A selection of the most important recent news, articles, and papers about AI.
General News, Articles, and Analyses
GenAI and Its Implications on the Legal Field | Vanderbilt University
https://law.vanderbilt.edu/genai-and-its-implications-on-the-legal-field/
(Friday, October 11, 2024) “As developers continue to expand the capabilities of generative AI, lawyers have begun to question how GenAI could impact litigation in the future. To address these questions, Vanderbilt Law School’s Branstetter Litigation and Dispute Resolution Program and the Vanderbilt AI Law Lab hosted a discussion on GenAI and its implications on the legal field. The presentation was moderated by Professor Brian Fitzpatrick and featured Robert Keeling, the head of eDiscovery and Data Analytics at Sidley Austin.”
Generative AI is rapidly evolving: How governments can keep pace | World Economic Forum
https://www.weforum.org/agenda/2024/10/generative-ai-governments-keep-pace/
Authors: Karla Yee Amezaga; Rafi Lazerson; and Manal Siddiqui
(Friday, October 11, 2024) “The economic potential, transformative impact and rapid adoption of GenAI have led governments at all levels to invest in examining how to secure AI innovation in their jurisdictions while mitigating the technology’s risks and preventing harm.”
GenAI is coming for commercial lending. Here’s what to do.
https://www.globalbankingandfinance.com/genai-is-coming-for-commercial-lending-heres-what-to-do/
Author: Kris Kowal
(Saturday, October 12, 2024) “Origination, relationship management, underwriting, loan servicing, portfolio monitoring, risk management, compliance, innovation and product development: That’s commercial lending. The question isn’t if, but rather when, generative AI touches every step of the commercial lending value chain. Commercial banks should be focusing on that “when.” That’s because, even if the GenAI tools were universally mature, secure, and reliable, AI talent limitless, and computing cycles gratis, no bank could manage a wholesale GenAI transition all at once.”
Accelerating chemicals and agriculture with gen AI | McKinsey
(Monday, October 14, 2024) “Industry players are standing up “win rooms” to mobilize frontline sales and subsequently modernize their operating models to capture the full benefits of generative AI.”
AI Chipsets
Game-Changer: How the World’s First GPU Leveled Up Gaming and Ignited the AI Era
https://blogs.nvidia.com/blog/first-gpu-gaming-ai/
Author: John Fenno
(Friday, October 11, 2024) “The release of NVIDIA’s GeForce 256 twenty-five years ago today, overlooked by all but hardcore PC gamers and tech enthusiasts at the time, would go on to lay the foundation for today’s generative AI. The GeForce 256 wasn’t just another graphics card — it was introduced as the world’s first GPU, setting the stage for future advancements in both gaming and computing.”
AI in Games
AI in games might’ve just proven itself useful for a change—Activision claims Call of Duty’s seen a 43% drop in ‘disruptive voice chat’ since the start of the year thanks to its robo-snitch | PC Gamer
Author: Harvey Randall
(Friday, October 11, 2024) “The AI software in question is ToxMod, which Activision announced it’d be including in Call of Duty August of last year. This software essentially acts as a goodie-two shoes narc that’s always watching your games—it’s not responsible for banning anybody, but it will listen to and report your flaming for moderation, hopefully, by a team of people able to make up for the fact that AI has a habit of imagining things.”
Technical Papers, Articles, and Preprints
[2410.08345] Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations
https://arxiv.org/abs/2410.08345
Authors: Gasztowtt, Henry; Smith, Benjamin; Zhu, Vincent; Bai, Qinxun; and Zhang, Edwin
(Thursday, October 10, 2024) “The improvement of economic policymaking presents an opportunity for broad societal benefit, a notion that has inspired research towards AI-driven policymaking tools. AI policymaking holds the potential to surpass human performance through the ability to process data quickly at scale. However, existing RL-based methods exhibit sample inefficiency, and are further limited by an inability to flexibly incorporate nuanced information into their decision-making processes. Thus, we propose a novel method in which we instead utilize pre-trained Large Language Models (LLMs), as sample-efficient policymakers in socially complex multi-agent reinforcement learning (MARL) scenarios. We demonstrate significant efficiency gains, outperforming existing methods across three environments. Our code is available at https://github.com/hegasz/large-legislative-models.”
