It's been an interesting day. The 45th President of the United States of America took office just two hours ago, and he is clearly unlike any other President that has gone before him. So just for fun, I thought I might feed his inauguration speech into Watson in real-time, in order to see what the smartest computer in the world had to say about it. Would he notice any anomalies, or insights that the professional political commentators might have missed? Might we some people respect Trump a little more if they looked at his speech more analytically than emotionally?

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The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a $5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

TLDR: IBM's Watson is about human augmentation, not termination

For the learning phase, Levinson used a combination of rote learning as in case-based reasoning, restructuring to derive significant generalizations, a similarity measure based on the generalizations, and a method of back propagation to estimate the value of any case that occurred in a game. For playing chess, the cases were board positions represented as graphs. Every position that occurred in a game was stored in a generalization hierarchy, such as those used in definitional networks. At the end of each game, the system used back propagation to adjust the estimated values of each position that led to the win, loss, or draw. When playing a game, the system would examine all legal moves from a given position, search for similar positions in the hierarchy, and choose the move that led to a position whose closest match had the best predicted value.

This webinar will be the final opportunity before the close of registration for potential competitors to ask any questions they have regarding prize registration. Prior to joining, please be sure to read the Competition Guidelines, available on ai.xprize.org

The department's research in Machine Learning contributes to the state-of-the-art of individual methods and algorithms as well as combinations of methods targeting particular tasks, for example, combining data-intensive methods with knowledge-based methods to produce user explanations for decision support. Our strongest contributions to the international research front until now have been within Bayesian learning and probabilistic reasoning, evolutionary learning and neural networks, and instance-based learning and case-based reasoning. In addition, we have ongoing activities at a high international level within large-scale data and information management. Over the last years there has been an increased interest in combined methods, e.g.

A content vector, that is an array of values that represents the information content suitable for the Vector space model matching, as explained in 4.; with paired justifications (They express the provenience of that element, if from initial interview or the current active stereotype, or feedback, etc.) Moreover, changes to the User Model are regulated using a fixed hierarchy, so that lower items in the hierarchy cannot overwrite the values set by higher items. For example an element modified by the user feedback is not affected by changes from the stereotypes, etc. Contexts are initializated all the same for every user that has a non-zero value in her/his user model content vector for each cluster having one or more non-null contexts. Then these contexts are copied in the user's model and evolves indipendently according to the single user history. A set of user keywords each weighted with a value representing its actual importance for the user.

The IBM Watson AI XPRIZE is a $5 million competition, challenging teams globally to develop and demonstrate how humans can collaborate with powerful AI technologies to tackle the world's grand challenges. The prize aims to accelerate adoption of AI technologies and spark creative, innovative, and audacious demonstrations of the technology that are truly scalable and solve societal grand challenges. To encourage innovation in any form, the competition is an open challenge in AI. Rather than set a single, universal goal for all teams, this competition will invite teams to each create their own goal and solution to a grand challenge. The IBM Watson AI XPRIZE is a four-year competition with annual milestone competitions in 2017 and 2018.

Photocopiers, PCs, and video conferencing rooms all rose from being technological novelties to standard tools of corporate life. Researchers at IBM are experimenting with an idea for another: a room where executives can go to talk over business problems with a version of Watson, the computer system that defeated two Jeopardy! An early prototype has been made in the Cognitive Environments Lab, which opened last year at IBM's Thomas J. Watson research center in Yorktown Heights, New York. It is intended to explore how software that can understand and participate in human interactions could "magnify human cognition," says Dario Gil, director for symbiotic cognitive systems at IBM research. The lab looks more or less like a normal meeting space, but with a giant display taking up one wall, and an array of microphones installed in the ceiling. Everything said in the room can be instantly transcribed, providing a detailed record of any meeting, and allowing the system to listen out for commands addressed to "Watson."