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.

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.

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.

Alan Turing, Albert Einstein, Stephen Hawking, John Nash--these "beautiful" minds never fail to enchant the public, but they also remain somewhat elusive. How do some people progress from being able to perform basic arithmetic to grasping advanced mathematical concepts and thinking at levels of abstraction that baffle the rest of the population? Neuroscience has now begun to pin down whether the brain of a math wiz somehow takes conceptual thinking to another level. Specifically, scientists have long debated whether the basis of high-level mathematical thought is tied to the brain's language-processing centers--that thinking at such a level of abstraction requires linguistic representation and an understanding of syntax--or to independent regions associated with number and spatial reasoning. In a study published this week in Proceedings of the National Academy of Sciences, a pair of researchers at the INSERM–CEA Cognitive Neuroimaging Unit in France reported that the brain areas involved in math are different from those engaged in equally complex nonmathematical thinking.

The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles. Each workshop is 2 day long and comprises 8 modules of 3 blocks of 30' each--including time for questions. Blocks are either Theory or Exercise, with at least one Exercise per module. The goal is to make you operational with machine learning at the end of the workshop.

One of the arguments for self-driving cars is that they will be safer than human-driven vehicles.Human error is the cause of 94% of car crashes, according to the National Highway Traffic Safety Administration. Those errors include drunk driving, speeding, distraction, and fatigue. But a new report finds that self-driving cars can't be tested enough hours to determine their safety. The report from research firm RAND Corporationsays autonomous vehicles would need to be tested "hundreds of millions of miles and sometimes hundreds of billions of miles" to gain enough information to compare its safety to human-driven automobiles. Such thorough testing would require "tens and sometimes hundreds of years," which would make it impractical to accomplish before clearing the vehicles for regular consumer use, the report said.

The Winograd Schema Challenge was proposed by Hector Levesque in 2011 as an alternative to the Turing Test. Chief among its features is a simple question format that can span many commonsense knowledge domains. Questions are chosen so that they do not require specialized knoweldge or training, and are easy for humans to answer. This article details our plans to run the WSC and evaluate results.

We review the First International Competition on Computational Models of Argumentation (ICMMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

If the artificial intelligence research community is to have a challenge problem as an incentive for research, as many have called for, it behooves us to learn the principles of past successful inducement prize competitions. Those principles argue against the Turing test proper as an appropriate task, despite its appropriateness as a criterion (perhaps the only one) for attributing intelligence to a machine.

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way.