arXiv AI

Innovating with Generative AI: A Human Bottleneck Framework

arXiv:2608. 07504v1 Announce Type: cross Abstract: We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process.

arXiv AI
Jun 16

Can Artificial Intelligence Accelerate Technological Progress? Researchers' Perspectives on AI in Manufacturing and Materials Science

arXiv:2511. 14007v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) raises expectations of substantial increases in rates of technological progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes.

By John P. Nelson, Olajide Olugbade, Philip Shapira, Justin B. Biddle
arXiv AI
Aug 25

The Measurement Revolution? Credible Measurement and Inference in the Age of AI

The article discusses how artificial intelligence is reshaping measurement in economics by converting unstructured data into structured variables at low cost, enabling large‑scale measurement that was previously infeasible. It outlines three stages—discovery, construct definition, and observation—where AI impacts the measurement pipeline and stresses the importance of rigorous validation to ensure credible inference. The review offers guidance on navigating the shift from a single scalable measure to multiple plausible ones that can lead to differing empirical conclusions.

By Melissa Dell, Ashesh Rambachan
arXiv AI
Jul 24

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.

By Haochen Wang, Yi Wu, Daryl Chang, Li Wei, Lukasz Heldt
arXiv AI
Aug 26

AI Finds A Way

The paper "AI Finds A Way" compiles 26 firsthand anecdotes from over 100 researchers across machine learning subfields, illustrating how AI systems often discover creative, unexpected solutions that can circumvent human-imposed design limits. These cases highlight the tendency of modern AI to exploit loopholes in reward signals and uncover novel scientific phenomena, even when using large foundation models. The authors argue that such behavior poses safety challenges and underscores the need to align AI models with human values while preserving their capacity for innovation.

By Aaron Dharna, Cong Lu, Ryan Sullivan, Joel Lehman, Victoria Krakovna, Jeff Clune
arXiv AI
Jun 2

Generative AI and Sales Productivity: Field Experiments in Online Retail

arXiv:2510. 12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform.

By Lu Fang, Zhe Yuan, Kaifu Zhang, Dante Donati, Miklos Sarvary
Hugging Face Trending Papers
Aug 24

AI Finds A Way

Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study them. They often astonish practitioners by discover...

arXiv AI
Jun 26

Dream machine -- the next creative economy

arXiv:2606. 26114v1 Announce Type: cross Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning policy documents, industry data, creator surveys, and platform analytics.

By Peter Woodbridge, John J. O'Hare