An estimated 1 million words (War & Peace twice over!) have been written speculating the much-anticipated outcome. Won't these new AI headquarters spawn the next 10 Amazons? To find these cities, don't just follow the city infrastructure, tax breaks, big companies, or nice weather. Go where the 50,000 AI engineers/scientists are today and where they are going tomorrow -- especially the 10,000 specialized researchers. Recruiting a top AI researcher is like recruiting an NFL quarterback: 8-figure, multi-year contracts, the whole nine yards.

Over the past 20 years, the demand for imaging services has been increasing, outpacing the supply of qualified physicians. It has stretched providers and radiologists to constantly produce more output, without compromising patient care. Not easily fixed and this demand will accelerate. With two billion people expected to join the global middle class in the next decade a crisis is imminent. This colossal challenge can only be addressed by adopting new technology that can enhance the capabilities of radiologists. Step forward, Zebra Medical Vision, a deep learning imaging analytics company that wants to deal with this avalanche of demand by providing products that can meet this surge.

Editor's Note: The Falling Walls Conference is an annual, global gathering of forward thinking individuals from 80 countries organized by the Falling Walls Foundation. Each year, on November 9--the anniversary of the fall of the Berlin Wall--20 of the world's leading scientists are invited to Berlin to present their current breakthrough research. The aim of the conference is to address two questions: Which will be the next walls to fall? And how will that change our lives? The author of the following essay is speaking at this year's Falling Walls gathering.

This article is featured in the new DZone Guide to Artificial Intelligence. Get your free copy for more insightful articles, industry statistics, and more! As developers, we are used to thinking in terms of commands or functions. A program is composed of tasks, and each task is defined using some programming constructs. Neural networks differ from this programming approach in the sense that they add the notion of automatic task improvement, or the capability to learn and improve similarly to the way the brain does.

Machine learning, as many readers will know, is a branch of artificial intelligence. It's essentially a method of programming which gives computers the ability to learn something on their own. So, then, after it's learned to fish, if you ask the computer a question, or ask it for a fish, its answer, or the fish, doesn't have to be in its memory banks – it can go and catch a new fish, or just make one up based on the information about fish it has already acquired. This is like the example of Google's DeepMind algorithm creating entirely new images of cats, people and other images based on the millions of images of cats, people and whatnot it had already seen. The new images the algorithm was creating were not exact replicas of any it had seen, and quite frankly they were terrible, but you get the idea.

Last month we reported on the results of 18th annual KDnuggets Software Poll: New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Here is a more detailed look at which tools go well with each other, and which don't. We also find an emerging Python-friendly ecosystem of tools that are commonly used with the two leading edges of data science: Big Data (Spark/Hadoop) and Deep Learning. A link to anonymized dataset is at the end of this post - analyze the data yourself and publish or send me the results.

Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome‐scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016). One powerful application of microscopy‐based assays involves assessment of changes in the subcellular localization or abundance of fluorescently labeled proteins in response to various genetic lesions or environmental insults (Laufer et al, 2013; Ljosa et al, 2013; Chong et al, 2015). Proteins localize to regions of the cell where they are required to carry out specific functions, and a change in protein localization following a genetic or environmental perturbation often reflects a critical role of the protein in a biological response of interest. High‐throughput (HTP) microscopy enables analysis of proteome‐wide changes in protein localization in different conditions, providing data with the spatiotemporal resolution that is needed to understand the dynamics of biological systems. The budding yeast, Saccharomyces cerevisiae, remains a premiere model system for the development of experimental and computational pipelines for HTP phenotypic analysis.

So where did AI come from? The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning. Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

Dean's Welcome, Ed Schlesinger, Johns Hopkins Whiting School of Engineering 9:05 a.m. Vice Provost's Welcome, Denis Wirtz, Johns Hopkins University 9:10 a.m. Director's Inaugural Address and Welcome, René Vidal, Johns Hopkins Mathematical Institute for Data Science Conditional Mean Embeddings for Reinforcement Learning, John Shawe-Taylor, University College London 11:40 a.m. Designing and Learning Representations for Visual Data in the Age of Deep Learning, Stefano Soatto, Amazon Web Services and University of California, Los Angeles 12:20 p.m. Alexa, Tell Me How Kaldi and Deep Learning Revolutionized Automatic Speech Recognition!, Sanjeev Khudanpur, Johns Hopkins Center for Language and Speech Processing Is Manifold Learning for Toy Data Only?, Marina Meila, University of Washington 5:20 p.m. Theoretical and Numerical Challenges in Medical Image Analysis and Computational Anatomy, Nicolas Charon, Johns Hopkins Center for Imaging Science