A selection of the most important recent news, articles, and papers about AI.
General News, Articles, and Analyses
High-Level Explanations: Agentic AI Deep-Dive
https://deepgram.com/learn/agentic-ai-explained
Author: Tife Sanusi
(Friday, September 13, 2024) “Agentic AI is the next step in AI’s evolution. The new technology is being actively sought after with organizations from NASA’s Jet Propulsion Laboratory, the arm of NASA that coordinates robotic space exploration to Hughes Network Systems, a satellite communications and service provider, building and using agentic AI systems for multiple applications. Agentic AI’s ability to act autonomously and be proactive has given them a level of efficiency and reliability that can not be gotten from other AI systems.”
Introducing OpenAI o1-preview
https://openai.com/index/introducing-openai-o1-preview/
(Tuesday, September 17, 2024) “We’ve developed a new series of AI models designed to spend more time thinking before they respond. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math. Today, we are releasing the first of this series in ChatGPT and our API. This is a preview and we expect regular updates and improvements. Alongside this release, we’re also including evaluations for the next update, currently in development.”
Amazon is stuffing generative AI into its shopping experience – The Verge
Author: Jess Weatherbed
(Thursday, September 19, 2024) “Amazon has introduced a batch of new generative AI tools that aim to improve the retail experience for both customers and sellers on the platform. One of the more notable features announced at the Amazon Accelerate event on Thursday will use customers’ preferences, search, browsing, and purchase history to create personalized product recommendations on Amazon’s homepage.”
‘Hunger Games’ studio Lionsgate announces AI video deal
https://www.bbc.com/news/articles/cp8l3mr5d17o
Author: Katharine Sharpe
(Thursday, September 19, 2024) “Entertainment giants Lionsgate are partnering with artificial intelligence (AI) company Runway to allow a new AI model to be trained on their extensive film and TV archive.”
AI Deepfakes in Political Ads Banned in California
https://aibusiness.com/responsible-ai/ai-deepfakes-in-political-ads-banned-in-california
Author: Liz Hughes
(Tuesday, September 24, 2024) “Just weeks before the November general election, California Gov. Gavin Newsom signed three bills making it illegal to use deepfakes and other misleading digitally created or modified content in campaign ads.”
Technical Papers, Articles, and Preprints
[2409.11654] How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
https://arxiv.org/abs/2409.11654
Authors: Bunne, Charlotte; Roohani, Yusuf; Rosen, Yanay; Gupta, Ankit; Zhang, Xikun; Roed, Marcel; Alexandrov, Theo; AlQuraishi, Mohammed; Brennan, Patricia; Burkhardt, Daniel B.; Califano, Andrea; Cool, Jonah; Dernburg, Abby F.; Ewing, Kirsty; Fox, Emily B.; Haury, Matthias; Herr, Amy E.; Horvitz, Eric; Hsu, Patrick D.; Jain, Viren; …; and Quake, Stephen R.
(Wednesday, September 18, 2024) “The cell is arguably the smallest unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of AI-powered Virtual Cells, where robust representations of cells and cellular systems under different conditions are directly learned from growing biological data across measurements and scales. We discuss desired capabilities of AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions is within reach.”
[2409.12447] Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts
https://arxiv.org/abs/2409.12447
Authors: Liang, Jenny T.; Lin, Melissa; Rao, Nikitha; and Myers, Brad A.
(Thursday, September 19, 2024) “The introduction of generative pre-trained models, like GPT-4, has introduced a phenomenon known as prompt engineering, whereby model users repeatedly write and revise prompts while trying to achieve a task. Using these AI models for intelligent features in software applications require using APIs that are controlled through developer-written prompts. These prompts have powered AI experiences in popular software products, potentially reaching millions of users. Despite the growing impact of prompt-powered software, little is known about its development process and its relationship to programming. In this work, we argue that some forms of prompts are programs, and that the development of prompts is a distinct phenomenon in programming. We refer to this phenomenon as prompt programming. To this end, we develop an understanding of prompt programming using Straussian grounded theory through interviews with 20 developers engaged in prompt development across a variety of contexts, models, domains, and prompt complexities. Through this study, we contribute 14 observations about prompt programming. For example, rather than building mental models of code, prompt programmers develop mental models of the FM’s behavior on the prompt and its unique qualities by interacting with the model. While prior research has shown that experts have well-formed mental models, we find that prompt programmers who have developed dozens of prompts, each with many iterations, still struggle to develop reliable mental models. This contributes to a rapid and unsystematic development process. Taken together, our observations indicate that prompt programming is significantly different from traditional software development, motivating the creation of tools to support prompt programming. Our findings have implications for software engineering practitioners, educators, and researchers.”
