NEWS IN BRIEF: AI/ML FRESH UPDATES

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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.

Uncooperative ChatGPT: Polite but Stubborn

Big tech companies exploit human language for AI gain, pushing for trust in products as collaborative tools. Author questions portrayal of book with ChatGPT assistance, highlighting caution against using large language models for self-expression.

Defending Amazon Bedrock Agents from Prompt Injections

Amazon Bedrock offers security controls against indirect prompt injections, safeguarding AI interactions. Indirect prompt injections can lead to data exfiltration, misinformation, and system manipulation. Understanding and mitigating these challenges are crucial for maintaining security and trust in AI systems.

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.

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.

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.

Clearing the Mind: Empowering LLMs

Recent large language models like OpenAI's o1/o3 and DeepSeek's R1 use chain-of-thought (CoT) for deep thinking. A new approach, PENCIL, challenges CoT by allowing models to erase thoughts, improving reasoning efficiency.

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.