As powerful as reinforcement learning is, Dr. LeCun says he believes that other forms of machine learning are more critical to general intelligence. "My money is on self-supervised learning," he said, referring to computer systems that ingest huge amounts of unlabeled data and make sense of it all without supervision or reward. He is working on models that learn by observation, accumulating enough background knowledge that some sort of common sense can emerge. "Imagine that you give the machine a piece of input, a video clip, for example, and ask it to predict what happens next," Dr. LeCun said in his office at New York University, decorated with stills from the movie "2001: A Space Odyssey." "For the machine to train itself to do this, it has to develop some representation of the data. It has to understand that there are objects that are animate and others that are inanimate. The inanimate objects have predictable trajectories, the other ones don't."

HRI2020 has already kicked off with workshops and the Industry Talks Session on April 3, however the first release of videos has only just gone online with the welcome from General Chairs Tony Belpaeme, ID Lab, University of Ghent and James Young, University of Manitoba. There is also a welcome from the Program Chairs Hatice Gunes from University of Cambridge and Laurel Riek from University of San Diego, requesting that we all engage with the participants papers and videos. The theme of this year's conference is "Real World Human-Robot Interaction," reflecting on recent trends in our community toward creating and deploying systems that can facilitate real-world, long-term interaction. This theme also reflects a new theme area we have introduced at HRI this year, "Reproducibility for Human Robot Interaction," which is key to realizing this vision and helping further our scientific endeavors. This trend was also reflected across our other four theme areas, including "Human-Robot Interaction User Studies," "Technical Advances in Human-Robot Interaction," "Human-Robot Interaction Design," and "Theory and Methods in Human-Robot Interaction."

Animal Crossing: New Horizons is a video game from Nintendo that's bringing comfort to millions of socially distant people around the world. MEGAN MANATA, BYLINE: It's a new day, perfect weather to go outside and water your flowers or maybe you'd like to spend your morning fishing. Maybe catching bugs is more your speed. Whatever way you want to spend your day, you can also pop by for a quick chat with your friendly furry neighbors. VANESSA NGUYEN: Right now, my favorite is Flo.

Author to whom correspondence should be addressed. Recently, methods based on Faster region-based convolutional neural network (R-CNN) have been popular in multi-class object detection in remote sensing images due to their outstanding detection performance. The methods generally propose candidate region of interests (ROIs) through a region propose network (RPN), and the regions with high enough intersection-over-union (IoU) values against ground truth are treated as positive samples for training. In this paper, we find that the detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially, detection performance of small objects is poor when choosing a normal higher threshold, while a lower threshold will result in poor location accuracy caused by a large quantity of false positives.

In China, doctors use artificial intelligence tools provided by Huawei Technologies Co. to detect signs of Covid-19 in CT scans. Chinese tech giant Baidu Inc. devised an algorithm that can analyze the biological structure of the new coronavirus and made it available to scientists working on a vaccine. AI is also behind biometric identification systems being rolled out by governments to track the virus and enforce lockdown efforts, including temperature screening systems deployed throughout Beijing and CCTV cameras hooked up to facial-recognition software in Moscow. "AI is being used to fight the virus on all fronts, from screening and diagnosis to containment and drug development," says Andy Chun, an adjunct professor at City University of Hong Kong and AI adviser at the Hong Kong Computer Science Society, a nonprofit industry group. The pandemic is opening up a massive opportunity for the tech industry, while it shines a light on calls for more scrutiny of AI innovations being developed faster than regulators are able to devise rules to protect citizens' rights.

'Curse of dimensionality' is a well-known problem in Data Science, which often causes poor performance, inaccurate results, and, most importantly, a similarity measure break-down. The primary cause of this is because high dimensional datasets are typically sparse, and often a lower-dimensional structure or'Manifold' would embed this data. So there is a non-linear relationship among the variables (or features or dimensions), which we need to learn to compute better similarity. Manifold learning is an approach to non-linear dimensionality reduction. The basis for algorithms in manifold learning is that the dimensionality of many data sets is only artificially high 1.

In light of the recent events surrounding Covid-19, learning for grades K-12 looks very different than it did a month ago. Parents and educators may be feeling overwhelmed about turning their homes into classrooms. With that in mind, a team led by Media Lab Associate Professor Cynthia Breazeal has launched aieducation.mit.edu to share a variety of online activities for K-12 students to learn about artificial intelligence, with a focus on how to design and use it responsibly. Learning resources provided on this website can help to address the needs of the millions of children, parents, and educators worldwide who are staying at home due to school closures caused by Covid-19, and are looking for free educational activities that support project-based STEM learning in an exciting and innovative area. The website is a collaboration between the Media Lab, MIT Stephen A. Schwarzman College of Computing, and MIT Open Learning, serving as a hub to highlight diverse work by faculty, staff, and students across the MIT community at the intersection of AI, learning, and education. "MIT is the birthplace of Constructionism under Seymour Papert.

Researchers behind an AI app that detects coronavirus in your voice have asked for volunteers to help by uploading audio of them coughing, breathing, and talking. Scientists from Cambridge University will use the data to develop machine learning algorithms that analyze a voice for symptoms of COVID-19. The COVID-19 Sounds App joins a growing list of tools using voice analysis to diagnose the coronavirus. The method remains unproven, but the team believes the sounds made by COVD-19 patients are so specific that they can reveal who has the disease. "Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they're speaking, as well as a dry cough, and the intervals of their breathing patterns," said Cambridge University Professor Cecilia Mascolo, who led the development of the app.

There's no denying that artificial intelligence is having a huge impact on our lives. According to PwC, AI will add $16 trillion to the world's economy over the next 10 years as automated decision-making spreads widely. Despite this incredible impact, AI doesn't bring much value for some problems, like predicting a viral pandemic, forecasting the winner of the presidential election, or servicing clients with diverse needs, experts say. Data is, of course, the rootstock for all forms of AI, whether it takes the form of a basic search engine or a self-driving car. But it turns out that some data are quite hard to come by, even for some of the most high-impact events.

We consider an NP-hard selective and periodic inventory routing problem (SPIRP) in a waste vegetable oil collection environment. This SPIRP arises in the context of reverse logistics where a biodiesel company has daily requirements of oil to be used as raw material in its production process. These requirements can be fulfilled by using the available inventory, collecting waste vegetable oil or purchasing virgin oil. The problem consists in determining a period (cyclic) planning for the collection and purchasing of oil such that the total collection, inventory and purchasing costs are minimized, while meeting the company's oil requirements and all the operational constraints. We propose a MIP-based heuristic which solves a relaxed model without routing, constructs routes taking into account the relaxation's solution and then improves these routes by solving the capacitated vehicle routing problem associated to each period. Following this approach, an a posteriori performance guarantee is ensured, as the approach provides both a lower bound and a feasible solution. The performed computational experiments show that the MIP-based heuristic is very fast and effective as it is able to encounter near optimal solutions with low gaps within seconds, improving several of the best known results using just a fraction of the time spent by a state-of-the-art heuristic. A remarkable fact is that the proposed MIP-based heuristic improves over the best known results for all the large instances available in the literature.