AI gets a boost via LLNL, SambaNova collaboration

Lawrence Livermore National Laboratory (LLNL) has installed a state-of-the-art artificial intelligence (AI) accelerator from SambaNova Systems, the National Nuclear Security Administration (NNSA) announced today, allowing researchers to more effectively combine AI and machine learning (ML) with complex scientific workloads.

Scientists voice concerns, call for transparency and reproducibility in AI research

In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications.

Assessing State of the Art in AI for Brain Disease Treatment

The range of AI technologies available for dealing with brain disease is growing fast, and exciting new methods are being applied to brain problems as computer scientists gain a deeper understanding of the capabilities of advanced algorithms. In APL Bioengineering, Italian researchers conducted a systematic literature review to understand the state of the art in the use of AI for brain disease. Their qualitative review sheds light on the most interesting corners of AI development.

American College of Radiology and University of Pennsylvania Create Joint Program to Advance Quantitative Imaging Diagnostics and Analytics

The American College of Radiology® (ACR®) Center for Research and Innovation™ (CRI) is pleased to announce a new collaborative effort with the Center for Biomedical Image Computing & Analytics (CBICA) in the Perelman School of Medicine at the University of Pennsylvania (Penn). The collaboration will leverage the ACR’s industry-leading research infrastructure and Penn’s scientific expertise in a joint effort to more rapidly advance imaging informatics.

The Future of Precision Medicine

Precision medicine is a rapidly growing approach to health care that focuses on finding treatments and interventions that work for people based on their genetic makeup, rather than their symptoms.

Zeeshan Ahmed, director of the new Ahmed Lab at Rutgers Institute for Health, Health Care Policy and Aging Research, discusses the future of precision medicine, what needs to be done to successfully analyze the data necessary to develop individualized treatments and the role genetics play during the COVID-19 pandemic.

UCI biochip innovation combines AI and nanoparticle printing for cancer cell analysis

Irvine, Calif., Oct. 7, 2020 – Electrical engineers, computer scientists and biomedical engineers at the University of California, Irvine have created a new lab-on-a-chip that can help study tumor heterogeneity to reduce resistance to cancer therapies. In a paper published today in Advanced Biosystems, the researchers describe how they combined artificial intelligence, microfluidics and nanoparticle inkjet printing in a device that enables the examination and differentiation of cancers and healthy tissues at the single-cell level.

Thomas J. Fuchs, DSc, Named Dean of Artificial Intelligence and Human Health and Co-Director of the Hasso Plattner Institute for Digital Health at Mount Sinai

Appointment Advances Health System’s Role as Leader in AI and Digital Health

Artificial intelligence can predict patients at highest risk for severe pain, increased opioid use after surgery

Artificial intelligence (AI) used in machine learning models can predict which patients are at highest risk for severe pain after surgery, and help determine who would most benefit from personalized pain management plans that use non-opioid alternatives, suggests new research being presented at the ANESTHESIOLOGY® 2020 annual meeting.

Q&A: How machine learning helps scientists hunt for particles, wrangle floppy proteins and speed discovery

At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is opening new avenues to advance the lab’s unique scientific facilities and research.

New Artificial Intelligence Platform Uses Deep Learning to Diagnose Dystonia with High Accuracy in Less Than One Second

Researchers at Mass Eye and Ear have developed a unique diagnostic tool called DystoniaNet that uses artificial intelligence to detect dystonia from MRI scans in 0.36 seconds. DystoniaNet is the first technology of its kind to provide an objective diagnosis of the disorder. In a new study of 612 brain MRI scans, the platform diagnosed dystonia with 98.8 percent accuracy.

Talking Alone: Researchers Use Artificial Intelligence Tools to Predict Loneliness

A team led by researchers at University of California San Diego School of Medicine has used artificial intelligence technologies to analyze natural language patterns to discern degrees of loneliness in older adults.

Active learning accelerates redox-flow battery discovery

In a new study from the U.S. Department of Energy’s Argonne National Laboratory, researchers are accelerating the hunt for the best possible battery components by employing artificial intelligence.

Automatic database creation for materials discovery: Innovation from frustration

A collaboration between the University of Cambridge and Argonne has developed a unique method of generating automatic databases to support specific fields of science using AI and high-performance computing.

Master’s Degree in Artificial Intelligence Now Within Reach of Low-income Students

The accelerated five-year bachelor’s degree in science and master’s degree in AI program is designed to adapt curricular and co-curricular support to enable students to complete their degrees in AI, autonomous systems or machine learning, which are critically important to advance America’s global competitiveness and national security. With this grant, FAU will recruit and train talented and diverse students who are economically disadvantaged and provide them with a unique opportunity to pursue graduate education in a burgeoning field.

FDA Guidance Fails to Ensure Security of 3D-Printed Masks and PPE

New Brunswick, N.J. (Sept. 16, 2020) – FDA guidelines for making 3D-printed masks, face shields and other personal protective equipment (PPE) in the COVID-19 era fail to defend against cyberattacks, according to Rutgers and Georgia Tech engineers. Due to the…

Wichita State joins prestigious national research institute to boost artificial intelligence field

Wichita State University has been named a founding member of a newly formed AI Institute for Foundations of Machine Learning (IFML), established by a $20 million grant from the National Science Foundation.

OU Receives $20 Million Grant to Lead Inaugural National Science Foundation Artificial Intelligence Institute

NSF recently announced an investment of more than $100 million to establish five AI Institutes to support research and education hubs nationwide. Amy McGovern, an OU professor with dual appointments in the School of Computer Science in the Gallogly College of Engineering and in the School of Meteorology in the College of Atmospheric and Geographic Sciences, will lead the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, which received $20 million of the NSF funding.

Scientists use reinforcement learning to train quantum algorithm

Scientists are investigating how to equip quantum computers with artificial intelligence and machine learning approaches.

White House Office of Technology Policy, National Science Foundation and Department of Energy Announce Over $1 Billion in Awards for Artificial Intelligence and Quantum Information Science Research Institutes

Today, the White House Office of Science and Technology Policy, the National Science Foundation (NSF), and the U.S. Department of Energy (DOE) announced over $1 billion in awards for the establishment of 12 new artificial intelligence (AI) and quantum information science (QIS) research institutes nationwide.

Filling in the blanks: How supercomputing can aid high-resolution X-ray imaging

Scientists are preparing for the increased brightness and resolution of next-generation light sources with a computing technique that reduces the need for human calculations to reconstruct images.

3 Awards Will Support Accelerator R&D for Medical Treatment, Miniaturization, and Machine Learning

U.S. Department of Energy awards announced in July will advance Lawrence Berkeley National Laboratory (Berkeley Lab) R&D to develop a more effective and compact particle-beam system for cancer treatment, improve particle-beam performance using artificial intelligence, and develop a high-power, rapid-fire laser system for both tabletop and large-scale applications.

Argonne scientists use artificial intelligence in new way to strengthen power grid resiliency

A new artificial neural network model, created by Argonne scientists, handles both static and dynamic features of a power system with a relatively high degree of accuracy.

LLNL pairs world’s largest computer chip from Cerebras with “Lassen” supercomputer to accelerate AI research

Lawrence Livermore National Laboratory (LLNL) and artificial intelligence computer company Cerebras Systems have integrated the world’s largest computer chip into the National Nuclear Security Administration’s (NNSA’s) Lassen system, upgrading the top-tier supercomputer with cutting-edge AI technology.

AI software enables real-time 3D printing quality assessment

Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.

“Multitasking” AI Tool Extracts Cancer Data in Record Time

Digital cancer registries collect, manage, and store data on cancer patients to help identify trends in diagnoses and treatment. However, cancer pathology reports are complex. To better leverage data, scientists developed an artificial intelligence-based natural language processing tool to help extract information from textual pathology reports.

How Cedars-Sinai Predicts Number of COVID-19 Patients

When the novel coronavirus started spreading across the U.S., hospital leaders were faced with a unique challenge: How could they accurately forecast the number of patients who would need hospitalization when no one knew what to expect from this new disease? To answer this and other questions, the data science team at Cedars-Sinai developed a machine learning platform to predict staffing needs. The team adjusted the platform’s algorithms to forecast data points related to the novel coronavirus. Now the platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for increasing COVID-19 patient volumes with an 85%-95% degree of accuracy.

How Technological, Socioeconomic and Geopolitical Forces are Altering Everything We Know about Marketing

A new study examines technological, socioeconomic and geopolitical forces altering the marketing industry — including deepening consumer relationships — and the implications for marketing managers, educators and researchers.

The University of Chicago is awarded a major federal contract to host a new COVID-19 medical imaging resource center

A new center hosted at the University of Chicago — co-led by the largest medical imaging professional organizations in the country — will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease. The work is supported by a $20 million, two-year federal contract that could be renewable to $50 million over five years.