Leaders in AI applications will talk about their personal paths from being research-focused grad students to results-focused product leaders. They will share lessons learned from which parts of academia did (and did not) carry over to making AI work in the real-world, and provide guidance to people pursuing a similar path.

Judea Pearl is chancellor's professor of computer science and statistics at UCLA and co-author of "The Book of Why: The Science of Cause and Effect" with Dana Mackenzie, a mathematics writer. This column originally appeared in the Wall Street Journal. Computer programs have reached a bewildering point in their long and unsteady journey toward artificial intelligence. They outperform people at tasks we once felt to be uniquely human, such as playing poker or recognizing faces in a crowd. Meanwhile, self-driving cars using similar technology run into pedestrians and posts and we wonder whether they can ever be trustworthy.

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining.

Some of the trends analyzed during the 4th edition of Madrid.AI included the possibilities of eye-tracking systems, the possibilities and limitations of Artificial Intelligence and the applications of Deep Learning in the real world.

Technology's tentacles have encroached every aspect of our lives. Sitting in the comfort of your home you can tune in to live discussions and gain new understanding about technologies that are reshaping our world view. NDTV Tech Conclave 2018 saw a congregation of leading minds in the technology, mobile, and digital industries. The conclave aimed to showcase and create opportunities by bringing together many of the top entrepreneurs, investors, enterprise leaders, academics, and policymakers from around the world. The moderator of this session outlined two diametrically opposite views of AI and threw it open to the panelists.

A panel of industry experts gathered at RSA 2018 in San Francisco to explore the role that machine learning and artificial intelligence is playing in the current cyber landscape. After opening the discussion by asking the panel to each give their own definition of what machine learning is, Ira asked the speakers to define what types of applications are most appropriate for the use of machine learning and AI. Hillard: The places where it is most mature is around speech and image processing, and also around fraud detection. "The technology should be an enabler to solving a problem but sometimes it gets lost in what's being accomplished." Friedrichs: Most people have woken up to the fact that machine learning and AI are not the panacea that marketing tells us they are, but they can add to the feature set of a product.

This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning. Classical machine learning methods: These chapters discuss the classical machine learning methods such as matrix factorization, topic modeling, dimensionality reduction, clustering, classification, linear models, and evaluation. All these techniques treat text as a bag of words. Contextual learning methods that combine different types of text and also combine text with heterogeneous data types are covered.

The key to getting better at deep learning (or most fields in life) is practice. Each of these problem has it's own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.

A magazine, Moshe informed me, is a collection of miscellaneous pieces, with emphasis on "collection" and "miscellaneous." Thus, starting with this spring 2018 issue, we are accentuating the "magazine" in AI Magazine. Most issues of AI Magazine in the past have been special issues containing a series of technical articles on specific topics. While we will continue to have special issues from time to time, most issues going forward will contain expository articles on a variety of topics. This issue, for example, contains a letter from AAAI Fellow Edwina Rissland, two articles based on award-winning papers at AAAI 2017, two articles on deployed AI applications selected from IAAI 2017, one article based on an award-winning classic AAAI paper, two competition reports, an AI in Industry column, and a conference report, among several other items.