We use generative AI to extract managerial expectations about their economic outlook from over 120,000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making.

Artificial intelligence is expected to have an impact on the upcoming US election in November. States have been trying to protect against misinformation by passing laws that require political advertisements to disclose when they have used generative AI. Twenty states now have rules on the books, and according to new research, voters have a negative reaction to seeing those disclaimers. That seems like a pretty fair response: If a politician uses generative AI to mislead voters, then voters don't appreciate that. The study was conducted by New York University's Center on Technology Policy and first reported by The Washington Post.

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified {\it a priori}, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL.

Hearst has become the latest major US publisher to sign an agreement to license its content to ChatGPT creator OpenAI. As part of a partnership announced on Tuesday, content from more than 60 Hearst-owned publications will appear in ChatGPT and other OpenAI products. Some of the publisher's more notable properties include Esquire, Cosmopolitan and Elle. It also owns newspapers like the San Francisco Chronicle. When Hearst content appears in ChatGPT, the software will provide citations and direct links.

I'm taking over TechScape from Alex Hern, and I'd like to introduce myself and my ideas for this newsletter. A bit about me: I started working at the Guardian the day Sam Bankman-Fried went on trial. My first holiday from my new job coincided with the shock firing of Sam Altman from OpenAI. The story I tell over and over again at parties is the one about how I was arrested and jailed while reporting a story on deadly testicular injections. We'll dissect the significance of the week's most substantial tech news, investigate odd niches, catch you up on the best of the Guardian's reporting and offer a helpful tip now and then.

Summary: The paper proposes a way to incorporate constraints into the learning of generative models through posterior regularization. In doing so, the paper draws connections between posterior regularization and policy optimization. One of the key contributions of this paper is that the constraints are modeled as extrinsic rewards and learned through inverse reinforcement learning. The paper studies an interesting and very practical problem and the contributions are substantial. The writing could definitely be made clearer for Sections 3 and 4, where the overloaded notation is often hard to follow. I have the following questions: 1.

The authors develop a framework allowing VAE type computation on a broad class of probablistic model structures. This is motivated in particular by the idea that some lvs may have a straightforward meaning and have some labels available (e.g. which digit in MNIST/SVHN, what lighting direction in the face data), whereas others are more intangible (e.g. They propose a slightly different approach to the semi-supervised VAE of Kingma et al., by considering the (semi)supervised variables y as LVs forced to specific values for the supervised data samples. This is straightforward in the setting where q(y x) can be calculated directly, and can be handled by importance sampling if integration over z is required to calculate q(y x). Experiments are presented on MNIST, SVHN and a faces image data with variation in lighting according 38 individuals.

This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc at https://github.com/GAIR-NLP/O1-Journey.

Effective monitoring of freight transportation is essential for advancing sustainable, low-carbon economies. Traditional methods relying on single-modal data and discrete simulations fall short in optimizing intermodal systems holistically. These systems involve interconnected processes that affect shipping time, costs, emissions, and socio-economic factors. Developing digital twins for real-time awareness, predictive analytics, and urban logistics optimization requires extensive efforts in knowledge discovery, data integration, and multi-domain simulation. Recent advancements in generative AI offer new opportunities to streamline digital twin development by automating knowledge discovery and data integration, generating innovative simulation and optimization solutions. These models extend digital twins' capabilities by promoting autonomous workflows for data engineering, analytics, and software development. This paper proposes an innovative paradigm that leverages generative AI to enhance digital twins for urban research and operations. Using freight decarbonization as a case study, we propose a conceptual framework employing transformer-based language models to enhance an urban digital twin through foundation models. We share preliminary results and our vision for more intelligent, autonomous, and general-purpose digital twins for optimizing integrated freight systems from multimodal to synchromodal paradigms.

Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, often fail to uncover valuable insights not explicitly stated in article titles or keywords. These approaches are unable to perform semantic searches and contextual understanding, limiting their effectiveness in classifying topics and characterizing studies. In this paper, we address these limitations by leveraging Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis. We developed a technical workflow that integrates a vector database, Sentence Transformers, a Gaussian Mixture Model (GMM), Retrieval Agent, and Large Language Models (LLMs) to enable contextual search, topic ranking, and characterization of research using customized prompt templates. A pilot study analyzing 223 urban science-related articles published in Nature Communications over the past decade highlights the effectiveness of our approach in generating insightful summary statistics on the quality, scope, and characteristics of papers in high-impact journals. This study introduces a new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.