A new advance in machine learning allows a computer program to recognize and draw handwritten characters based off a few examples, reports John Markoff for The New York Times. Markoff explains that the “improvements are noteworthy because so-called machine-vision systems are becoming commonplace in many aspects of life.”
Joel Achenbach reports for The Washington Post on the new program developed by researchers from MIT, NYU and the University of Toronto that can learn by example, a characteristic of human learning. Prof. Joshua Tenenbaum explains that the new system has made “a significant advance in capturing the way that people are thinking about these concepts.”
Los Angeles Times reporter Amina Khan writes that researchers have developed a program that learns to recognize and draw handwritten characters based off a few examples. Prof. Joshua Tenenbaum explains that the system, “can learn a large class of visual concepts in ways that are hard to distinguish from human learners.”
Hilary Brueck writes for Fortune that researchers from MIT, NYU and the University of Toronto have developed a new technique that allows machines to learn in a more human-like manner. The new technique “comes one step closer to getting machines to learn new things in a one-shot manner, more like humans do.”
Researchers have developed a learning program that can recognize handwritten characters after seeing only a few examples, reports Emily Chung for CBC News. The program “could lead to computers that are much better at speech recognition — especially recognizing uncommon words — or classifying objects and behaviour for businesses or the military.”
MIT researchers have developed an algorithm that can predict household income in urban areas based off of Google Street View images, writes Bourree Lam for The Atlantic. The algorithm "explains 77 percent of the variation in income at the block-group level,” explains graduate student Nikhil Naik.
Prof. Scott Aaronson speaks with New Scientist reporter Jacob Aron about Google’s D-Wave quantum computer. “This is certainly the most impressive demonstration so far of the D-Wave machine’s capabilities,” says Aaronson. “And yet, it remains totally unclear whether you can get to what I’d consider ‘true quantum speedup’ using D-Wave’s architecture.”
In this video, CBS Boston’s Bree Sison reports on MIT startup Affectiva, which is developing technology that can identify human emotions and could help with mental health. Rana el Kaliouby, Affectiva CSO and co-founder, explains that the technology could “tell you something is off, or flag it to a friend or doctor. Or maybe it could customize a digit experience to help you.”
Emma Woollacott reports for Forbes on Vuvuzela, a text-messaging system that MIT researchers developed to encrypt the metadata and content of messages. “Vuvuzela uses multiple servers instead of one, to give each message multiple layers of encryption,” writes Woollacott.
In this video, CBS News correspondent Don Dahler speaks with Prof. Dina Katabi about her group’s work developing wireless technology that can track a person’s motion through walls. Katabi and her colleagues demonstrated how the system also detects a person’s elevation and could be used to help protect seniors at risk of falling.
In an article for Optics.org, Matthew Peach writes that MIT researchers have developed a technique that exploits the polarization of light to improve the quality of 3-D imaging. The technique “could lead to high-quality 3-D cameras integrated into cellphones, and perhaps to the ability to photograph an object and then use a 3-D printer to produce a replica.”
In an article for New Scientist, Anna Nowogrodzki writes that MIT researchers have developed a device that allows users to answer the phone with a kick of their foot. “The system’s algorithm analyses the foot’s motion and transmits the information via Bluetooth to your phone,” writes Nowogrodzki.
Graduate student Daniel McDuff is developing a computer system that can read human emotions by monitoring facial movements, reports Jane Wakefield for BBC News. “It translates that into seven of the most commonly recognized emotional states - sadness, amusement, surprise, fear, joy, disgust and contempt,” McDuff explains.
In this video, BBC News reporter Stephen Beckett speaks with Prof. Dina Katabi about a new system her group developed that can track people through walls using wireless signals. “It’s using these very low-power signals, sending them, and observing the reflection of the body through the wall,” explains Prof. Dina Katabi.
Huffington Post reporter Nitya Rajan writes that MIT researchers have developed a device that can see through walls. Rajan explains that the device works by “sending wireless signals through a wall and capturing whatever bounces back off to put together an image of the person on the other side of the wall.”