“To fully realize the benefits of digital innovation in health, including artificial intelligence, greater investments are needed in enabling building blocks – from supporting government and practitioner decision-making to ensuring data systems are responsive and inclusive,” noted Rebecca Distler, the Foundation’s Strategist for AI, Data, and Digital Health. “Our new grants reflect ongoing exploration of how to ensure digitally enabled approaches continue to put people and communities at the center of health interventions.”
The portfolio includes a wide range of organizations – from new and small nonprofits to established research institutions – working in countries across four continents. It collectively represents an ecosystem approach, from standards and exchange architectures to research and project implementation to governance and practitioner support.
A new study by the Oregon State University College of Engineering shows that machine learning techniques can offer powerful new tools for advancing personalized medicine, care that optimizes outcomes for individual patients based on unique aspects of their biology and disease features.
The research with machine learning, a branch of artificial intelligence in which computer systems use algorithms and statistical models to look for trends in data, tackles long-unsolvable problems in biological systems at the cellular level, said Oregon State’s Brian D. Wood, who conducted the study with then OSU Ph.D. student Ehsan Taghizadeh and Helen M. Byrne of the University of Oxford.
“Those systems tend to have high complexity – first because of the vast number of individual cells and second, because of the highly nonlinear way in which cells can behave,” said Wood, a professor of environmental engineering. “Nonlinear systems present a challenge for upscaling methods, which is the primary means by which researchers can accurately model biological systems at the larger scales that are often the most relevant.”
“Camera traps collect a ton of data,” [Sara Beery] says. “It is incredibly time-consuming for a human to go through all of that data … and get out the information that’s hidden in all those pixels.”
So Beery helped develop Microsoft’s AI for Earth MegaDetector, a model that detects animals in camera trap photos.
Working in the field is also an important part of Beery’s research. Oftentimes with machine learning, solutions and changes don’t always translate to the field. So she collaborates with ElephantBook in the Masai Mara in Kenya, keeping tabs on elephants using a hybrid human-AI approach that she thinks is an important next phase in conservation technology.
A team of researchers from the University of Wisconsin Milwaukee and the Center for Free-Electron Laser Science at the Deutsches Elektronen-Synchrotron in Germany have combined machine learning and quantum mechanical calculations to get the most precise record yet of structural changes in a photoactive yellow protein (PYP) that has been excited by light. Their study, published in Nature in November, showed that they were able to make movies of processes that occur in quadrillionths of a second.
When PYP absorbs light, it absorbs its energy, then rearranges itself. Because the protein’s function inside the cell is determined by its structure, whenever PYP folds or bends after being illuminated, this triggers huge changes. One important example of proteins interacting with light is in plants during photosynthesis, says Abbas Ourmazd, a physicist at UWM and coauthor on the study. More specifically, PYP is similar to proteins in our eyes that help us see at night, when a protein called retinal changes shape, activating some of our photoreceptor cells, explains Petra Fromme, director of the Biodesign Center for Applied Structural Discovery at Arizona State University, who was not involved with the study. PYP’s shape change also helps some bacteria detect blue light that may be damaging to their DNA so they can move away from it, Fromme notes.
Proceedings of the National Academy of Sciences; Matthias S. Treder et al.
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How do we adaptively switch from perceiving the external world to retrieving goal-relevant internal memories? To tackle this question, we used—in a cued-recall paradigm—direct intracranial recordings from the human hippocampus complemented by high-density scalp electroencephalography (EEG). We found that a hippocampal signal ∼500 ms after a perceptual cue marks the conversion from external (perceptual) to internal (mnemonic) representations. This sets in motion a recall cascade involving posterior parietal and medial prefrontal cortex, revealed via source-localized and time-resolved EEG alpha power. Together, these results unveil the hippocampal–cortical dynamics supporting rapid and flexible memory recall.
University of Maryland, The Diamondback student newspaper, Jamie Oberg
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The University of Maryland, along with two other higher education institutions in the state, received a $3 million grant for an initiative to increase diversity in college leadership.
“Breaking the M.O.L.D.” is a joint project between the University of Maryland, Baltimore County, Morgan State University and this university to create a pipeline for underrepresented groups, primarily people of color and women, in leadership across the three schools.
The Georgia Institute of Technology was awarded a grant from the U.S. Department of Commerce’s Economic Development Administration (EDA) as part of its $1 billion Build Back Better Regional Challenge. Georgia Tech is one of 60 entities to be awarded funding to assist communities nationwide in their efforts to accelerate the rebuilding of their economies in the wake of the pandemic.
As a leader in artificial intelligence, manufacturing research, and innovation-led economic development, Georgia Tech will utilize the grant for technical assistance to plan the Georgia Artificial Intelligence Manufacturing Corridor (GA-AIM). Led by Thomas Kurfess and Aaron Stebner in the George W. Woodruff School of Mechanical Engineering and in collaboration with local partners, GA-AIM will fill existing technology gaps, build a technological opportunity framework that includes underrepresented communities and rural Georgia counties, and better secure the state’s manufacturing infrastructure.
Researchers have developed a test to reveal bacterial contamination in dairy products well before they have a chance to reach anyone’s lips.
Researchers at McMaster University, with support provided by Toyota Tsusho Canada, Inc., have proven a method that will allow producers, packagers and retailers to detect bacterial contamination in milk products simply by reading a signal from a test printed inside every container.
The technology can be adapted to detect the most common food pathogens and is also expected to be effective for use with other foods and beverages.
Brain-computer interface technology is advancing rapidly, to the point where consumers could someday soon talk to Amazon Alexa using only their mind. As exciting as this possibility may seem, particularly for people with communication and physical disabilities, it also raises some serious questions about privacy, according to a new whitepaper published by IBM and the Future of Privacy Forum.
The whitepaper titled “Privacy and the Connected Mind: Understanding the Data Flows and Privacy Risks of Brain-Computer Interfaces” explores both the medical benefits and the difficult questions brain-computer interface technology poses around privacy and consumer welfare.
Engineers at Caltech, ETH Zurich, and Harvard are developing an artificial intelligence (AI) that will allow autonomous drones to use ocean currents to aid their navigation, rather than fighting their way through them.
“When we want robots to explore the deep ocean, especially in swarms, it’s almost impossible to control them with a joystick from 20,000 feet away at the surface. We also can’t feed them data about the local ocean currents they need to navigate because we can’t detect them from the surface. Instead, at a certain point we need ocean-borne drones to be able to make decisions about how to move for themselves,” says John O. Dabiri.
a paper by @SciOrestis
, @watkins_welcome
, @aawinecoff
, @klaudiajaz
and Tithi Chattopadhyay, will be presented next week at a @NeurIPSConf
workshop on human-centered AI https://bit.ly/3rMWcqz
Yale plans to establish a new center—the Kline Tower Institute (KTI) for the Foundations of Data Science—to increase dramatically the university’s expertise in and capacity for education and research in the field of data science.
Recently approved by the Yale Corporation, the Institute’s mission is to support and foster the mathematical, algorithmic, and statistical foundations of this emergent field and their applications in multiple domains. The KTI will play a central role in advancing the data science priority outlined by Yale’s University Science Strategy Committee Report by facilitating cross-disciplinary collaboration on campus in a variety of ways: through organizational support and funding for faculty research, training to students and postdoctoral fellows, and by aiding partnerships with leading researchers and practitioners in the field.
Daniel A. Spielman, photo courtesy of the MacArthur FoundationThe KTI will be led by Daniel A. Spielman.
SCALE AI is proud to announce the creation of four new AI research chairs in major Canadian universities. These investments will help secure Canada’s leadership in AI research by attracting and retaining some of the brightest young researchers in the field. They will join the ranks of some of the world’s top experts in artificial intelligence and represent the future of this field. The new chairs are as follows:
The SCALE AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics, at HEC Montréal, held by Professor Carolina Osorio;
The SCALE AI Chair in Data Science for Retail, at McGill University, held by Professor Maxime Cohen;
The SCALE AI Chair in Data-Driven Supply Chains, at Polytechnique Montréal, held by Professor Thibaut Vidal;
The Data-Driven Algorithms for Modern Supply Chains, at the University of Toronto, held by Professor Elias Khalil.
Researchers at Gladstone Institutes have developed a new technology that lets them track thousands of cells at a time and determine the precise moment of death for any cell in the group. The team showed, in a paper published in the journal Nature Communications, that the approach works in rodent and human cells as well as within live zebrafish, and can be used to follow the cells over a period of weeks to months.
“Getting a precise time of death is very important for unraveling cause and effect in neurodegenerative diseases,” says Steve Finkbeiner, MD, PhD, director of the Center for Systems and Therapeutics at Gladstone and senior author of both new studies. “It lets us figure out which factors are directly causing cell death, which are incidental, and which might be coping mechanisms that delay death.”
“ACM FAccT solicits proposals for tutorials to be presented at the 2022 Conference, which will be held both in-person and online. The in-person conference will take place in Seoul, South Korea on June 21-24 2022.” Deadline for proposals is January 28, 2022.
The US and United Kingdom (UK) will collaborate on a series of innovation prize challenges to catalyze research and advancements related to privacy-enhancing technologies (PETs). These technologies give the user greater control over the data being processed to protect personal information and intellectual property. The aim of the prize challenge is to bring together the top minds in both countries to encourage and facilitate the adoption of PETs.
As a large problem area and growing concern among scientists, both countries heavily invested in privacy-enhancing technologies over the past decade. PETs are already used to address a number of societal problems from Covid-19 contact tracing to protecting online banking transactions. This project will build on past contributions, with the White House Office of Science and Technology Policy, the National Science Foundation, and the National Institute of Standards and Technology leading an interagency initiative to develop challenges alongside a team of specialists from the UK.
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The eScience Institute’s Data Science for Social Good program is now accepting applications for student fellows and project leads for the 2021 summer session. Fellows will work with academic researchers, data scientists and public stakeholder groups on data-intensive research projects that will leverage data science approaches to address societal challenges in areas such as public policy, environmental impacts and more. Student applications due 2/15 – learn more and apply here. DSSG is also soliciting project proposals from academic researchers, public agencies, nonprofit entities and industry who are looking for an opportunity to work closely with data science professionals and students on focused, collaborative projects to make better use of their data. Proposal submissions are due 2/22.
My colleagues and I reviewed five types of human guidance to train AIs: evaluation, preference, goals, attention, and demonstrations without action labels [1]. (Fig 3). They don’t replace imitation or reinforcement learning methods, but rather work with them to widen the communication pipeline between humans and learning agents.
