In recent years, entire industries have popped up that rely on the delicate interplay between human workers and automated software. Companies like Facebook work to keep hateful and violent content off their platforms using a combination of automated filtering and human moderators. In the medical field, researchers at MIT and elsewhere have used machine learning to help radiologists better detect different forms of cancer. What can be tricky about these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isn’t always merely a question of who does a task “better;” indeed, if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help. To tackle this complex issue, researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning system that can either make a prediction about a task, or defer the decision Continue reading An automated health care system that understands when to step in
Video Friday is your weekly selection of awesome robotics videos, collected by your 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!): AWS Cloud Robotics Summit – August 18-19, 2020 – [Virtual Conference] CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference] ICUAS 2020 – September 1-4, 2020 – Athens, Greece ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Virtual Conference] IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada ICSR 2020 – November 14-16, 2020 – Golden, Colorado Let us know if you have suggestions for next week, and enjoy today’s videos.
Engineers have been chasing a form of AI that could drastically lower the energy required to do typical AI things like recognize words and images. This analog form of machine learning does one of the key mathematical operations of neural networks using the physics of a circuit instead of digital logic. But one of the main things limiting this approach is that deep learning’s training algorithm, back propagation, has to be done by GPUs or other separate digital systems. Now University of Montreal AI expert Yoshua Bengio, his student Benjamin Scellier, and colleagues at startup Rain Neuromorphics have come up with way for analog AIs to train themselves. That method, called equilibrium propagation, could lead to continuously learning, low-power analog systems of a far greater computational ability than most in the industry now consider possible, according to Rain CTO Jack Kendall. Analog circuits could save power in neural networks in part Continue reading Startup and Academics Find Path to Powerful Analog AI
Art is often heralded as the greatest journey into the past, solidifying a moment in time and space; the beautiful vehicle that lets us momentarily escape the present. With the boundless treasure trove of paintings that exist, the connections between these works of art from different periods of time and space can often go overlooked. It’s impossible for even the most knowledgeable of art critics to take in millions of paintings across thousands of years and be able to find unexpected parallels in themes, motifs, and visual styles. To streamline this process, a group of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Microsoft created an algorithm to discover hidden connections between paintings at the Metropolitan Museum of Art (the Met) and Amsterdam’s Rijksmuseum. Inspired by a special exhibit “Rembrandt and Velazquez” in the Rijksmuseum, the new “MosAIc” system finds paired or “analogous” works from different cultures, Continue reading Algorithm finds hidden connections between paintings at the Met
iRobot has been on a major push into education robots recently. They acquired Root Robotics in 2019, and earlier this year, launched an online simulator and associated curriculum designed to work in tandem with physical Root robots. The original Root was intended to be a classroom robot, with one of its key features being the ability to stick to (and operate on) magnetic virtual surfaces, like whiteboards. And as a classroom robot, at $200, it’s relatively affordable, if you can buy one or two and have groups of kids share them. For kids who are more focused on learning at home, though, $200 is a lot for a robot that doesn’t even keep your floors clean. And as nice as it is to have a free simulator, any kid will tell you that it’s way cooler to have a real robot to mess around with. Today, iRobot is announcing a Continue reading iRobot’s New Education Robot Makes Learning to Code a Little More Affordable
Machines are gaining the ability to write, and they are getting terrifyingly good at it. More details
Roboticists love hard problems. Challenges like the DRC and SubT have helped (and are still helping) to catalyze major advances in robotics, but not all hard problems require a massive amount of DARPA funding—sometimes, a hard problem can just be something very specific that’s really hard for a robot to do, especially relative to the ease with which a moderately trained human might be able to do it. Catching a ball. Putting a peg in a hole. Or using a straight razor to shave someone’s face without Sweeney Todd-izing them.
Researchers identified a problem that holds the key to whether all encryption can be broken — as well as a surprising connection to a mathematical concept that aims to define and measure randomness. More details
Deep learning systems are revolutionizing technology around us, from voice recognition that pairs you with your phone to autonomous vehicles that are increasingly able to see and recognize obstacles ahead. But much of this success involves trial and error when it comes to the deep learning networks themselves. A group of MIT researchers recently reviewed their contributions to a better theoretical understanding of deep learning networks, providing direction for the field moving forward. “Deep learning was in some ways an accidental discovery,” explains Tommy Poggio, investigator at the McGovern Institute for Brain Research, director of the Center for Brains, Minds, and Machines (CBMM), and the Eugene McDermott Professor in Brain and Cognitive Sciences. “We still do not understand why it works. A theoretical framework is taking form, and I believe that we are now close to a satisfactory theory. It is time to stand back and review recent insights.” Climbing Continue reading Looking into the black box
In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with “relatively minor changes.” However, peer review may be set to undergo its biggest revolution ever—the integration of artificial intelligence. Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers. AIRA’s interactive online platform, which Continue reading Peer Review of Scholarly Research Gets an AI Boost