NEWS IN BRIEF: AI/ML FRESH UPDATES

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Reimagining AI Training Costs: Beyond Hardware

Hardware choices and training time impact energy, water, and carbon footprints during AI model training. Longer training time can decrease energy efficiency by 0.03% per hour, highlighting environmental costs of AI adoption.

AI Voices Revolutionize Audiobooks

Audible, an Amazon brand, will introduce over 100 AI-generated voices for audiobooks in multiple languages. AI technology will be used for narration, with translation capabilities to come, offered to select publishers.

AI tool revolutionizes public consultations in UK

AI tool Consult will analyze responses 1,000x faster than humans, saving time & money for the Scottish government. The system promises to revolutionize public consultations by efficiently processing feedback on non-surgical cosmetic procedures.

MIT's New Center on Inequality & Future of Work

MIT's Shaping the Future of Work Initiative evolves into the James M. and Cathleen D. Stone Center on Inequality, focusing on wealth distribution and technology's impact on the workforce. Led by prominent scholars, the center aims to advance research, inform policymakers, and engage the public on critical economic issues.

Discover Your True Age with FaceAge AI

New technology FaceAge.Age uses selfies to scientifically assess aging, promising a simple way to track changes over time. This innovative approach could revolutionize how we monitor our aging process.

Cracking the Code: Mastering MCP in Action

Model Context Protocol (MCP) is essential for integrating custom tools with Claude Desktop, providing a centralized way to manage tools across multiple interfaces. Compared to traditional methods like RAG, MCP allows for seamless integration without the need to build a custom server from scratch.

House of Lords challenges government on AI

House of Lords backs amendment to data bill, forcing AI companies to disclose use of copyrighted material, against government wishes. Peers demand transparency in AI models, a blow to government's plans for copyright protection.

PSO-Powered Linear SVR in C#

Training linear SVR is challenging due to its non-calculus differentiable loss function, leading to the exploration of PSO over evolutionary algorithms. Using PSO for linear SVR training yielded superior results, showcasing the importance of parameter tuning for optimizing predictive models.