This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.

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Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

Yann LeCun is director of AI research at Facebook and Silver Professor at New York University, affiliated with the Courant Institute of Mathematical Sciences, the Center for Neural Science, and the Center for Data Science, for which he served as founding director until 2014. Over his career, Yann has held a wide range of positions, including a postdoc at the University of Toronto, head of the Image Processing Research department at AT&T Labs-Research, and a researcher at the NEC Research Institute, as well as the 2015–2016 annual visiting professor chair of computer science at Collège de France. His research interests include machine learning and artificial intelligence with applications to computer vision, natural language understanding, robotics, and computational neuroscience. Yann is best known for his work in deep learning and the invention of the convolutional network method, widely used for image, video, and speech recognition. He is the recipient of the 2014 IEEE Neural Network Pioneer Award and the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award.

Digital data and cloud storage follow Moore's law: the world's data doubles every two years, while the cost of storing that data declines at roughly the same rate. This abundance of data enables more features, and better machine learning models to be created. "In the world of intelligent applications, data will be king, and the services that can generate the highest-quality data will have an unfair advantage from their data flywheel -- more data leading to better models, leading to a better user experience, leading to more users, leading to more data," Somasegar says. For instance, Tesla has collected 780 million miles of driving data, and they're adding another million every 10 hours. This data is feed into Autopilot, their assisted driving program that uses ultrasonic sensors, radar, and cameras to steer, change lanes, and avoid collisions with little human interaction.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype--it's a production-grade enterprise technology. Francisco explores the theoretical underpinnings of semantic folding, which solves the representational problem and the semantic grounding problem--both well known by AI-researchers since the 1980s, and offers an introduction to the Retina Engine, an Apache Spark library for semantic processing of text. Along the way, Francisco demonstrates functional prototypes of semantic classification, semantic filtering, and semantic searching and explains the applications of semantic folding for the finance, media, automotive, legal, medical, and safety and security industries.

AI is the cornerstone of the next generation of technology applications and is already on its way to infiltrating every part of our daily lives. As such, training AI from a diverse set of perspectives on the world is vital if we hope to advance AI in a way that makes the world a better place. The ever-important task of fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or select data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is "taught" and make both teaching and using AI accessible to anyone in the world.

Welcome to the O'Reilly Artificial Intelligence live stream. During one of our scheduled live broadcasts (see schedule below), the live presentation will appear here automatically. Note: All sessions and keynotes from O'Reilly Artificial Intelligence 2016 in New York will be recorded (pending speaker consent) and available in Safari approximately 3 weeks after the conference ends.

Improving delivery of healthcare rests on the ability to predict individual disease with precision, accuracy, and speed. Healthcare's progress rests on two core capabilities: the first is identifying an individual's current and future health state; the second is determining the subsequent best course of action per individual. Both need to be done with precision and timeliness to enable real-time action and iteration with data. This demands the ability to combine, organize, normalize, interpret, and take action on highly complex structured and unstructured data at speed and scale. And yet, most organizations lack the skills and manpower to do so, leading to labor-intensive and costly processes.