Eric Horvitz

AI on Review: How Large Language Models Are Reshaping Peer Review The Peer Review Crunch and New "Reviewer Duties" Peer review is the backbone of scientific quality control, ensuring that research findings are vetted for accuracy and significance before publication (pmc.ncbi.nlm.nih.gov). In fast-moving fields like machine learning and computer vision, top conferences function much like journals - and the integrity of science depends on rigorous peer evaluation. However, the system is straining under an avalanche of submissions. Major AI conferences now routinely receive well over 10,000 papers, a surge that has stretched the reviewer pool to its limits (arxiv.org). This deluge has led to radical policy changes: some conferences now essentially conscript all submitting authors into service as reviewers. For example, ICLR 2025 explicitly warned authors that any paper without at least one author signed up to review would be desk-rejected (reddit.com). NeurIPS and others have similarly pleaded that "all authors help with reviewing, if asked," to tackle the reviewer shortage. https://lnkd.in/d7KDP99K

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New research presented at NeurIPS 2025 explores how to build fairer and more sustainable data ecosystems for large language models. The study, coauthored by AIMSEC Faculty member Beibei L. of Carnegie Mellon’s Carnegie Mellon University - Heinz College of Information Systems and Public Policy, proposes a framework for LLM data markets that evaluates dataset contributions and introduces a “fairshare” pricing mechanism, highlighting how transparent and equitable pricing could strengthen training data markets while improving model performance and long-term data supply. You can read more here! https://lnkd.in/erSeijPt

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CellVoyager is an autonomous AI framework built to systematically analyze single-cell data, helping to solve the scalability bottleneck in modern biological research. Operates through a five-stage iterative cycle where CellVoyager formulates scientific hypotheses, writes and runs code, self-corrects any errors, and uses visual models to interpret the resulting graphs and text. Learning from these interpretations, the agent continuously updates its exploration plan to uncover novel, unbiased biological insights that human researchers might otherwise overlook. Throughout this process, the system carefully documents its reasoning and steps to ensure all discoveries remain fully transparent and reproducible for expert review via Jupyter Notebooks Recently an agentic AI system that come to my attention is called Kai which is for building single-cell omics analyses in jupyter notebooks . Kai is infact more generalzed in its analysis compared to CellVoyager. I will be testing out these in coming days. https://lnkd.in/g2nTKqZC https://lnkd.in/garmcn-6 https://lnkd.in/gdyfyxqF

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NEW RESEARCH ALERT from the RAND Center on AI, Security, and Technology (CAST) Aurelia Attal-Juncqua, DrPH; Saskia Popescu, PhD; JP T., Graham Griffin; Rebecca Moritz; and Forrest Crawford mapped and evaluate the informal bioeconomy from a biosecurity perspective. Their recommendations identify concrete ways to enhance biosafety and biosecurity in this growing and important domain. Key findings include: - Biosafety training is common but differs by biolab. - There are no federal regulations specific to informal biolabs, but there is governance and oversight via workplace or jurisdictional regulations. - Financial incentives could be a mechanism for the implementation of standardized biosafety and biosecurity practices. - Options for additional visibility into informal biolabs' activities exist. Read the full report here: https://lnkd.in/eCFPtjhj

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