Clustering is an unsupervised machine learning technique for discovering and grouping related data points in huge datasets.

If you've ever swatted a mosquito away from your face, only to have it return again (and again and again), you know that insects can be remarkably acrobatic and resilient in flight. Those traits help them navigate the aerial world, with all of its wind gusts, obstacles, and general uncertainty. Such traits are also hard to build into flying robots, but MIT Assistant Professor Kevin Yufeng Chen has built a system that approaches insects' agility. Chen, a member of the Department of Electrical Engineering and Computer Science and the Research Laboratory of Electronics, has developed insect-sized drones with unprecedented dexterity and resilience. The aerial robots are powered by a new class of soft actuator, which allows them to withstand the physical travails of real-world flight.

Typically, drones require wide open spaces because they're neither nimble enough to navigate confined spaces nor robust enough to withstand collisions in a crowd. "If we look at most drones today, they're usually quite big," says Chen. "Most of their applications involve flying outdoors. The question is: Can you create insect-scale robots that can move around in very complex, cluttered spaces?" According to Chen, "The challenge of building small aerial robots is immense." Pint-sized drones require a fundamentally different construction from larger ones.

Washington: A study in the Journal of Medical Internet Research introduced Biocogniv's new AI-COVID software that can easily predict the probability of COVID-19 infection. A team of researchers from the University of Vermont and Cedars-Sinai discovered high accuracy in predicting the probability of COVID-19 infection using routine blood tests, which can help hospitals reduce the number of patients referred for scarce PCR testing. Lead author and University of Vermont Assistant Professor Timothy Plante, M.D., M.H.S said, "Nine months into this pandemic, we now have a better understanding of how to care for patients with COVID-19, but there's still a big bottleneck in COVID-19 diagnosis with PCR testing." PCR testing is the current standard diagnostic for COVID-19, and requires specific sampling, like a nasal swab, and specialized laboratory equipment to run. "According to data from over 100 US hospitals, the national average turnaround time for COVID-19 tests ordered in emergency rooms is above 24 hours, far from the targeted one-hour turnaround," Biocogniv Chief Operating Officer Tanya Kanigan, PhD, said.

Whether it's diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness. In artificial intelligence, the establishment of trust is still developing. In the past years, deep learning has proven to be remarkably good at difficult tasks in computer vision, natural language processing, and other fields that were previously off-limits for computers. But we also have ample proof that placing blind trust in AI algorithms is a recipe for disaster: self-driving cars that miss lane dividers, melanoma detectors that look for ruler marks instead of malignant skin patterns, and hiring algorithms that discriminate against women are just a few of the many incidents that have been reported in the past years.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Whether it's diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness. In artificial intelligence, the establishment of trust is still developing. In the past years, deep learning has proven to be remarkably good at difficult tasks in computer vision, natural language processing, and other fields that were previously off-limits for computers.

Natural language processing (NLP) is one of the most important technologies to arise in recent years. Specifically, 2019 has been a big year for NLP with the introduction of the revolutionary BERT language representation model. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. Convolutional Neural Network (CNNs) are typically associated with computer vision, but more recently CNNs have been applied to problems in NLP.