India is building a database for companies to train AI models: Rajeev Chandrasekhar (Mint)

Mint: India is building a database for companies to train AI models: Rajeev Chandrasekhar. “India is building a large database of anonymized non-personal data for Indian companies and startups that are using artificial intelligence (AI), said Rajeev Chandrasekhar, minister of state (MoS) for Electronics and Information Technology, at the Global Fintech Fest (GFF), an industry event, held in Mumbai on Wednesday.”

University of Alberta: AI researchers improve method for removing gender bias in natural language processing

University of Alberta: AI researchers improve method for removing gender bias in natural language processing. “Researchers have found a better way to reduce gender bias in natural language processing models while preserving vital information about the meanings of words, according to a recent study that could be a key step toward addressing the issue of human biases creeping into artificial intelligence.”

University of Maine: Artificial intelligence can be used to better monitor Maine’s forests, UMaine study finds

University of Maine: Artificial intelligence can be used to better monitor Maine’s forests, UMaine study finds. “Monitoring and measuring forest ecosystems is a complex challenge because of an existing combination of softwares, collection systems and computing environments that require increasing amounts of energy to power. The University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory has developed a novel method of using artificial intelligence and machine learning to make monitoring soil moisture more energy and cost efficient — one that could be used to make measuring more efficient across the broad forest ecosystems of Maine and beyond.”

Argonne National Laboratory: Soaking Up the Sun with Artificial Intelligence

Argonne National Laboratory: Soaking Up the Sun with Artificial Intelligence. “Solar absorbers are a material used to convert this energy into heat or electricity. Maria Chan, a scientist in the U.S. Department of Energy’s (DOE) Argonne National Laboratory, has developed a machine learning method for screening many thousands of compounds as solar absorbers. Her co-author on this project was Arun Mannodi-Kanakkithodi, a former Argonne postdoc who is now an assistant professor at Purdue University.”

Northumbria University: Major New Project To Reveal New Insights Into 19th Century British And Other Immigrant Sailors In The U.S. Navy

Northumbria University: Major New Project To Reveal New Insights Into 19th Century British And Other Immigrant Sailors In The U.s. Navy. “The ‘Civil War Bluejackets’ Project—so named because of the distinctive uniform worn by U.S. Civil War sailors—is a collaboration between historians at Northumbria University, Newcastle, and computer scientists at the University of Sheffield and the University of Koblenz-Landau. Funded by the UK Arts and Humanities Research Council, the project launches on 6 September 2022 with a call for citizen volunteers to help transcribe tens of thousands of Civil War “Muster Rolls”, documents that were carried on board U.S. ships and which capture the personal details of the c.118,000 men who fought on water for the Union between 1861 and 1865.”

New York Times: The Animal Translators

New York Times: The Animal Translators. “Machine-learning systems, which use algorithms to detect patterns in large collections of data, have excelled at analyzing human language, giving rise to voice assistants that recognize speech, transcription software that converts speech to text and digital tools that translate between human languages. In recent years, scientists have begun deploying this technology to decode animal communication, using machine-learning algorithms to identify when squeaking mice are stressed or why fruit bats are shouting.”

Nature: A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification

Nature: A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. “We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem of instance classification for large archaeological images datasets, i.e. where each individual object (instance) is itself a class that all of the multiple images of that object belongs.”

Seeing the light: researchers develop new AI system using light to learn associatively (University of Oxford)

University of Oxford: Seeing the light: researchers develop new AI system using light to learn associatively . “Researchers at Oxford University’s Department of Materials, working in collaboration with colleagues from Exeter and Munster have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.”

University of Michigan: Open source platform enables research on privacy-preserving machine learning

University of Michigan: Open source platform enables research on privacy-preserving machine learning. “The biggest benchmarking data set to date for a machine learning technique designed with data privacy in mind has been released open source by researchers at the University of Michigan. Called federated learning, the approach trains learning models on end-user devices, like smartphones and laptops, rather than requiring the transfer of private data to central servers.”

National Science Foundation: Citizen science project analyzes data to model treetop snowpack and predict melt

National Science Foundation: Citizen science project analyzes data to model treetop snowpack and predict melt. “Thousands of volunteers categorized 13,600 images from remote U.S. locations into images that showed snow on tree branches, images that didn’t, and images that were inconclusive. In the future, the dataset could be used to train machine learning in analyzing the images.”

MIT Sloan School of Management: The promise of edge computing comes down to data

MIT Sloan School of Management: The promise of edge computing comes down to data. “Cloud adoption has rocketed as companies seek computing and storage resources that can be scaled up and down in response to changing business needs. But even given the cost and agility upsides to cloud, there’s rising interest in yet another deployment model — edge computing, which is computing that’s done at or near the source of the data. It can empower new use cases, especially the innovative artificial intelligence and machine learning applications that are critical to modern business success.”

TechRadar Pro: Why this chess grandmaster left Google behind

TechRadar Pro: Why this chess grandmaster left Google behind. “When [Tal] Shaked arrived at Google in 2004, the company had just 3,000 employees and looked nothing like the sprawling megacorporation it is today. He was brought on as a junior engineer to work on Google Search. At the time, Google’s search rankings were not powered by any form of intelligence. Instead, a dedicated team of engineers was tasked with managing a complex rule-based system designed to serve up the best and most relevant results to users.”