UK semantic technology company True Knowledge has released Evi, a mobile app that competes with Siri.
The mobile app is available on the Android Market and on iTunes. You can pose queries to either by speaking or typing. The Android app uses Google’s ASR speech technology and the iTunes app uses Nuance.
True Knowledge has been developing a natural answering question answering system since 2007. You can query the True Knowledge online via a Web interface. Tty the following links for some examples:
The Evi app has a number of additional features beyond the Web-based True Knowledge QA system and these wil probably be expanded on in the months to come.
A part-time, two person effort UMBC VP for Research Don Engel and his wife Marianne nearly won the DARPA Shredder Challenge. Their entry, Schroddon got a late start, but held the top leaderboard spot for quite a while before being bested by “All Your Shreds Are Belong To U.S.” at the end. The first prize was $50,000 and second was … well, priceless.
It still won’t be able to pass as a human like the Nexus 6, but Honda’s Asimo robot now enjoys more autonomy.
Honda Motor Co., Ltd. today unveiled an all-new ASIMO humanoid robot newly equipped with the world’s first1 autonomous behavior control technology. With a further advance in autonomy, the all-new ASIMO can now continue moving without being controlled by an operator. Moreover, with significantly improved intelligence and the physical ability to adapt to situations, ASIMO took another step closer to practical use in an office or a public space where many people come and go.
If you are in the DC area this weekend and are interested in using Semantic Web technologies, you should come to the AAAI 2011 Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges. It runs from Friday to Sunday midday at the he Westin Arlington Gateway in Arlington, Virginia.
Join us to meet the thought governmental and business leaders in US open government data activities, and discuss the challenges. The symposium features Friday (Nov 4) as governmental day with speakers on Data.gov, openEi.org, open gov data activities in NIH/NCI and NASA and Saturday (Nov 5) as R&D day with speakers from industry, including Google and Microsoft, as well international researchers.
This symposium will explore how AI technologies such as the Semantic Web, information extraction, statistical analysis and machine learning, can be used to make the valuable knowledge embedded in open government data more explicit, accessible and reusable.
Computer Science pioneer John McCarthy died at his home in his sleep on Monday. He was 84. He is noted for creating the Lisp programming language, making ground-breaking contributions to Artificial Intelligence (including naming the field), adding important results to the mathematical theory of computation, and helping to develop computer time sharing. He studied mathematics under John Nash at Princeton
McCarthy held the first “computer-chess” match in the mid-1960s between scientists in the US and the USSR, transmitting the moves by telegraph. The soviet team ran on inferior hardware and used Claude Shannon’s brute-force Type-A strategy while the MIT team had an IBM 7090 implemented Shannon’s more sophisticed Type-B approach that used a heuristic plausible move generator. The Soviets won.
McCarthy was born in 1927 in Boston and taught himself higher math using Caltech textbooks when his family moved to the area, allowing him to take advanced classes when he enrolled as a teenager. He received a Ph.D. from Princeton in 1951.
He won the Turing Award from the Association for Computing Machinery in 1972 and the National Medal of Science in 1991. Over the years, he held faculty appointments at Princeton, M.I.T., Dartmouth, and Stanford University, where he spend his las 39 years.
Genetic information for chronic disease prediction
Michael A. Grasso, MD, PhD
University of Maryland School of Medicine
1:00pm Friday 23 September 2011, 227 ITE
Type 2 diabetes and coronary artery disease are commonly occurring polygenic-multifactorial diseases, which are responsible for significant morbidity and mortality. The identification of people at risk for these conditions has historically been based on clinical factors alone. However, this resulted in prediction algorithms that are linked to symptomatic states, which have limited accuracy in asymptomatic individuals. Advances in genetics have raised the hope that genetic testing may aid in disease prediction, treatment, and prevention. Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We present an overview of genetic issues involved in polygenic-multifactorial diseases, and summarize ongoing efforts use this information for disease prediction.
Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine, and an Assistant Research Professor of Computer Science at the University of Maryland Baltimore County. He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, and the William Beaumont Medical Research Honor Society. He completed a residency at the University of Maryland School of Medicine, and currently works in the Department of Emergency Medicine at the University of Maryland Medical Center. He has been awarded more than $1,200,000 in grant funding from the National Institutes of Health, the National Bureau of Standards and Technology, and the Department of Defense, and has authored more than 35 scholarly papers and abstracts. His research interests include clinical decision support systems, clinical data mining, clinical image processing, personalized medicine, software engineering, database engineering, and human factors. He is also a semi-professional trumpet player and is interested in the specific medical needs of performing artists, especially instrumental musicians.
Here’s a word cloud that visualizes the 200 most significant words extracted from over 400 papers from our research group over the past ten years. Significance was estimated by tf-idf where the idf data is from a collection of newswire articles (thanks Paul!). The word cloud was created with Wordle.
“Today, hospitals and doctors use a system of about 18,000 codes to describe medical services in bills they send to insurers. Apparently, that doesn’t allow for quite enough nuance. A new federally mandated version will expand the number to around 140,000—adding codes that describe precisely what bone was broken, or which artery is receiving a stent. It will also have a code for recording that a patient’s injury occurred in a chicken coop.”
We want to see the search engine companies develop and support a Microdata vocabulary for ICD-10. An ICDM-10 OWL DL ontology has already been done, but a Microdata version might add a lot of value. We could use it on our blogs and Facebook posts to catalog those annoying problems we encounter each day, like W59.22XD (Struck by turtle, initial encounter), or Y07.53 (Teacher or instructor, perpetrator of maltreat and neglect).
Humor aside, a description logic representation (e.g., in OWL) makes the coding system seem less ridiculous. Instead of appearing as a catalog of 140K ground tags, it would emphasize that it is a collection of a much smaller number of classes that can be combined in productive ways to produce them or used to create general descriptions (e.g., bitten by an animal).
Many Google+ users have been reporting frequent notices about new followers that they don’t know and appear to be attractive young women. The suspicious followers have minimal profiles and no posts. These are obviously false accounts being created for some yet unknown purpose, but how can one prove it?
I just got a notice, for example, that Janet Smith of Philadelphia is following me. Now Janet Smith is a common name and Philadelphia is a big place — there are probably hundreds of people who live in the Philadelphia area with that name. The 990 other people she’s following seem like a pretty random bunch, though I do know many and have more than a few in my own circles. Most seem to have a fair number of followers.
So there is not much to go on other than her profile image. This is a great use for Google’s new image search. I dragged the picture into the image search query field and Google identified its best guess for the image as Indian actress Koyel Mullick. Sure enough, if you search for images with her name, the precise Janet Smith image is result number 15.
Of course, there are still some subtle issues. This is just one kind of false profile — one created for one identity but using an image from a different one. It’s common on most social media systems, including G+, for some people to use a picture of someone or something other than themselves. But it’s obvious to a human viewer that using a picture of a rabbit, Marilyn Monroe or the mighty Thor on your profile is not meant to deceive. It will be challenging to automate the process of discriminating the intent to deceive from modesty, homage or an ironic gesture.
The First Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one-day event to be held at the Johns Hopkins University in Baltimore on Friday, 23 September 2011. Its goal is to bring together students taking computational approaches to speech, language, and learning, so that they can introduce their research to the local student community, give and receive feedback, and engage each other in collaborative discussion. Attendance is open to all and free but space is limited, so online registration is requested by September 16. The program runs from 10:00am to 5:00pm and will include oral presentations, poster sessions, and breakout sessions.
Stanford is experimenting with an interesting idea — offering some of their most popular undergraduate computer science courses online for free and simultaneously with their regular offerings. An AI course was announced several weeks ago and now there are similar offerings for databases and machine learning. These are taught by first rate instructors (who are also top researchers!) and are the same courses that Stanford students take.
“A bold experiment in distributed education, “Introduction to Artificial Intelligence” will be offered free and online to students worldwide during the fall of 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford’s introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams.”
“A bold experiment in distributed education, “Introduction to Databases” will be offered free and online to students worldwide during the fall of 2011. Students will have access to lecture videos, receive regular feedback on progress, and receive answers to questions. When you successfully complete this class, you will also receive a statement of accomplishment. Taught by Professor Jennifer Widom, the curriculum draws from Stanford’s popular Introduction to Databases course.”
“A bold experiment in distributed education, “Machine Learning” will be offered free and online to students worldwide during the fall of 2011. Students will have access to lecture videos, lecture notes, receive regular feedback on progress, and receive answers to questions. When you successfully complete the class, you will also receive a statement of accomplishment. Taught by Professor Andrew Ng, the curriculum draws from Stanford’s popular Machine Learning course.”
If successful, this might be a game changer. Two weeks after the online AI course was announced, 56,000 students had signed up! The approach might work for many disciplines, not just CS. The Kahn Academy is a related effort.
Universities should keep an eye on them and think about how to adapt if they are successful. Most of our students will probably benefit from taking our traditional courses. If so, we should be able to explain the benefits from taking them (and make sure we deliver those benefits). At the same time, we may want to leverage the online material from these courses in a synergistic way.
The special issue on semantic sensing will be edited by Harith Alani, Oscar Corcho and Manfred Hauswirth. Papers will be reviewed on a rolling basis and authors are encouraged to submit before the final deadline of 20 December 2011.
The issue on the semantic and social web will be edited by John Breslin and Meena Nagarajan. Papers will be reviewed on a rolling basis and authors are encouraged to submit before the final deadline of 21 January 2012.
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