"Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. This simplifies things for users in two ways. First, questions do not often translate into a simple list of keywords. ...Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents."
– from Bonnie Webber & Nick Webb. Question Answering. In The Handbook of Computational Linguistics and Natural Language Processing. Alexander Clark, Chris Fox, Shalom Lappin (Eds.). Wiley, 2010.
Wouldn't it be great if an Android app could see and understand its surroundings? Can you imagine how much better its user interface could be if it could look at its users and instantly know their ages, genders, and emotions? Well, such an app might seem futuristic, but it's totally doable today. With the IBM Watson Visual Recognition service, creating mobile apps that can accurately detect and analyze objects in images is easier than ever. In this tutorial, I'll show you how to use it to create a smart Android app that can guess a person's age and gender and identify prominent objects in a photograph.
The alliance is the latest by IBM in a bid to harness Watson's cognitive learning capabilities to benefit millions of college students and professors. The announcement follows a separate agreement announced at the end of June between IBM and Blackboard, and the roll out of an IBM Watson-enabled app for Apple earlier this month, among other initiatives. For Pearson, the alliance represents a chance to combine its global offering of digital learning products with IBM's cognitive learning platform in an effort to give students a more immersive learning experience with their college courses. And it promises to give instructors greater insights about how well students are navigating through their courses. To accomplish that, Watson will essentially ingest and analyze all of Pearson courseware.
We describe a course in which students train an instance of Watson and develop an application that interacts with the trained instance. Additionally, students learn technical information about the Jeopardy! version of Watson and they discuss a future infused with cognitive assistants. In this paper, we provide learning outcomes and course assessment items. We provide detailed course materials and advice for instructors interested in teaching such a course. The advice is in the form of best practices, a description of a successful use case and an evaluation of our experience teaching this course.
We developed a course in which students train an instance of Watson and develop an application that interacts with the trained instance. Additionally, students learn technical in-formation about the Jeopardy! version of Watson and they discuss a future infused with cognitive assistants. In this poster, we justify this course, characterize major assessment items and provide advice on choosing a domain.
Friedland, Noah S., Allen, Paul G., Matthews, Gavin, Witbrock, Michael, Baxter, David, Curtis, Jon, Shepard, Blake, Miraglia, Pierluigi, Angele, Jurgen, Staab, Steffen, Moench, Eddie, Oppermann, Henrik, Wenke, Dirk, Israel, David, Chaudhri, Vinay, Porter, Bruce, Barker, Ken, Fan, James, Chaw, Shaw Yi, Yeh, Peter, Tecuci, Dan, Clark, Peter
Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle. This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge, empirical evaluation, results, and failure analysis.