Goto

Collaborating Authors

Suffolk County


'Scary and chilling': AI surveillance takes U.S. prisons by storm

The Japan Times

When the sheriff in Suffolk County, New York, requested $700,000 from the U.S. government for an artificial intelligence system to eavesdrop on prison phone conversations, his office called it a key tool in fighting gang-related and violent crime. But the county jail ended up listening to calls involving a much wider range of subjects -- scanning as many as 600,000 minutes per month, according to public records from the county obtained by the Thomson Reuters Foundation. Beginning in 2019, Suffolk County was an early pilot site for the Verus AI-scanning system sold by California-based LEO Technologies, which uses Amazon speech-to-text technology to transcribe phone calls flagged by keyword searches. The company and law enforcement officials say it is a crucial tool to keep prisons and jails safe, and to fight crime, but critics say such systems trample the privacy rights of prisoners and other people, like family members, on the outside. "T he ability to surveil and listen at scale in this rapid way -- it is incredibly scary and chilling," said Julie Mao, deputy director at Just Futures Law, an immigration legal group.


Facebook wants machines to see the world through our eyes

#artificialintelligence

For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video--specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. "Machines will be able to help us in our daily lives only if they really understand the world through our eyes," says Kristen Grauman at FAIR, who leads the project. Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. "The video in this data set is much closer to how humans observe the world," says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.


Creating a More Resilient Energy Grid Through Artificial Intelligence

#artificialintelligence

Stony Brook University professor Peng Zhang, a SUNY Empire Innovation professor in the Department of Electrical and Computer Engineering, is leading a statewide team of collaborators in developing "AI-Grid," an artificial intelligence-enabled, autonomous grid designed to keep power infrastructure resilient from cyberattacks, faults and disastrous accidents. The work is part of the National Science Foundation's (NSF) Convergence Accelerator Program, which supports and builds upon basic research and discovery that involves multidisciplinary work to accelerate solutions toward societal impact. In September 2020, the program launched the 2020 cohort, which included AI-Grid as a phase 1 awardee and grant funding of a $1 million to further AI-Grid research from an idea to a low-fidelity prototype. The Convergence Accelerator recently selected teams for phase 2, to focus on expanding the solution prototype and to build a sustainability plan beyond the NSF funding. Under phase 2, a new $5


Artificial Intelligence Can Help Doctors Manage COVID-19

#artificialintelligence

Chest x-rays used in the COVID-Net study show differing infection extent and opacity in the lungs of COVID-19 patients. Artificial intelligence (AI) technology developed by researchers at the University of Waterloo is capable of assessing the severity of COVID-19 cases with a promising degree of accuracy. A study, which is part of the COVID-Net open-source initiative launched more than a year ago, involved researchers from Waterloo and spin-off start-up company DarwinAI, as well as radiologists at the Stony Brook School of Medicine and the Montefiore Medical Center in New York. Deep-learning AI was trained to analyze the extent and opacity of infection in the lungs of COVID-19 patients based on chest x-rays. Its scores were then compared to assessments of the same x-rays by expert radiologists.


Seven Stony Brook AI Papers Make Prestigious NeurIPS Conference

#artificialintelligence

As a global cornerstone of the artificial intelligence community, NeurIPS … "NeurIPS is the premier conference in machine learning, with prominence …


Enabling fairer data clusters for machine learning

#artificialintelligence

Research published recently by CSE investigators can make training machine learning (ML) models fairer and faster. With a tool called AlloX, Prof. Mosharaf Chowdhury and a team from Stony Brook University developed a new way to fairly schedule high volumes of ML jobs in data centers that make use of multiple different types of computing hardware, like CPUs, GPUs, and specialized accelerators. As these so-called heterogeneous clusters grow to be the norm, fair scheduling systems like AlloX will become essential to their efficient operation. This project is a new step for Chowdhury's lab, which has recently published a number of tools aimed at speeding up the process of training and testing ML models. Their past projects Tiresias and Salus sped up GPU resource sharing at multiple scales: both within a single GPU (Salus) and across many GPUs in a cluster (Tiresias).


Enabling fairer data clusters for machine learning

#artificialintelligence

Research published recently by CSE investigators can make training machine learning (ML) models fairer and faster. With a tool called AlloX, Prof. Mosharaf Chowdhury and a team from Stony Brook University developed a new way to fairly schedule high volumes of ML jobs in data centers that make use of multiple different types of computing hardware, like CPUs, GPUs, and specialized accelerators. As these so-called heterogeneous clusters grow to be the norm, fair scheduling systems like AlloX will become essential to their efficient operation. This project is a new step for Chowdhury's lab, which has recently published a number of tools aimed at speeding up the process of training and testing ML models. Their past projects Tiresias and Salus sped up GPU resource sharing at multiple scales: both within a single GPU (Salus) and across many GPUs in a cluster (Tiresias).


Machine Learning Can Identify Areas Most at Risk from Pandemic

#artificialintelligence

Areas most at risk from the COVID-19 pandemic can be identified by a new machine learning tool developed by researchers at startup company Akai Kaeru LLC, which is affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science. The software they use analyzes a massive data set from all 3,007 U.S. counties. The researchers found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties. After analyzing and assessing the data within counties they created nearly 300 sets of counties at a high risk for COVID-19 and related death rates. Many of these counties within the


New machine learning tool identifies US counties at higher risk for COVID deaths

#artificialintelligence

The task of controlling the COVID-19 pandemic nationwide and predicting where cases will spike next and which areas may have high mortality rates remains daunting for scientists and public officials. A new machine learning tool developed by researchers at a startup company (Akai Kaeru LLC) affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science (IACS) may help gauge areas most at risk for the virus and high death rates. The software they use analyzes a massive data set from all 3,007 U.S. counties. They found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties.


LI artificial intelligence startup predicts where COVID-19 will spike – IAM Network

#artificialintelligence

A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly. In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller. Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk. "These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said. Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professor on leave from Stony Brook University.