The current U.S. administration has launched a wara on academia. Indirect costs, or, more accurately, facility and administration expenses, support research but cannot be directly attributed to a specific project, such as lab infrastructure, utilities, and administrative support. These are real costs; the limit, which has since been suspended by courts, is a severe blow to biomedical research in the U.S. Beyond expanding this limit to other agencies, such as the National Science Foundation (NSF), the administration is also reportedly considering slashing NSF's annual budget from approximately US 9 billion down to about US 3– 4 billion. This would deal a devastating blow to academic U.S. research, especially computing research. As statedc by the Computing Research Association (CRA), "NSF budget cuts would put the future of U.S. innovation and security at risk."

A team of scientists claims to have discovered a new colour that humans cannot see without the help of technology. The researchers based in the United States said they were able to "experience" the colour, which they named "olo", by firing laser pulses into their eyes using a device named after the Wizard of Oz. Olo cannot be seen with the naked eye, but the five people who have seen it describe it as being similar to teal. Professors from the University of California, Berkeley and the University of Washington School of Medicine published an article in the journal, Science Advances, on April 18 in which they put forth their discovery of a hue beyond the gamut of human vision. They explained that they had devised a technique called Oz, which can "trick" the human eye into seeing olo.

A Cornell-led research team has developed an artificial intelligence-powered ring equipped with micro-sonar technology that can continuously and in real time track fingerspelling in American Sign Language (ASL). In its current form, SpellRing could be used to enter text into computers or smartphones via fingerspelling, which is used in ASL to spell out words without corresponding signs, such as proper nouns, names and technical terms. With further development, the device could potentially be used to continuously track entire signed words and sentences. "Many other technologies that recognize fingerspelling in ASL have not been adopted by the deaf and hard-of-hearing community because the hardware is bulky and impractical," said Hyunchul Lim, a doctoral student in the field of information science. "We sought to develop a single ring to capture all of the subtle and complex finger movement in ASL." Lim is lead author of "SpellRing: Recognizing Continuous Fingerspelling in American Sign Language using a Ring," which will be presented at the Association of Computing Machinery's conference on Human Factors in Computing Systems (CHI), April 26-May 1 in Yokohama, Japan.

American Sign Language (ASL) has long enabled real-time conversations for English-speaking people who are deaf and hard-of-hearing. But discussions often face significant lags when one or more conversants aren't fluent in the language system. But by combining deep learning artificial intelligence and micro-sonar technologies, researchers at Cornell University are developing a new wearable to help overcome the communication barriers. With further refinement, SpellRing may one day facilitate entire conversations regardless of your ASL comprehension skills. ASL's earliest iterations developed in the early 18th century at the American School for the Deaf in Hartford, Connecticut.

With the goal of uncovering the challenges faced by European AI students during their research endeavors, we surveyed 28 AI doctoral candidates from 13 European countries. The outcomes underscore challenges in three key areas: (1) the findability and quality of AI resources such as datasets, models, and experiments; (2) the difficulties in replicating the experiments in AI papers; (3) and the lack of trustworthiness and interdisciplinarity. From our findings, it appears that although early stage AI researchers generally tend to share their AI resources, they lack motivation or knowledge to engage more in dataset and code preparation and curation, and ethical assessments, and are not used to cooperate with well-versed experts in application domains. Furthermore, we examine existing practices in data governance and reproducibility both in computer science and in artificial intelligence. For instance, only a minority of venues actively promote reproducibility initiatives such as reproducibility evaluations. Critically, there is need for immediate adoption of responsible and reproducible AI research practices, crucial for society at large, and essential for the AI research community in particular. This paper proposes a combination of social and technical recommendations to overcome the identified challenges. Socially, we propose the general adoption of reproducibility initiatives in AI conferences and journals, as well as improved interdisciplinary collaboration, especially in data governance practices. On the technical front, we call for enhanced tools to better support versioning control of datasets and code, and a computing infrastructure that facilitates the sharing and discovery of AI resources, as well as the sharing, execution, and verification of experiments.

Elvis Nava is a fellow at ETH' Zurich's AI center as well as a doctoral student at the Institute of Neuroinformatics and in the Soft Robotics Lab. In ETH Zurich's Soft Robotics Lab, a white robot hand reaches for a beer can, lifts it up and moves it to a glass at the other end of the table. There, the hand carefully tilts the can to the right and pours the sparkling, gold-coloured liquid into the glass without spilling it. Computer scientist Elvis Nava is the person controlling the robot hand developed by ETH start-up Faive Robotics. The 26-year-old doctoral student's own hand hovers over a surface equipped with sensors and a camera.

The Department of Computer Science, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, is looking for a Doctoral student in computing science with a focus on responsible AI with learning from multiple representations. The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 60 Doctoral students within the department consists of a diverse group from different nationalities, background and fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. Is this interesting for you?

Using a miniature camera and a customized deep neural network, Cornell researchers have developed a first-of-its-kind wristband that tracks the entire body posture in 3D. BodyTrak is the first wearable to track the full body pose with a single camera. If integrated into future smartwatches, BodyTrak could be a game-changer in monitoring user body mechanics in physical activities where precision is critical, said Cheng Zhang, assistant professor of information science and the paper's senior author. "Since smartwatches already have a camera, technology like BodyTrak could understand the user's pose and give real-time feedback," Zhang said. "That's handy, affordable and does not limit the user's moving area."

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations. This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker's voice and reduce background noise, "ClearBuds" use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone. The researchers presented this project June 30 at the ACM International Conference on Mobile Systems, Applications, and Services. "ClearBuds differentiate themselves from other wireless earbuds in two key ways," said co-lead author Maruchi Kim, a doctoral student in the Paul G. Allen School of Computer Science & Engineering.

Digital information is everywhere in the era of smart technology, where data is continuously generated by and communicated among cell phones, smart watches, cameras, smart speakers and other devices. Securing digital data on handheld devices requires massive amounts of energy, according to an interdisciplinary group of Penn State researchers, who warn that securing these devices from bad actors is becoming a greater concern than ever before. Led by Saptarshi Das, Penn State associate professor of engineering science and mechanics, researchers developed a smart hardware platform, or chip, to mitigate energy consumption while adding a layer of security. The researchers published their results on June 23 in Nature Communications. "Information from our devices is currently stored in one location, the cloud, which is shared and stored in large servers," said Das, who also is affiliated with the Penn State School of Electrical Engineering and Computer Science, the Materials Research Institute and the College of Earth and Mineral Sciences' Department of Materials Science and Engineering.