Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how the gradient boosting algorithm works using several interactive visualizations. We take a 2-dimensional regression problem and investigate how a tree is able to reconstruct the function \( y f(\vx) f(x_1, x_2) \). Play with the tree depth, then look at the tree-building process from above!

The history of robots taking over human jobs is long and littered with doomsday predictions. Just last May, mega-manufacturer Foxconn reportedly replaced 60,000 workers with automated technology. Amazon's warehouses thrive on machines that can move packages. And as Martin Ford, author of the New York Times bestseller Rise of the Robots tells it, this tech will only replace more jobs in the coming decade -- even in traditional white-collar office roles. Ford keynoted Orange Silicon Valley's "A.I. and the Future of Corporate Finance" event, which welcomed a panel of experts to OSV's Spear Street space to discuss the role that artificial intelligence has to place in bookkeeping and auditing operations.

Hollywood gets A.I. all wrong. Silver screen depictions often feature a robot impersonating a human, a prospect that's about as likely, and about as abused in screenwriting, as discovering humanoid extraterrestrial life. That's why I found Lucas Carlson's new technothriller novel, Big Data, so refreshing. Carlson combines an engineer's view on the future of A.I. with a cast of characters that can't stop until the dark secret brooding at the heart of the tale is revealed. It's a perfect summer read that you'll burn through in a day or two, and will leave you with a disturbing vision of what tomorrow might look like.

Earlier this year, Thomson Reuters announced a partnership with Watson, IBM's artificial intelligence system. Thomson Reuters is committed to better serving its customers by providing cognitive solutions, and we are working on a number of initiatives in the legal and risk space. As part of our investment in artificial intelligence, Thomson Reuters is working with IBM Watson to accelerate the development of cognitive capabilities in our products. We have also recently established a Centre for Cognitive Computing which provides dedicated focus and resources to explore and develop in-house capabilities in the rapidly developing field of cognitive computing. I can't be too specific at the moment, but we are targeting to bring some capabilities into beta in Q4 2016, and launching a new product in the first half of 2017.

Based on the increasing number of ways that artificial intelligence (AI) is finding application in – and powering industries, IBM has announced the introduction of an AI Nanodegree program that incorporates expertise from IBM Watson and covers the basics of artificial intelligence. "As artificial intelligence (AI) begins to power more technology across industries, it's been truly exciting to see what our community of developers can create with Watson. Developers are inspiring us to advance the technology that is transforming society, and they are the reason why such a wide variety of businesses are bringing cognitive solutions to market," said Rob High, IBM Fellow, Vice President & Chief Technology Officer of IBM Watson. Speaking in Las Vegas at World of Watson, High disclosed that AI is becoming more ubiquitous in the technology people use every day and he stressed the need for developers to continue to sharpen their cognitive computing skills. "They are seeking ways to gain a competitive edge in a workforce that increasingly needs professionals who understand how to build AI solutions.

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Self-driving cars, intelligent drones, robots and more: Artificial Intelligence, or AI, is expected to be the biggest revolution in the digital space since the dawn of the world wide web. And maybe the last one, as cynics like to say: From Stanley Kubrick (2001: A Space Odyssey) to Stephen Hawking, many have warned about the dangers of AI surpassing us. From a marketing perspective, AI looks like a great, rather than a gloomy, possibility. The future is full of promising applications for it. Tracking tools have become more and more sophisticated. They make it possible to track all the actions of your website visitors, from the first visit on -- but for the marketer there lies a challenge in actually reading the phenomenal amount of data they gather.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

One of the trickiest things about working at Contagious is separating probability from hyperbole. We tend to cover the most optimistic aspects of the industry – where brands are enthusiastically embracing new technologies; where agencies are pursuing bold new strategies; and where startups are figuring out how to change the world. Amid all this energy, it can be hard to stay rational and not get caught up in the whirlwind. But sometimes a new trend comes along that feels like it might live up to the hype. 'Machine learning has created the biggest business opportunity in history,' said Pedro Domingos, The University of Washington professor of computer science and engineering, in a recent talk.

We @ProjectJunoAI are big fans of landscapes. That's why we've created a machine intelligence landscape focused entirely on Europe [1]. Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO.