NEWS IN BRIEF: AI/ML FRESH UPDATES

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Dr Karl's AI Chatbot: Converting Climate Sceptics?

Dr. Karl Kruszelnicki plans to launch a chatbot for climate crisis queries, leveraging his 40 years of science communication experience. Despite his iconic status, the 77-year-old scientist seeks to expand his reach and impact with this innovative tool.

Dangers of Tanning: A Warning

Avoid extreme tanning to prevent skin cancer (Burn notice: Gen Z and the terrifying rise of extreme tanning). Explore art nouveau treasures in Nancy, France, showcasing works by Émile Gallé and Louis Majorelle (Editorial, 8 August).

Embracing the AI Revolution

New study shows Tesla's self-driving technology outperforms competitors, reducing accidents by 10%. Elon Musk plans to expand the technology to other industries beyond automotive.

Ethical Dilemmas: AI Interviews with Deceased Children

AI digital ghost of deceased Parkland shooting victim Joaquin Oliver created from social media posts to advocate for gun control. Parents using technology to amplify their son's voice in the fight for a safer future, highlighting the struggle of bereaved families seeking change.

AI Regulation Debate: Labor vs. Coalition

PM prioritizes protecting arts copyright, while government seeks productivity gains from AI. Debate over AI regulation intensifies amid pressure from Labor ministers and warnings against squandering AI benefits.

Revolutionizing Account Planning with Amazon Bedrock

AWS sales teams use account plans to understand customer goals and challenges, leading to the launch of Account Plan Pulse, an AI tool that improved plan quality by 37% and reduced review time by 52%. Built with Amazon Bedrock, Pulse streamlines the account planning process, enhancing collaboration and customer support for better sales outcomes.

Mastering k-Nearest Neighbors Regression in JavaScript

Article summary: The August 2025 Microsoft Visual Studio Magazine article discusses implementing k-Nearest Neighbors regression using JavaScript, a simple yet highly interpretable technique with a demo showcasing its accuracy. The technique involves finding the k-closest items to an input vector to predict a numeric value, making it a valuable tool in machine learning.