A selection of the most important recent news, articles, and papers about AI.
General News, Articles, and Analyses
Video game actors’ strike: 80 games adopt SAG-AFTRA AI terms, union says – Los Angeles Times
Author: Christi Carras
(Friday, September 6, 2024) “SAG-AFTRA says 80 video games have agreed to abide by its proposed AI terms amid the ongoing video game actors’ strike.”
Roblox is launching a generative AI that builds 3D environments in a snap | MIT Technology Review
Author: Scott J Mulligan
(Friday, September 6, 2024) “It will make it easy to build new game environments on the platform, even if you don’t have any design skills.”
The Top Use Cases for Gen AI In Your Banking Contact Center
Author: Garret Reich
(Friday, September 6, 2024) “Gen AI is reshaping contact centers, promising enhanced customer experiences. But are banks ready for this seismic shift in customer service?”
Meta Llama: Everything you need to know about the open generative AI model | TechCrunch
Author: Kyle Wiggers
(Sunday, September 8, 2024) “Here’s everything you need to know about Llama, from its capabilities and editions to where you can use it. We’ll keep this post updated as Meta releases upgrades and introduces new dev tools to support the model’s use.”
Axios Exclusive: Employees at top AI firms back California’s AI bill
https://www.axios.com/2024/09/09/openai-anthropic-deepmind-employees-ai-bill
Author: Megan Morrone
(Monday, September 9, 2024) “Around 120 current and former employees of OpenAI, Anthropic, Google’s DeepMind and Meta have issued a statement in support of California’s new AI regulation bill.”
Science & Tech Spotlight: Generative AI in Health Care | U.S. GAO
https://www.gao.gov/products/gao-24-107634
Author: U.S. Government Accountability Office
(Monday, September 9, 2024) “The health care sector faces many challenges, such as high costs, long timelines for drug development, and provider burnout. Generative artificial intelligence (AI) is an emerging tool that may help address these and other challenges.”
Google Gemini: Everything you need to know about the generative AI models | TechCrunch
https://techcrunch.com/2024/09/10/what-is-google-gemini-ai/
Author: Kyle Wiggers
(Tuesday, September 10, 2024) “To make it easier to keep up with the latest Gemini developments, we’ve put together this handy guide, which we’ll keep updated as new Gemini models, features, and news about Google’s plans for Gemini are released.”
Mistral releases its first multimodal AI model: Pixtral 12B | VentureBeat
https://venturebeat.com/ai/pixtral-12b-is-here-mistral-releases-its-first-ever-multimodal-ai-model/
Author: Shubham Sharma
(Wednesday, September 11, 2024) “Mistral AI is finally venturing into the multimodal arena. Today, the French AI startup taking on the likes of OpenAI and Anthropic released Pixtral 12B, its first ever multimodal model with both language and vision processing capabilities baked in.”
Technical Papers, Articles, and Preprints
[2409.01666] In Defense of RAG in the Era of Long-Context Language Models
https://arxiv.org/abs/2409.01666
Authors: Yu, Tan; Xu, Anbang; and Akkiraju, Rama
(Tuesday, September 3, 2024) “Overcoming the limited context limitations in early-generation LLMs, retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the past. Recently, the emergence of long-context LLMs allows the models to incorporate much longer text sequences, making RAG less attractive. Recent studies show that long-context LLMs significantly outperform RAG in long-context applications. Unlike the existing works favoring the long-context LLM over RAG, we argue that the extremely long context in LLMs suffers from a diminished focus on relevant information and leads to potential degradation in answer quality. This paper revisits the RAG in long-context answer generation. We propose an order-preserve retrieval-augmented generation (OP-RAG) mechanism, which significantly improves the performance of RAG for long-context question-answer applications. With OP-RAG, as the number of retrieved chunks increases, the answer quality initially rises, and then declines, forming an inverted U-shaped curve. There exist sweet points where OP-RAG could achieve higher answer quality with much less tokens than long-context LLM taking the whole context as input. Extensive experiments on public benchmark demonstrate the superiority of our OP-RAG.”
