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10 Bits: The Data News Hotlist

by Martin Makaryan

This week’s list of top data news highlights covers May 18, 2024 to May 24, 2024 and includes articles on tracking Russian missile launchers in Ukraine and using data to predict risks of developing cancer.

1. Enhancing Laser Experiments

An international team of scientists from the United States, Germany, and the Czech Republic is using machine learning to optimize high-power lasers. The researchers trained these models on information about how laser beams affect the materials they target, allowing the systems to make real-time adjustments to the laser pulses during experiments, which reduces the need for manual intervention. The machine learning model continuously fine-tunes the laser’s pulse shape to improve performance.

2. Creating TikTok Ads

TikTok has launched a new suite of tools called “TikTok Symphony” that brands can use to create more engaging advertisements on the platform. The suite includes an AI assistant chatbot that helps write scripts and a “creative studio” that uses generative AI to create TikTok videos from text prompts.

3. Building AI-Powered Laptops

Microsoft is integrating AI into all major Windows laptops. New laptops will offer built-in features like  Microsoft’s AI assistant tool Copilot and a new tool called “Recall,” which allows a user to input a text prompt to search and bring up a snapshot of their screen from a given time.

4. Tracking Russian Missile Launchers

Researchers at the Royal Melbourne Institute of Technology in Australia analyzed social media sources and data from Sentinel-1, the European Space Agency’s scientific satellite, to reveal closely guarded locations of Russian mobile missile launchers in occupied parts of Ukraine.

5. Assisting College Students

A Minnesota-based nonprofit called North Star Prosperity has developed a chatbot called Wyatt that uses AI to provide support to college students applying for federal student aid. The new tool answers questions such as which types of aid students are eligible for, sends reminder alerts about deadlines, and gives application tips to avoid delays or other application issues.

6. Developing Rural Tourism

UN Tourism, a United Nations agency promoting sustainable tourism globally, has launched a new digital tool called STAR that allows local governments in rural areas to assess their tourism strategies and receive customized advice from the agency on how to improve. Users answer a set of questions on STAR, which then uses these responses to generate an individual action plan aligned with the agency’s standards to boost rural tourism. The tool also uses these responses to generate data insights for UN member states to evaluate their rural tourism policies.

7. Responding to Wildfires

The National Aeronautics and Space Administration (NASA) has created a wildfire digital twin to help firefighters and disaster observers more accurately monitor wildfires and predict harmful air pollution. The tool uses machine learning and AI to analyze data from air and space sensors, as well situational reports from disaster zones, to forecast wildfire spread in real-time.

8. Delivering Medication to Rural Areas

Canadian-based Script Runner, a company that provides last-mile solutions to pharmacies and hospitals, has launched a pharmacy delivery service that uses autonomous drones to service remote and rural areas in Canada. During a successful test, the autonomous drone delivered an elderly patient’s insulin prescription to a rural town with poor road conditions.

9. Assisting Surgeons

Sony has developed a robot to assist doctors performing delicate microscopic surgeries like operating on nerves and blood vessels. The robot stores miniature surgical instruments near its arm and automatically exchanges them during a surgery. It uses a control device for high-precision movements that researchers designed by replicating delicate movements of surgeons’ fingertips.

10. Predicting Cancer Risk

Scientists at the European Molecular Biology Laboratory and the German Cancer Research Center have developed a predictive model that can help identify an individual’s risk of developing one of 20 most common types of cancer, enabling early detection and treatment. Scientists trained the model on data from the Danish health register, which included the health records, family history, and lifestyle details of 6.7 million adults collected between 1995 and 2004.

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