This blog post discusses strategies for staying up-to-date on the rapidly evolving field of AI, covering resources, tools, and techniques for tracking news, research, and developments.
In this paper, the authors discuss the challenges faced in developing the knowledge stack for the Companion cognitive architecture and share the tools, representations, and practices they have developed to overcome these challenges. They also outline potential next steps to allow Companion agents to manage their own knowledge more effectively.
A proposed experiment using a quantum-mechanical AI to test the Wigner’s Friend thought experiment could have profound implications for our understanding of quantum reality and the nature of observation.
Explores the dynamic relationship between language, cognition, and the role of Large Language Models (LLMs) in expanding our understanding of the functional significance of language.
Abner Li discusses Pixie, a new AI assistant exclusive to Google's Pixel devices. The article highlights the capabilities of Pixie, which uses data from Google products to evolve and provide a more personalized experience compared to Siri. It also compares Pixie to Google Assistant's previous iteration and Apple's Siri.
An article by Phillip Morrison exploring the concept of machine thinking in the age of computers. The author discusses the recurring question of why life is different from non-life, with each new generation of machines that simulate aspects of life.
Explore the innovative world of AI gardens and how artificial intelligence is transforming the way we cultivate plants. Discover the benefits, role of AI in gardening, case studies, and the future of AI technology in gardening.
The increasing use of autonomous and AI-enabled systems relies on optical and radio frequency sensors. These systems face growing vulnerabilities from directed-energy laser and microwave weapons, which can disrupt or damage their sensors and electronics.
The paper proposes a two-phase framework called TnT-LLM to automate the process of end-to-end label generation and assignment for text mining using large language models, where LLMs produce and refine a label taxonomy iteratively using a zero-shot, multi-stage reasoning approach, and are used as data labelers to yield training samples for lightweight supervised classifiers. The framework is applied to the analysis of user intent and conversational domain for Bing Copilot, achieving accurate and relevant label taxonomies and a favorable balance between accuracy and efficiency for classification at scale.
