Polar Manufacturing has been making metal hinges, locks, and brackets in south Chicago for more than 100 years. Some of the company's metal presses--hulking great machines that loom over a worker--date from the 1950s. Last year, to meet rising demand amid a shortage of workers, Polar hired its first robot employee. The robot arm performs a simple, repetitive job: lifting a piece of metal into a press, which then bends the metal into a new shape. And like a person, the robot worker gets paid for the hours it works.
Surrey University researchers have demonstrated proof-of-concept of using their multimodal transistor (MMT) in artificial neural networks that mimic the human brain. According to the university, the advance marks a key step towards using thin-film transistors as artificial intelligence hardware and moves edge computing forward, with the prospect of reducing power needs and improving efficiency, rather than relying solely on computer chips. The MMT, first reported by Surrey researchers in 2020, is said to overcome long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems. Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations.
He comes with an army of names. Hume Initiative, the nonprofit, has convened an ethics committee with many heavy-hitters in the field of emotional and ethical AI, including the founder of Google's human-centered Empathy Lab, Danielle Krettek Cobb; the "algorithmic fairness" expert Karthik Dinakar; and Dacher Keltner, the University of California at Berkeley professor who was Cowen's graduate school mentor and advised Pixar on the emotions in "Inside Out."
In MIT 2.C161, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions. Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering.
WiFi and Bluetooth-based methods are accurate in detecting social distancing breaches and our approach complements them. The WiFI and Bluetooth-based methods need appropriate sensing technologies and cannot be easily deployed in all kinds of environments (e.g., public places or isolated locations). These methods also need additional infrastructure to be in place for detection. Our method uses the visual feed from a depth camera onboard a mobile robot and existing CCTV infrastructure (if available) to detect social distancing breaches. In addition, the robot can autonomously navigate and interact with people and encourage them to maintain social distancing.
A new portable scanner combines laser scanning technology with cameras to create precise 3D images in colour, and it could be used for everything from infrastructure inspection to construction to robot vision. Lidar measures the distance to surfaces using a laser. Each measurement records a point in space, building a "point cloud" to show surfaces and objects. Unlike a camera, the point cloud gives exact distances and dimensions, but the images are monochromatic and can be hard to interpret.
A robot piloted by a ball of algae can swim through water and move around obstacles, powered only by photosynthesis. Neil Phillips at the University of the West of England, UK, and his colleagues wanted to build a robot with no electronic parts, meaning it wouldn't interfere with any electromagnetically sensitive measurement instruments. The team inserted a marimo, a ball of algae that forms naturally in freshwater currents, inside a 3D-printed plastic spherical shell equipped with vents.
California is evaluating whether Tesla's self-driving tests require regulatory oversight, following "videos showing a dangerous use of that technology" and federal investigations into Tesla vehicle crashes, a state regulator said. The California department of motor vehicles previous said that Tesla's full self-driving, or FSD, beta requires human intervention and therefore is not subject to its regulations on autonomous vehicles. But the agency is revisiting that decision "following recent software updates, videos showing a dangerous use of that technology, open investigations by the National Highway Traffic Safety Administration (NHTSA), and the opinions of other experts", the department said in a letter on Friday to Lena Gonzalez, chair of the state senate transportation committee. The Los Angeles Times first reported the letter. Tesla did not respond to a request for comment.
University of South Australia's Habib Habibullah says their algorithm could be applied in many environments, including industrial warehouses where robots are commonly used, for robotic fruit picking, packing and pelletizing, and also for restaurant robots An algorithm developed by researchers at the University of South Australia (UniSA) aims to help robots avoid humans and other obstacles in their path while taking the fastest, safest route to their destination. The researchers based their model on the best elements of existing algorithms and used it to create a TurtleBot able to avoid collisions by adjusting its speed and direction. They performed simulations in nine different scenarios and found their model outperformed the online collision avoidance algorithms Dynamic Window Approach and Artificial Potential Field. Said UniSA's Habib Habibullah, "Our proposed method sometimes took a longer path, but it was faster and safer, avoiding all collisions."
Iktos is working with Astrogen to use artificial intelligence (AI) to identify small molecules as candidates for the treatment of Parkinson's disease. Under the collaboration's terms, Iktos will apply its proprietary machine-learning algorithm to virtually "sketch"molecules directed against a defined target and shortlist candidates for preclinical studies, the companies reported in a press release. That target is not yet public. In turn, Astrogen will screen the candidates in the lab and inside living organisms, and it will guide the development process from preclinical to clinical stages. The companies will work together in the production and selection of molecules that show the most promise as a Parkinson's treatment.