🔥 Enhancing reality while facing scrutiny
AI innovation for good persists
🐾 IN TODAY'S WILD
AI continues its rapid evolution, now with UNIST's BF-STVSR model transforming blurry videos into clear, seamless footage.
Amidst these advancements, the debate around AI's societal impact sharpens: claims about generative AI's election influence may be overblown, and calls for regulating large AI developers, not just specific models, are growing.
Underlying this progress are significant resource demands, with AI's energy and water consumption becoming an urgent global concern. While LLMs excel in information retrieval, their high computational needs remain a hurdle.
Still, AI innovation for good persists: Apple's latest study aims to unlock street navigation for blind users. As C-suite leaders define AI's role in customer experience, the generative AI tech stack continues to advance.
🦾 AI daily pulse
A research team, led by Professor Jaejun Yoo from the Graduate School of Artificial Intelligence at UNIST has announced the development of an advanced artificial intelligence (AI) model, "BF-STVSR (Bidirectional Flow-based Spatio-Temporal Video Super-Resolution)," capable of simultaneously improving both video resolution and frame rate. AI model transforms blurry, choppy videos into clear, seamless footage. [LINK]
Don’t panic (yet): Assessing the evidence and discourse around generative AI and elections. Why claims about the impact of generative AI on elections have been overblown. [LINK]
Frontier AI regulatory statutes should cover large AI developers, not particular AI models or their uses. Entity-Based Regulation in Frontier AI Governance. [LINK]
⚡️ Top trends
As demand for increasingly powerful artificial intelligence (AI) models surges, so does the need for energy, water, and hardware. This has resulted in a dual narrative: while AI can significantly contribute to solving climate challenges, it also consumes vast resources that may worsen them if left unchecked. Understanding and addressing this balance is now an urgent global concern. Artificial intelligence's impact on the environment: latest research. [LINK]
LLMs have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment. Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. [LINK]
💻 Top techies
Apple’s newest AI study unlocks street navigation for blind users. [LINK]
The generative AI tech stack [LINK]
🔮 What else
C-suite leaders have a vision for where generative AI fits in CX. [LINK]
MIT researchers studying impacts of generative AI:
