The Ohio State University: The deadly impact of urban streets that look like highways

The Ohio State University: The deadly impact of urban streets that look like highways. “Serious auto crashes in urban areas are more likely on city streets that look to drivers like highways, new research suggests. The study used a novel approach: Ohio State University researchers applied machine learning techniques to analyze more than 240,000 images of road segments in Columbus, Ohio, taken from Google Street View. The goal was to see what the roads looked like to drivers and whether that was linked to serious and deadly crashes.”

Analytics India: Google Lens’ AI Factor

Analytics India: Google Lens’ AI Factor. “Google Lens was launched by CEO Sundar Pichai at the Google developer conference in 2017. This announcement was part of the ‘AI first’ strategy, which was also announced at this conference. Pichai had then called it the key reflection of Google’s direction, highlighting it as an example of Google being at an ‘inflection point with vision’. He said, ‘All of Google was built because we started understanding text and web pages. So the fact that computers can understand images and videos has profound implications for our core mission’. In this article, we list out major AI breakthroughs that have been responsible for making Google Lens an efficient tool.”

Lanfrica: A database for African languages developed by a student of Jacobs University (EurekAlert)

EurekAlert: Lanfrica: A database for African languages developed by a student of Jacobs University. “‘We want to improve the visibility and representation of African languages on the Internet,’ explained Bonaventure [Dossou]. Discoverability is limited not only because English dominates machine learning technologies, and language assistants from Google or Apple barely support African languages. But also because many African languages are not written languages. Often, only a few texts and sources exist as a data basis for NLP technologies (Natural Language Processing) such as machine translation. Lanfrica is intended to remedy this situation. It sees itself as a catalog, a research tool that provides easy and clear access to existing research, data packages or archives. And it aims to bring together existing initiatives dealing with the machine readability of African languages.”

University of Maryland: Researchers Work to Make Artificial Intelligence Genuinely Fair

University of Maryland: Researchers Work to Make Artificial Intelligence Genuinely Fair. “Artificial intelligence (AI) algorithms help make online shopping seamless, calculate credit scores, navigate vehicles and even offer judges criminal sentencing guidelines. But as the use of AI increases exponentially, so does the concern that biased data can result in flawed decisions or prejudiced outcomes. Now, backed by a combined $1.6 million in funding from the National Science Foundation (NSF) and Amazon, two teams of University of Maryland researchers are working to eliminate those biases by developing new algorithms and protocols that can improve the efficiency, reliability and trustworthiness of AI systems.”

WIRED: The Census Is Broken. Can AI Fix It?

WIRED: The Census Is Broken. Can AI Fix It?. “The once-a-decade endeavor informs the distribution of federal tax dollars and apportions members of the House of Representatives for each state, potentially redrawing the political map. According to emails obtained through a records request, Trump administration officials interfered in the population count to produce outcomes beneficial to Republicans, but problems with the census go back much further.”

The National Academies: Ensuring Human Control over AI-Infused Systems

The National Academies: Ensuring Human Control over AI-Infused Systems. “Human control over technology was a concern thousands of years ago when early humans sought to ensure safe use of fire. Later, control over horse-drawn wagons and eventually steam engines led to debates about how to make the most of their benefits while limiting dangers. Now questions of control are central in the design of AI-infused technologies, for which some advocates envision full machine autonomy while others promote human autonomy (Shneiderman 2020).”

Daily Beast: We’re a Big Step Closer to Full Color Night Vision

Daily Beast: We’re a Big Step Closer to Full Color Night Vision. “In a new study published Wednesday in the journal PLOS One, researchers at the University of California, Irvine used machine learning to transform what you see through a night vision scope or camera into a veritable rainbow of colors. This game-changing development could benefit not just the military, but also medical technologies, healthcare, and even more niche tasks like art restoration.”

The Conversation: Charities are contributing to growing mistrust of mental-health text support — here’s why

The Conversation: Charities are contributing to growing mistrust of mental-health text support — here’s why. “Like many areas of society, mental healthcare has changed drastically as a result of the pandemic. Forced to adapt to a growing demand for counselling and crisis services, mental health charities have had to quickly increase their digital services to meet the needs of their users…. Recently, two charities faced a public backlash as a result of how they used machine learning and handled data from users who contacted their mental health support services at a point of crisis.”

TheMayor: Dublin to train AI to transcribe 19-century historic records

TheMayor: Dublin to train AI to transcribe 19-century historic records. “Today, local authorities in Dublin announced the new ‘Transcription Week’ event, which will take place between 28 March and 1 April. During the event, hundreds of volunteers will transcribe 18- and 19-century municipal documents that will later be made available to the public…. Furthermore, the work from the volunteers will be used to train an Artificial Intelligence programme, that will be used to transcribe even more documents in the future.”

Ars Technica: Take a peek inside a flickering candle flame with these 3D-printed shapes

Ars Technica: Take a peek inside a flickering candle flame with these 3D-printed shapes. “Markus Buehler and his postdoc, Mario Milazzo, combined high-resolution imaging with deep machine learning to sonify a single candle flame. They then used that single flame as a basic building block, creating ‘music’ out of its flickering dynamics and designing novel structures that could be 3D-printed into physical objects.”

VentureBeat: Language models that can search the web hold promise — but also raise concerns

VentureBeat: Language models that can search the web hold promise — but also raise concerns. “In a paper published early this month, researchers at DeepMind, the AI lab backed by Google parent company Alphabet, describe a language model that answers questions by using Google Search to find a top list of relevant, recent webpages. After condensing down the first 20 webpages into six-sentence paragraphs, the model selects the 50 paragraphs most likely to contain high-quality information; generates four ‘candidate’ answers for each of the 50 paragraphs (for a total of 200 answers); and determines the ‘best’ answer using an algorithm.”

The Conversation: How AI helped deliver cash aid to many of the poorest people in Togo

The Conversation: How AI helped deliver cash aid to many of the poorest people in Togo. “The simple idea behind this approach, as we explained in the journal Nature on March 16, 2022, is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms – which are fancy tools for pattern recognition – can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor.”

Tech Xplore: A new model to automatically detect and filter spam emails

Tech Xplore: A new model to automatically detect and filter spam emails. “Over the past few years, computer scientists have developed increasingly advanced computational models to automatically detect spam emails. To perform well, however, most of these models need to be trained on large email datasets, which were manually labeled by humans. Researchers at Sinhgad Institute of Technology Lonavala in India have recently created a new technique for the automatic detection of spam emails.”