NEWS IN BRIEF: AI/ML FRESH UPDATES

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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.

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.

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.

The Irreplaceable Human Touch in Therapy

Dr. Roman Raczka warns against AI therapy chatbots replacing human support in mental health care, highlighting the importance of genuine human interaction. While AI offers benefits, concerns about data privacy and dependency on technology persist, yet it can provide a valuable 24/7 anonymous space to complement in-person mental health services.

Calculating the Threat of Super Intelligence

AI safety advocate Max Tegmark calls for existential threat assessments before releasing powerful AI systems, drawing parallels to Oppenheimer's calculations before the first nuclear test. Tegmark's research indicates a 90% probability that highly advanced AI could pose a catastrophic risk, emphasizing the importance of safety calculations akin to those conducted before the Trinity test.

Musical Icons Push Starmer on AI Copyright

UK creative industry leaders, including Coldplay and Dua Lipa, urge PM to protect artists' copyright from big tech. Major artists fear livelihoods at risk as AI companies push to use copyright-protected work without permission.

Breaking Down AccentFold: Key Insights on African ASR

African-accented English poses a challenge for ASR systems, but AccentFold offers a unique solution by learning accent embeddings from over 100 African accents. This method helps ASR systems generalize to accents they have never seen before, making it a significant contribution to the field of ML research.

Uncovering the Risks of Misleading Data

The article delves into how statistical misunderstandings can lead to data deception, highlighting the importance of correlation not implying causation. It also emphasizes the significance of remembering base proportions in interpreting data accurately.