MIT's Personal Robotics Group has been one of the driving forces behind social robotics since… well, since they pretty much invented social robotics. Led by Professor Cynthia Breazeal, who is also founder of social robot startup Jibo, the MIT group has built an amazing collection of smart, cute, and squishy creatures, and now they have a new one. The latest, smartest, cutest, and squishiest social robot that MIT has been testing out is named Tega, and it's already gotten to work, adorably teaching Spanish to preschoolers. We spoke with Jackie Kory Westlund, a Ph.D. student in the MIT Media Lab who's been doing research with Tega, about why it's such a useful social assistive robotics platform and how to keep preschoolers from utterly destroying it with hugs. To provide some context for Tega, have a look at a couple of the other robots developed by MIT's Personal Robotics Group, which is really just an excuse to post one of my favorite robot videos of all time: You can sort of imagine that Dragonbot and Tofu maybe got extra cuddly one lonely night at the Media Lab, and Tega was the result.

Video Friday is your weekly selection of awesome robotics videos, collected by your mysophobic Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. XDog is a small electric quadruped designed and built by Xing Wang, a graduate student at Shanghai University, with support from his adviser Jia Wenchuan. The robot has 12 motors (each leg has 3 DoF), and uses force sensors on each foot, IMU, and joint-angle sensors for control.

A fast-food chain with restaurants in 28 states is getting ready to start serving burgers and fries in Champaign-Urbana. Bruce Kim, director of franchise development for Checkers and Rally's Restaurants, said the company is in the process of awarding a franchise for up to three new Checkers restaurants in the area. "We are a quick-service restaurant," he said. "We are known for our seasoned, seared and grilled burgers; fries; grilled, all-meat hot dogs; crazy good chicken wings; golden fish sandwiches; and ice cream. "Our seasoned fries were named the best fries for 2015 by Yahoo." Kim said Checkers restaurants typically stay open late, with most of them serving customers in their double drive-thru until 2 or 3 a.m. The Tampa, Fla.-based chain has 828 locations nationwide, and the website Thrillist recently named Checkers the fastest-growing fast-food chain in Illinois. Kim said the chain is up to 20 Checkers restaurants and seven Rally's restaurants in the state, and the next step is to start building stores in Champaign County. "There is plenty of room to grow, and we are trying to build several locations in Champaign-Urbana," Kim said. "Our studies show we have room for three stores, with our growth franchise-driven." "The community has a good, solid income base, good ethnic diversity and lots of university students.

Some time ago I started a journey into one of the most exciting fields in Computer Science -- Machine Learning. This is my subjective guide for anyone who would like to explore this topic, but don't know how to start. Your first steps should lead to Stanford Machine Learning class at Coursera by Andrew Ng. This course is simply brilliant! Along a way, you will be given everything you need to know, including algebra review.

Editor's note: This is the third in a series of articles about the effects of software and automation on the economy. You can read the other stories here and here. The way Hod Lipson describes his Creative Machines Lab captures his ambitions: "We are interested in robots that create and are creative." Lipson, an engineering professor at Cornell University (this July he's moving his lab to Columbia University), is one of the world's leading experts on artificial intelligence and robotics. His research projects provide a peek into the intriguing possibilities of machines and automation, from robots that "evolve" to ones that assemble themselves out of basic building blocks. A few years ago, Lipson demonstrated an algorithm that explained experimental data by formulating new scientific laws, which were consistent with ones known to be true. He had automated scientific discovery. Lipson's vision of the future is one in which machines and software possess abilities that were unthinkable until recently.

Voiceless in his life so far, a severely disabled 16-year-old is marvelling at being able to speak for the first time after breaking his silence with the words "Hello Mum", using a digital communication aid. James Walker is a rugby fan, likes pop music, lives with his family in Hull and has a girlfriend - Emily. He has a condition which caused hundreds of daily seizures when he was a child. Known as Lennox-Gastaut Syndrome, it left him with a severe learning disability and without the ability to walk or move. He says it's "funny" after being silent for so long that he can now communicate with friends and family and, as he puts it, "learn something exciting".

John Hennessy joined Stanford in 1977 right after receiving his Ph.D. from the State University of New York at Stony Brook. He soon became a leader of Reduced Instruction Set Computers. This research led to the founding of MIPS Computer Systems, which was later acquired for 320 million. There are still nearly a billion MIPS processors shipped annually, 30 years after the company was founded. Hennessy returned to Stanford to do foundational research in large-scale shared memory multiprocessors. In his spare time, he co-authored two textbooks on computer architecture, which have been continuously revised and are still popular 25 years later. This record led to numerous honors, including ACM Fellow, election to both the National Academy of Engineering and the National Academy of Sciences. Not resting on his research and teaching laurels, he quickly moved up the academic administrative ladder, going from the CS department chair to Engineering college dean to provost and finally to president in just seven years. He is Stanford's tenth president, its first from engineering, and he has governed it for an eighth of its existence. Since 2000, he doubled Stanford's endowment, including a record 6.2 billion for a single campaign. He used those funds to launch many initiatives--which often cross departmental lines--along with new buildings to house them. Undergraduate applications also doubled, for the first time making Stanford even more selective than Harvard.

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

A thesis submitted for the degree of Doctor of Philosophy of The Australian National University. In this work we introduce several new optimisation methods for problems in machine learning. Our algorithms broadly fall into two categories: optimisation of finite sums and of graph structured objectives. The finite sum problem is simply the minimisation of objective functions that are naturally expressed as a summation over a large number of terms, where each term has a similar or identical weight. Such objectives most often appear in machine learning in the empirical risk minimisation framework in the non-online learning setting. The second category, that of graph structured objectives, consists of objectives that result from applying maximum likelihood to Markov random field models. Unlike the finite sum case, all the non-linearity is contained within a partition function term, which does not readily decompose into a summation. For the finite sum problem, we introduce the Finito and SAGA algorithms, as well as variants of each. For graph-structured problems, we take three complementary approaches. We look at learning the parameters for a fixed structure, learning the structure independently, and learning both simultaneously. Specifically, for the combined approach, we introduce a new method for encouraging graph structures with the "scale-free" property. For the structure learning problem, we establish SHORTCUT, a O(n^{2.5}) expected time approximate structure learning method for Gaussian graphical models. For problems where the structure is known but the parameters unknown, we introduce an approximate maximum likelihood learning algorithm that is capable of learning a useful subclass of Gaussian graphical models.

This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.