"The rule of thumb is that both images--the one from the satellite and the one from the autonomous vehicle--have to have identical content for current techniques to work. The differences that they can handle are about what can be accomplished with an Instagram filter that changes an image's hues," says Anthony Fragoso (MS '14, PhD '18), lecturer and staff scientist, and lead author of the Science Robotics paper. "In real systems, however, things change drastically based on season because the images no longer contain the same objects and cannot be directly compared." The process--developed by Chung and Fragoso in collaboration with graduate student Connor Lee (BS '17, MS '19) and undergraduate student Austin McCoy--uses what is known as "self-supervised learning." While most computer-vision strategies rely on human annotators who carefully curate large data sets to teach an algorithm how to recognize what it is seeing, this one instead lets the algorithm teach itself.

All the sessions from Transform 2021 are available on-demand now. Hungryroot, an AI-powered delivery service, hopes to occupy a similar niche for online groceries in the United States. The recommender system uses a collaborative filtering, supervised learning model to match consumer preferences to foods. Customers answer questions about their dietary habits, the kinds of foods they (and family members) like, the family size, budget, and more. On a weekly basis, the Hungryroot algorithm predicts the groceries the customer might like.

Epilepsy is a common cause of morbidity and mortality, especially among children, despite advances in management regimens.1, 2 Accurate monitoring and tracking of seizures are important to evaluate seizure burden, recurrence risk, and response to treatment. Outside the hospital, seizure tracking relies on patients' and families' self-reporting, which is often unreliable due to underreporting, seizures missed by caregivers, and patients' difficulties recalling seizures.3-6 Although long-term video-electroencephalography (EEG) in the epilepsy monitoring unit (EMU) is the gold standard for accurately diagnosing and evaluating epilepsy,7 it is also time-consuming and costly, can be perceived as stigmatizing, and places a greater burden on patients and caregivers than seizure monitoring with wearable devices. Based on prior studies, there exists a large clinical gap and urgent medical need to detect a broad range of seizures, beyond focal to bilateral tonic–clonic seizures (FBTCSs) and generalized tonic–clonic seizures (GTCSs), with wearable devices.3, Recent advances in the use and development of non-EEG-based seizure detection devices utilizing a variety of sensors and modalities provided innovative opportunities to fill this gap and to monitor patients continuously in the outpatient setting.

Biometric authentication is a key piece of the tech industry's plans to make the world passwordless. But a new method for duping Microsoft's Windows Hello facial recognition system shows that a little hardware fiddling can trick the system into unlocking when it shouldn't. Services like Apple's FaceID have made facial recognition authentication more commonplace in recent years, with Windows Hello driving adoption even farther. Apple only lets you use FaceID with the cameras embedded in recent iPhones and iPads, and it's still not supported on Macs at all. But because Windows hardware is so diverse, Hello facial recognition works with an array of third-party webcams.

Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming and unreproducible manual trial-and-error process to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods, e.g., based on resampling error estimation for supervised machine learning, can be employed. It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.

A 3D-printed robotic hand controlled by pressurised water can complete the first level of classic computer game Super Mario Bros in less than 90 seconds. Ryan Sochol and his team at the University of Maryland were able to 3D print the hand in a single operation using a machine that can deposit hard plastic, a rubber-like polymer and a water-soluble "sacrificial" material.

An eerie four-legged robot is shown pacing through the woods with relative ease. But when brought inside and tested in other situations, such as slippery surfaces, it had balance issues and difficulty walking. In one example, when weighted bags were placed on its back, the robot fell over. With Facebook's AI software enabled, it wobbled but managed to stay upright and keep walking when the bags were tossed onto it. There are no cameras on the device. All of the robot's movements were guided by sensors in its feet and various joints, which allow it to experience the world through "touch."

Shortly before Prime Day in June, Amazon announced it was developing two robots for its infamously demanding distribution centers. Named "Bert" and "Ernie" after the Sesame Street Muppets, the robots, Amazon claimed, would help relieve the physical burden of its jobs by autonomously carting materials through distribution center floors and lifting heavy totes off shelves. They were not, the company stressed, intended to increase speed or replace workers, but to improve safety and free workers for tasks "that require…critical thinking skills." According to the company, the robots weren't some nefarious plot; instead, they embodied its empathy for workers and commitment to innovations that would help consumers and employees alike. The announcement's timing was convenient.

Tesla owners who want to tap into a self-driving feature when traveling local streets got a boost this weekend, when the electric car maker began releasing a much anticipated software update, reports say. The updates to the Full Self-Driving beta version 9 became available Saturday according to tech publication The Verge, and Electrek, a news site dedicated to reports about Tesla and other electric vehicles. The update expands assisted driving capabilities for a small pool of Tesla owners who get to try out features early, according to Electrek. Tesla's assisted driving programs have come under scrutiny in the wake of several accidents involving Teslas, including some that were in Autopilot mode. In the wake of those incidents, federal transportation officials have said Tesla has done an inadequate job of monitoring drivers to make sure they are engaged and also has permitted the Autopilot feature to be used on roads where it's not suitable.

But Peter Scott-Morgan has never been afraid of robots. As a scientist and roboticist by trade, he spent decades researching how artificial intelligence (AI) might transform our lives. Then, in 2017, Dr Scott-Morgan was diagnosed with motor neuron disease, the same paralysing condition that killed Stephen Hawking. Months after puzzling over his "wonky foot" falling asleep, he was told he had two years to live. To survive, he would turn to the technology he had spent his career researching.