SEOUL--Humanity has fallen to artificial intelligence in checkers, chess, and, last month, Go, the complex ancient Chinese board game. But some of the world's biggest nerds are confident that machines will meet their Waterloo on the pixelated battlefields of the computer strategy game StarCraft. A key reason: Unlike machines, humans are good at lying. StarCraft, created in 1998, is one of the world's most popular computer game franchises. It pits three races against one another: the humanlike Terrans, the slimy insectoid Zerg and a mystical race with psionic powers called the Protoss.

Artificial intelligence is being used to reduce poaching. Scientists have developed an AI system that uses – and learns from – information on where poaching is taking place to map out the most effective patrols for rangers seeking to protect wildlife. Thousands of animals are illegally killed every day for their skin, traditional medicines and trophy hunting. As a result, wild tiger populations have decreased 95% over the past 100 years, black rhinos have reduced by 98% since 1960, and more than 30,000 elephants are killed each year for their ivory. Human patrols are the most direct way to protect wildlife from poachers.

Deep Learning: Intelligence from Big Data Tue Sep 16, 2014 6:00 pm - 8:30 pm Stanford Graduate School of Business Knight Management Center – Cemex Auditorium 641 Knight Way, Stanford, CA A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer's ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence. Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intelligence as a Service, by providing APIs that democratize access to Deep Learning algorithms.

This seminar is part of the Oxford Martin School Hilary Term seminar series: Blurring the lines: the changing dynamics between man and machine Speakers: Dr Carl Frey, James Martin Fellow, Oxford Martin Programme on the Impacts of Future Technology Dr Michael Osborne, University Lecturer in Machine Learning, University of Oxford Will you one day lose your job to a robot, or even an algorithm? Dr Carl Frey and Dr Michael Osborne's recent working paper, 'The Future of Employment: How susceptible are jobs to computerisation?', found that nearly half of US jobs could be susceptible to computerisation over the next two decades. So as technology races ahead, will low-skilled workers need to retrain in order to remain part of the workforce?

Often they involve more than one. In December, Microsoft-owned Skype unveiled a demo version of a real-time translation service. As one caller speaks English or Spanish, the program renders it in the other language, in both spoken and written form. The U of T computer science department website hosts a version of a tool that many industry players are racing to perfect: upload a picture, and it generates a written caption. At a CIFAR talk in March, Ruslan Salakhutdinov, now a U of T professor, showed that the model is eerily accurate -- but not always.

Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

Big Data is a powerful technology for business, but that power is even more important in the world of science. While business collects data on customers and processes, science can create staggering amounts of information with a single experiment. Often, that data can take years to work through by conventional means. Big Data processing can turn years into months or weeks. One of the latest tools science has taken up to handle their Big Data needs is machine learning.

Today, inside the towering glass and steel Four Seasons Hotel in downtown Seoul, South Korea, Google will put the future of artificial intelligence to the test. At one o'clock in the afternoon local time, a digital Google creation will challenge one of the world's top players at the game of Go, the ancient Eastern pastime that's often compared to chess--though it's exponentially more complex. This Google machine is called AlphaGo, and to win, it must mimic not just the analytical skills of a human, but at least a bit of human intuition. Over the years, machines have topped the best humans at checkers, chess, Othello, Scrabble, Jeopardy!, and so many other contests of human intellect. But they haven't beat the very best at Go.

Editor's Note: This article was originally printed in the 2008 Scientific American Special Report on Robots. It is being published on the Web as part of ScientificAmerican.com's In recent years the mushrooming power, functionality and ubiquity of computers and the Internet have outstripped early forecasts about technology's rate of advancement and usefulness in everyday life. Alert pundits now foresee a world saturated with powerful computer chips, which will increasingly insinuate themselves into our gadgets, dwellings, apparel and even our bodies. Yet a closely related goal has remained stubbornly elusive. In stark contrast to the largely unanticipated explosion of computers into the mainstream, the entire endeavor of robotics has failed rather completely to live up to the predictions of the 1950s. In those days experts who were dazzled by the seemingly miraculous calculational ability of computers thought that if only the right software were written, computers could become the articial brains of sophisticated autonomous robots. Within a decade or two, they believed, such robots would be cleaning our oors, mowing our lawns and, in general, eliminating drudgery from our lives.

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets.