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What would you like Martha Grabowski, Director of IS at LeMoyne, to speak about? - Syracuse Women in Machine Learning & Data Science (Syracuse, NY)

#artificialintelligence

WiMLDS's mission is to support and promote women and gender minorities who are practicing, studying or are interested in the fields of machine learning and data science. We create opportunities for members to engage in technical and professional conversations in a positive, supportive environment by hosting talks by women and gender minority individuals working in data science or machine learning. Events include technical workshops, networking events and hackathons. We are inclusive to anyone who supports our cause regardless of gender identity or technical background.


Big Data, Cybersecurity, IoT Startups Encouraged to Apply to GENIUS NY Unmanned Systems Accelerator Program

#artificialintelligence

SYRACUSE, NY – The GENIUS NY program, the largest unmanned systems accelerator in the world, is now opening its applications to include startups in big data (smart cities, cybersecurity) and internet of things (IoT) (smart devices, AI). GENIUS NY is CenterState CEO's in-residence business accelerator program at The Tech Garden in Central New York. The program invests $3 million in five early stage companies each year, while also providing incubator space, business programming, mentors and advisers, and resources. Now in its third year, it has already invested $9 million in 17 startups. Applications are open through Oct.1, 2019.


How Mathematicians in Chicago Are Stopping Water Leaks in Syracuse

#artificialintelligence

SYRACUSE, N.Y.--It was a nightmare scenario: As thousands of Syracuse University basketball fans poured into town on February 1, 2014 for a big match against arch rival Duke, a water main break flooded Armory Square, surrounding the city's iconic 24-second shot clock monument. Days before the game, there were 11 other water main breaks around the city. Mayor Stephanie Miner was desperate for help to get a handle on the problem; on average, water lines in the city were breaking 332 times a year, nearly once every day. But she couldn't get the state to help foot the bill for the onerous costs of updating the city's underground infrastructure. She even tried to shame state officials with a "Hunger Games"-style ad campaign that showed her wading in thigh-high water wielding a wrench.


Apple to pay $24.9 million to settle Siri patent lawsuit

AITopics Original Links

Apple has agreed to pay $24.9 million to a patent holding company to resolve a 5-year-old lawsuit accusing Siri of infringing one of its patents. Apple will pay the money to Marathon Patent Group, the parent company of Texas firm Dynamic Advances, which held an exclusive license to a 2007 patent covering natural language user interfaces for enterprise databases. Marathon reported the settlement in a filing with the U.S. Securities and Exchange Commission Tuesday. On Wednesday, in response to the settlement, Magistrate Judge David Peebles of U.S. District Court for the Northern District of New York dismissed a lawsuit against Apple filed by Dynamic Advances and Rensselaer Polytechnic Institute in Troy, New York, where the natural language technology was created. A trial had been scheduled to begin early next month in Syracuse, New York.


SS01-01-002.pdf

AAAI Conferences

Howard A. Blair EECS Dept., 2-177 SciTech Syracuse University Syracuse, NY 13210 USA Introduction Solutions to problems are often not unique. A representation of a problem as a theory in some logical formalism often admits a number of models representing solutions (Marek Truszczyfiski, 1999). The solution-representing models are perhaps required to come from a particular class. The typical strategy is to represent a class of problem instances E, for example the problem of determining a Hamiltonian circuit in a digraph if there is one, as a theory TE, and a specific instance I of E, e.g. a specific digraph, as a theory T1 in such a way that certain kinds of models of the combined theory TE TI represent the solutions, i.e. in the example, the required Hamiltonian circuits in I, as the result of a mapping from answer sets (the models) solutions of I. As a specific formalism for answer set programming, DATALOG programs with negation (Ceri, Gottlob, & Tanka, 1990) and their stable models have received a large amount of attention e.g.


Applying Link Analysis on Automatically Extracted Information from Texts Within KNOW-IT1 (.KNOWledge Base _Information Tools)

AAAI Conferences

Woojin Paik, Elizabeth Liddy, Eileen Allen, Eric Brown, Andrew Farris, Robert Irwin, Jennifer Liddy, and Ian Niles TextWise Inc. 2-212 Center for Science and Technology Syracuse, NY 13244 {woojin, liz, eileen, eric, drew, rjirwin, jennifer, ihniles}@textwise.com Introduction A robust information extraction system, which can accurately and rapidly convert vast amounts of textual data into a semantic network type knowledge representation scheme, will be a useful preprocessor for various link analysis applications. In this paper, we describe an end-to-end knowledge discovery system which takes raw texts as input and generates a knowledge base which can be utilized for a variety ofinforrnation access systems, such as a visual knowledge base browser and a question-answering system. We wish to explore the possibility of using our visual knowledge base browser as a link analyzer, and show that it is possible to apply link analysis techniques to vast amounts of textual data through an automatic information extraction system. Then, underlying natural language processing techniques which comprise the information extraction system will be described.


The Sixth Annual Knowledge-Based Software Engineering Conference

AI Magazine

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.