Facebook has almost 2B active users. At a developer conference today, the company announced plans to connect even more, and Mark Zuckerberg hinted at a near-future scenario where AI-controlled bots will answer basic questions via the Messenger app. Why is that such a big announcement? Because 2016 is the year of the chatbots, a turning point when the AI that drives these machines can imitate human speech, respond to queries, help you find the right product online, and troubleshoot problems. In fact, while there is no way to prove it, there's a good chance you have already chatted online with a customer service bot--say, at one of those pop-ups you see at an online store or when you visit a big-name brand site.

You don't have to be a Go champion to have artificial intelligence change your game. You get in your car, and your Apple iPhone tells you what traffic looks like where you're going--before you ask. We're all on the road with Tesla's self-driving cars, which are redefining what driving means. The artificial intelligence calendar assistant Amy emails three of your friends to figure out a meeting time that works for everyone--and nails it. Thankfully, chatting with Amazon's Alexa is a lot more entertaining than, say, would be Hal, the fictional artificial intelligence from the film 2001: A Space Odyssey.

We asked developers at Facebook's F8 Developer's Conference what they think of the new chatbot features in Messenger. A link has been sent to your friend's email address. We asked developers at Facebook's F8 Developer's Conference what they think of the new chatbot features in Messenger.

Classy started as a code challenge for a technical interview for a machine learning job in Tokyo. The challenge was to create an an application that uses a supervised learning algorithm to classify images based on their text content. This application was then wrapped in a server with a tiny REST API, and Classy was born. I can certainly make that happen! Let me know a little more about what you're working on using the contact form below and we'll get the ball rolling.

WIRE)--How do our brains learn and understand the world? That question is of paramount importance to both neuroscientists and technologists who want to build intelligent machines. It has been understood for over a hundred years that the inputs and outputs of the brain are constantly changing sequences of patterns and therefore learning and recalling sequences must be a fundamental operation of neurons. Numerous proposals have been made for how neural networks might learn sequences. However, these proposals did not match the anatomy and function observed in the brain.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than 50k per year. How can tree methods help us?

Numenta's theory of how the brain learns and understands sequences of patterns may be an essential component for creating intelligent machines REDWOOD CITY, CA –April 12, 2016-- How do our brains learn and understand the world? That question is of paramount importance to both neuroscientists and technologists who want to build intelligent machines. It has been understood for over a hundred years that the inputs and outputs of the brain are constantly changing sequences of patterns and therefore learning and recalling sequences must be a fundamental operation of neurons. Numerous proposals have been made for how neural networks might learn sequences. However, these proposals did not match the anatomy and function observed in the brain.

During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.

IBM's Watson is perhaps most famous for beating its human competition in Jeopardy, but this week, IBM continued its ad campaign highlighting how the supercomputer uses data to solve business problems. In 15 spots from Ogilvy & Mather that launched during the Masters golf tournament this weekend, IBM used celebrities, thought leaders and brands to show the power of Watson and the IBM Cloud. Director Ridley Scott and author Stephen King follow in the footsteps of Bob Dylan, who appeared in Watson spots in October discussing the data behind music. Scott, who also appeared in Watson ads in February, discusses artificial intelligence in the new ads. Watson tells the director that the way he portrays AI in his movies, which include Alien and Blade Runner, is "not flattering," and that in the real world, Watson helps humans "reduce waste, detect fraud and fight pollution." "Whether you're a writer, like Stephen King or any business person, the need to connect with your audience is really important, and the ads show that Watson can help you do that," Ann Rubin, vp of branded content and global creative at IBM, told Adweek.

In Southern California, near TFS U.S. headquarters, TIMS will support the development of telematics car insurance services for Toyota customers, as well as new experiences aimed at more fully satisfying customers by working in unison with dealers and distributors. TIMS will contribute to the development of insurance offerings that benefit consumers-including so-called'pay how you drive' insurance, which encourages safer driving. TIMS plans to conduct analysis of big data, and conduct relevant marketing and promotion of the new services to help offer broader insurance options to users. The aim of establishing the new company is to contribute-from the aspect of automobile insurance-to the achievement of Toyota's vision of a mobile society that is safe, secure and convenient. AD and TFS will invest in the new company through their own subsidiaries in the U.S., while investment on the part of TMC will come from Toyota Connected, Inc. (TC) established in the U.S. in January 2016.