Wouldn't it be great to be able to search videos for what people are actually saying instead of relying on tags or descriptions? Koemei (pronounced "co-may") aims to do just that through their cloud-based speech recognition software that rapidly transcribes video and audio, even if people have accents or more than one person is speaking. But the startup is also targeting the large quantities of media content from videoconferencing, webcasting, and classroom lectures being produced in business, government, and educational institutions, and by indexing the transcripts, video libraries will become easily searchable. According to the company, it currently takes about an hour for its system to automatically transcribe one hour of media at a cost of about $0.09 a minute, much cheaper than manual transcription, though user pricing for the service has not been announced. Building on 8 years of research, the startup comes out of the Idiap Research Institute in Switzerland, and is in the process of raising $1.5 million is a Series A round on top of its Angel funding since its 2010 launch.

The C5.0 algorithm uses the Max-Gain method of selecting the best attribute. H measures the information content or entropy in bits (i.e., number of yes/no questions that must be asked) associated with a set S of examples, which consists of the subset P of positive examples and subset N of negative examples. Note: 0 H(P,N) 1, where 0 no information, and 1 maximum information. Half the examples in S are positive and half are negative. Say all of the examples in S are positive and none are negative.

The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.

Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).

To some degree, that's inevitable: Many people who enroll in MOOCs may have no interest in doing homework, but simply plan to listen to video lectures in their spare time. Others, however, may begin courses with the firm intention of completing them but get derailed by life's other demands. Identifying those people before they drop out and providing them with extra help could make their MOOC participation much more productive. The problem is that you don't know who's actually dropped out -- or, in MOOC parlance, "stopped out" -- until the MOOC has been completed. One missed deadline does not a stopout make; but after the second or third missed deadline, it may be too late for an intervention to do any good.

Faculty, students, and industry representatives packed MIT's Grier Room on Thursday, Dec. 4, to learn about new research in robotics, machine learning, wireless power transfer, synthetic biology, and more -- all being conducted by undergraduates through the groundbreaking SuperUROP program within the Department of Electrical Engineering and Computer Science (EECS). "My feeling is some of this work is as good as master's-quality research. It's very good stuff," said Vincent Chan, the Joan and Irwin M. Jacobs Professor of Electrical Engineering and Aeronautics and Astronautics, who is advising two SuperUROP students this year. "We are creating a community of scholars. As they are exposed to the breadth of research in EECS, their excitement and enthusiasm to engage in research and innovation is contagious," said Anantha Chandrakasan, the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering and EECS department head, who launched the year-long SuperUROP research program in 2012 to expand the experience familiar to many through MIT's Undergraduate Research Opportunities Program (UROP).

The late professor Seth Teller created 6.811 (Principles and Practices in Assistive Technologies, or PPAT) in the fall of 2011. Through his extensive experience developing assistive technologies (AT) at MIT, his compassion for making technology available to all, and his innovative approach and drive to build this class, student interest in PPAT and AT has grown steadily since. Following Teller's untimely death on July 1 this year, a group of former PPAT and AT students including his graduate student William Li SM '12, who TA'd the inaugural PPAT offering; Grace Teo PhD '14, a former student and member of the MIT Assistive Technology Club; and a core group of students who took the class in 2013 have formed a team to continue Teller's legacy through both the coninuation of PPAT and an outgrowth known as "AT Hack," a one-day workshop launched in spring 2014. Li and Teo, who will co-instruct this year's class, and three other members of the team will work with Professor Rob Miller, MIT MacVicar Faculty Fellow, member of the Computer Science and Artificial Intelligence Lab (CSAIL), and co-education officer of the Department of Electrocal Engineering and Computer Science (EECS). Every year since the inaugural offering of PPAT, Miller had worked with Teller to help develop and teach the course.

MIT President L. Rafael Reif announced today a significant expansion of the Institute's programs in learning research and online and digital education -- from pre-kindergarten through residential higher education and lifelong learning -- that fulfills a number of recommendations made in 2014 by the Institute-Wide Task Force on the Future of MIT Education. Most notably, Reif announced the creation of the MIT Integrated Learning Initiative (MITili), to be led by Professor John Gabrieli, and a new effort to increase MIT's ability to improve science, technology, engineering, and mathematics (STEM) learning by students from pre-kindergarten through high school (pK-12), to be led by Professor Angela Belcher. The announcement also included a program to support faculty innovations in MIT residential education and new work to enhance MIT's continuing education programs. In keeping with the high priority of these new efforts and of the entire field of digital learning, Professor Sanjay Sarma, now dean of digital learning, will oversee them in the newly created position of vice president for open learning, reporting directly to Reif. Chancellor Cynthia Barnhart, who will share responsibility with Sarma for several aspects of this work, predicts that the programs announced today will have "far-reaching and tremendous implications for education -- for MIT students as well as for students not at MIT."

If you're too lazy to mow your own lawn, and too lazy to supervise a robot that will mow your own lawn, then outdoor equipment maker Husqvarna has an app for you. My Automower is a free app for iPhone, iPad, and iPod Touch that lets you control Husqvarna Automowers with GPS such as the 260 ACX from anywhere. The app displays the location of the Automower on Google Maps, and can order the machine to start or stop mowing, and automatically recharge itself. Users can also set mowing times and check the status of their Automower, as well as view instructional videos on its use. GPS is standard on the 260 ACX, which can mow 1.4 acres, recharge automatically, and text users if something disturbs its mowing.