NEWS IN BRIEF: AI/ML FRESH UPDATES

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Embracing AI: The Future of Art

Tate Modern's Electric Dreams exhibition explores artists embracing AI as opportunity, not threat, showcasing their longstanding relationship with technology. Director Catherine Wood highlights the symbiosis between art and technology, emphasizing their enduring connection.

Call for Investigation: NDAs at OpenAI

OpenAI whistleblowers seek investigation into restrictive contracts requiring permission to contact regulators, potentially stifling concerns about the company. Non-disclosure agreements at OpenAI under scrutiny for potential repercussions on employees raising issues with federal authorities.

The Rise of Autonomous Weapons

AI-enabled weapons are on the rise in military use, with companies like Elbit Systems leading the way in developing lethal autonomous drones. The industry is booming as defense companies showcase their advancements in AI technology for combat purposes.

The Influence of Conspiracy Theories on Politics

Renée DiResta, former Stanford Internet Observatory manager, delves into online propaganda in her new book. She highlights the evolution of propaganda and its impact on society, emphasizing the need for a more accurate diagnosis of the issue.

Unlocking the Secrets of Time Series for LLMs

Foundation models, like Large Language Models (LLMs), are being adapted for time series modeling through Large Time Series Foundation Models (LTSM). By leveraging sequential data similarities, LTSM aims to learn from diverse time series data for tasks like outlier detection and classification, building on the success of LLMs in computational linguistic domains.

Streamlining Model Customization in Amazon Bedrock

Amazon Bedrock offers customizable large language models from top AI companies, allowing enterprises to tailor responses to unique data. AWS Step Functions streamline model customization workflows, reducing development timelines for optimal results.

AI Trustworthiness: A Guide

MIT researchers introduce new approach to improve uncertainty estimates in machine-learning models, providing more accurate and efficient results. The scalable technique, IF-COMP, helps users determine when to trust model predictions, especially in high-stakes scenarios like healthcare.