Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University's Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure. Artificial neural networks, a type of machine learning, can be trained to identify similarities―and even correlate parameters such as structure and properties―but there are two major challenges, says Agar. One is that the majority of vast amounts of data generated by materials experiments are never analyzed. This is largely because such images, produced by scientists in laboratories all over the world, are rarely stored in a usable manner and not usually shared with other research teams.

Matt Bilsky, founder and CEO of FLX Solutions, discusses the snake-like robot he invented called the FLX BOT. The FLX BOT consists of modular links, each with a joint that can extend and rotate to get into tight spaces. Each link includes sensors including inertial measurement units and a camera. The robot is used to navigate and work in challenging environments, such as above ceilings and within walls. Matt discusses the key innovations of his product as well as his academic and entrepreneurial journey that led him to the FLX BOT.

Supply chain professionals are optimistic about the potential for artificial intelligence within their operations, but they have also struggled with the technology during the coronavirus pandemic, according to a survey from Secondmind, which develops machine-learning applications for businesses. The survey (which polled more than 500 supply chain managers and planners using AI) found that 90% of respondents believe AI will transform supply chains for the better by 2025, while 82% have been frustrated by AI-powered decisions during the course of the pandemic. The discrepancy highlights potential barriers of AI while underscoring that professionals who have experienced these issues still see a future for the technology. AI is an umbrella term that can include many statistical or computer science techniques. Gary Brotman, vice president of product and marketing at Secondmind, said he views AI as a term for processes that allow a computer to do something that would traditionally be done by a person.

Content provided by Youshan Zhang, the first author of the paper Impact of ImageNet Model Selection on Domain Adaptation. It is known that training and updating of the machine learning model depend on data annotation. We often have a serious problem that lacks labeled data for training in the real world. Therefore, it is often necessary to transfer knowledge from an existing labeled domain to an unlabeled new domain. However, due to the phenomenon of data bias or domain shift, machine learning models do not generalize well from an existing domain to a novel unlabeled domain. Domain adaptation has been a promising method to mitigate the domain shift problem.

Robots are complex machines with lots of components. Each of these components has a precise purpose, and when each component acts as expected, it creates a seamless system that can accomplish intricate tasks. This idea scales to networks of robots working in tandem to accomplish even more complex tasks. In this case, when one machine falters or fails to collaborate with the others, it can cause chaos: Picture a drone flying away from its fleet and failing to photograph its assigned area, or a self-driving car getting too close to another and disrupting carefully designed platoon. Making networks like these smarter, more functional, and more efficient is the subject of two research projects at Lehigh University led by Nader Motee, an associate professor of mechanical engineering and mechanics at the P.C. Rossin College of Engineering and Applied Science.

This blog was coauthored by Maiya Moncino, a research associate in international economics at the Council on Foreign Relations. Just as the mobile phone and solar energy have allowed developing nations to leap-frog into more advanced stages of technology, advances in medical technology can provide easy access to maternal and women's healthcare in poor and rural areas around the world, particularly in developing countries. Every day, 830 women die from complications resulting from pregnancy or child birth (based on 2015 data). The vast majority of these deaths are preventable, and occur in regions that lack access to basic resources. Maternal mortality rates are significantly higher in African countries, South and Southeast Asia, and parts of Latin America.

IMAGE: Brian Davison, Associate Professor of Computer Science Engineering at Lehigh University, is principal investigator of an NSF-backed project to develop a search engine intended to help scientists and others locate... view more There was a time--not that long ago--when the phrases "Google it" or "check Yahoo" would have been interpreted as sneezes, or a perhaps symptoms of an oncoming seizure, rather than as coherent thoughts. Today, these are key to answering all of life's questions. It's one thing to use the Web to keep up with a Kardashian, shop for ironic T-shirts, argue with our in-laws about politics, or any of the other myriad ways we use the Web in today's world. But if you are a serious researcher looking for real data that can help you advance your ideas, how useful are the underlying technologies that support the search engines we've all come to take for granted? "Not very," says Brian Davison, associate professor of computer science at Lehigh University.

Want to order up a drawing? Say you want a picture of a bird with a yellow crown and black rings around its eyes. Researchers have enlisted artificial intelligence tools, including computer vision and natural language processing, to program a "drawing bot" that can create a picture from the ground up, based merely on a descriptive caption. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," Microsoft researcher Xiaodong He said in a report on the project.

If you're handed a note that asks you to draw a picture of a bird with a yellow body, black wings and a short beak, chances are you'll start with a rough outline of a bird, then glance back at the note, see the yellow part and reach for a yellow pen to fill in the body, read the note again and reach for a black pen to draw the wings and, after a final check, shorten the beak and define it with a reflective glint. Then, for good measure, you might sketch a tree branch where the bird rests. Now, there's a bot that can do that, too. The new artificial intelligence technology under development in Microsoft's research labs is programmed to pay close attention to individual words when generating images from caption-like text descriptions. This deliberate focus produced a nearly three-fold boost in image quality compared to the previous state-of-the-art technique for text-to-image generation, according to results on an industry standard test reported in a research paper posted on arXiv.org.

A possibly lethal exception could be the error that leads a self-driving car's AI to make the wrong decision at the wrong time. That is why researchers developed a bug-hunting method that can systematically expose bad decision-making by the deep learning algorithms deployed in online services and autonomous vehicles. The new DeepXplore method uses at least three neural networks--the basic architecture of deep learning algorithms--to act as "cross-referencing oracles" in checking each other's accuracy. Researchers at Columbia University and Lehigh University designed DeepXplore to solve an optimization problem in which they looked to strike the best balance between two objectives: maximizing the number of neurons activated within neural networks, and triggering as many conflicting decisions as possible among different neural networks. By assuming that the majority of neural networks will generally make the right decision, DeepXplore automatically retrains the neural network that made the lone dissenting decision to follow the example of the majority in a given scenario.