for such agents that help them support more effective, efficient, and general task-oriented dialog. This set is inspired by our research in Interactive Task Learning with a robotic agent named Rosie. This paper defines each requirement and gives examples of work we have done that illustrates them.

The semantic class (i.e., type) of an entity plays a vital role in many natural language processing tasks, such as question answering. However, most of existing type classification systems extensively rely on hand-crafted features. This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia. It consists of two parts. The mention model uses recurrent neural networks to recursively obtain the vector representation of an entity mention from the words it contains. The context model, on the other hand, employs multilayer perceptrons to obtain the hidden representation for contextual information of a mention. Representations obtained by the two parts are used together to predict the type distribution. Using automatically generated data, these two parts are jointly learned. Experimental studies illustrate that the proposed approach outperforms baseline methods. Moreover, when type information provided by our method is used in a question answering system, we observe a 14.7% relative improvement for the top-1 accuracy of answers.

In this paper, we formulate a sparse optimization framework for extractive document summarization. The proposed framework has a decomposable convex objective function. We derive an efficient ADMM algorithm to solve it. To encourage diversity in the summaries, we explicitly introduce an additional sentence dissimilarity term in the optimization framework. We achieve significant improvement over previous related work under similar data reconstruction framework. We then generalize our formulation to the case of compressive summarization and derive a block coordinate descent algorithm to optimize the objective function. Performance on DUC 2006 and DUC 2007 datasets shows that our compressive summarization results are competitive against the state-of-the-art results while maintaining reasonable readability.