Panos Ipeirotis

Exciting news - our work on the impact of LLMs on Wikipedia was mentioned in the WSJ! See Greg Ip’s latest column on the impacts of LLMs on knowledge generation: https://lnkd.in/eA8QZ_3A This is part of a larger conversation that highlights the need to study not just algorithms, but how *humans* use these algorithms. In the short term, when do humans combine their own expertise with the algorithm’s strengths to arrive at better outcomes than they could alone versus when do they over-rely on the algorithm and make worse decisions? In the long term, how do these algorithms change our behavior and how might this create feedback loops between humans <-> algorithms? More to come on this front - stay tuned! :)

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Special Issue of Production and Operations Management on "Generative AI (GenAI) and Agentic AI at the Marketing–Operations Interface." Guest edited by Roland Rust, Ming-Hui Huang, Shane Wang, Praveen Kopalle. Commitment to Timeliness   Given the rapid pace of research and practice in this domain, the editorial team is committed to quick turnaround and timely decisions. A paper submitted to the special issue will be processed right away. Authors are encouraged to submit as soon as they are ready. Our goal is to ensure that accepted papers are published swiftly, with the special issue scheduled for print publication in early 2027 to maximize its relevance and impact.   First-round submission deadline: June 30, 2026   First-round decisions: August 31, 2026   Revised submission deadline: November 15, 2026   Final decisions: December 31, 2026

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#upskilling during the #shutdown Tip 11: Version Control is not just for computer science. It's part of Reproducible Results for #science and #statistics too (The American Statistical Association - ASA even includes it on their reproducible research recommendations https://lnkd.in/gD95Bx-9). Luckily, #Git (the primary version control program) is not only free and open source, but also has a free and open source book https://lnkd.in/geT_viFC)

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A study on using LLMs to create plain language summaries. “Our findings indicate that while LLMs can generate PLSs that appear indistinguishable from human-written ones in subjective evaluations, human-written PLSs lead to significantly better comprehension.” Also interesting is that self-reported comprehension isn't as reliable as testing it through questions. https://lnkd.in/gw_FDpp7

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📣 New Springer Book Release in Lecture Notes in Physics Lectures on Graph Theory: Insights into Feynman Diagrams by Ray D. Sameshima 🔗 https://lnkd.in/dzcdxZ4M 📘 This volume offers a structured introduction to the mathematical framework underlying Feynman diagrams, with a focus on graph theory and its applications in theoretical physics. Topics include group theory, linear algebra, and matrix theory, building toward integral representations of Feynman diagrams. 📘 Feynman's parametrization is treated in depth, with supplementary chapters on Schwinger and Lee-Pomeransky approaches. Advanced readers will find further discussion on integration-by-parts identities and intersection number theory. 📘 The book is accessible to readers with a basic background in algebra and calculus.

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Congratulations Pia Saunders Campbell, Merve Ayyüce KIZRAK, Ph.D., and Elsie Jang on the publication of "Meeting the AI Workforce Challenge." This blend of public-private-philanthropic perspectives, is a great read for anyone engaged in workforce development, AI policy, or the future of talent. As with any of the prior technological breakthroughs, we must and will evolve into the new jobs and skills of the future; however, the rapid pace of AI calls on us to do this faster than ever before. Key Takeaways 👇 🔄 AI is rapidly reshaping the U.S. labor market, particularly in entry-level and technical roles. Adapting quickly is urgent for both organizations and individuals. 🤝 The federal government's AI Action Plan and America’s Talent Strategy are solid starts, but coordinated cross-sector action—including youth leadership and local partnerships—is essential for equitable, future-ready outcomes. 📚 There’s a pressing need to integrate AI literacy, ethical reasoning, and problem-solving into all levels of education and professional training, supporting resilient, adaptive workers. 🪴 Youth engagement and inclusion are critical. Policies shaped with (and by) young people will be more innovative and enduring as the pace of technological change accelerates. Grateful for the opportunity to share my perspectives with this team along the way and learn alongside such passionate change makers! https://lnkd.in/eKfNX7UC #AI #WorkforceDevelopment #FutureofWork #Collaboration #TalentStrategy

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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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This study used impressive, theory-informed coding and analyses of digital trace data to reveal clusters of students who reliably differed on prior knowledge, motivation, and performance. To me, this demonstrates how strong validity arguments can be made about the coding of digital trace data. https://lnkd.in/ecMxe7Ht

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🚨 New Research on Large Language Models and Interpretability: 1. Their tendency to self-repair makes it difficult to trace why a particular output happened. 2. That breaks the chain of causal interpretability. 3. And without causal clarity, true explainability remains out of reach. 📄 In our latest paper, we propose GIM a method to tackle this head-on, unlocking more reliable interpretability in LLMs. 📎 Link to the paper: https://lnkd.in/dKXH4vfC Congratulations to Joakim Edin et al. Collaboration between Corti, Stanford University, Copenhagen University and LUT University.

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Over the years working with universities, I’ve noticed something interesting about institutional data environments. People talk a lot about data silos. But far fewer talk about shadow systems. These are the spreadsheets, local databases, shared folders, and small scripts that quietly power a huge amount of institutional work. They emerge because people need answers quickly, systems can’t always keep up, and departments develop their own ways of working with data. The problem is that once data leaves a central system, it often begins a new lifecycle - copied, transformed, filtered, and reused in ways that are difficult to track. Over time, this can introduce risks around: • data security • data quality • governance and accountability In a new post, I explore why shadow systems exist, the risks they create, and how institutions can start illuminating them through data lineage and governance. 👉 Shadow Systems in Higher Education: The Data Infrastructure Nobody Wants to Talk About: https://lnkd.in/gag9Byn2 Plaid Analytics

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Large Language Models (LLMs) are revolutionizing work, but the challenge of "hallucinations", generating plausible but incorrect information, remains. A new paper, "A comprehensive taxonomy of hallucinations in Large Language Models," provides a critical framework for understanding this issue with lots of rich detail. https://lnkd.in/eXsfzms6 The author, Manuel Cossio defines a formal taxonomy, distinguishing between intrinsic (contradicting input context) and extrinsic (inconsistent with reality) hallucinations. The paper argues that hallucinations are an inherent and inevitable aspect of computable LLMs, regardless of architecture or training. This research underscores that future efforts must focus on robust detection, mitigation, and continuous human oversight for responsible and reliable deployment of these powerful tools. It's a must-read for anyone interested in AI. Pay particular attention to Table 2. #AI #MachineLearning #LLM #NLP #ArtificialIntelligence

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Stanford Institute for Human-Centered Artificial Intelligence (HAI): AI Index Report 2025 Top Takeaways 1. AI performance on demanding benchmarks continues to improve. In 2023, researchers introduced new benchmarks—MMMU, GPQA, and SWE-bench—to test the limits of advanced AI systems. Just a year later, performance sharply increased: scores rose by 18.8, 48.9, and 67.3 percentage points on MMMU, GPQA, and SWE-bench, respectively. 2. AI is increasingly embedded in everyday life. From healthcare to transportation, AI is rapidly moving from the lab to daily life. In 2023, the FDA approved 223 AI-enabled medical devices, up from just six in 2015. On the roads, self-driving cars are no longer experimental: Waymo, one of the largest U.S. operators, provides over 150,000 autonomous rides each week, while Baidu’s affordable Apollo Go robotaxi fleet now serves numerous cities across China. 3. Business is all in on AI, fueling record investment and usage, as research continues to show strong productivity impacts. In 2024, U.S. private AI investment grew to $109.1 billion—nearly 12 times China’s $9.3 billion and 24 times the U.K.’s $4.5 billion. Generative AI saw particularly strong momentum, attracting $33.9 billion globally in private investment—an 18.7% increase from 2023. 4. The U.S. still leads in producing top AI models—but China is closing the performance gap. In 2024, U.S.-based institutions produced 40 notable AI models, significantly outpacing China’s 15 and Europe’s three. While the U.S. maintains its lead in quantity, Chinese models have rapidly closed the quality gap. 5. The responsible AI ecosystem evolves—unevenly. AI-related incidents are rising sharply, yet standardized RAI evaluations remain rare among major industrial model developers. However, new benchmarks like HELM Safety, AIR-Bench, and FACTS offer promising tools for assessing factuality and safety. 6. Global AI optimism is rising—but deep regional divides remain. 7. AI becomes more efficient, affordable and accessible. Driven by increasingly capable small models, the inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024. 8. Governments are stepping up on AI—with regulation and investment. In 2024, U.S. federal agencies introduced 59 AI-related regulations—more than double the number in 2023—and issued by twice as many agencies. Globally, legislative mentions of AI rose 21.3% across 75 countries since 2023, marking a ninefold increase since 2016. 9. AI and computer science education is expanding—but gaps in access and readiness persist. 10. Industry is racing ahead in AI—but the frontier is tightening. Nearly 90% of notable AI models in 2024 came from industry, up from 60% in 2023, while academia remains the top source of highly cited research.

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Our new #icml2025 paper shows how to use synthetic data from Mixture-of-Agents to boost LM fine-tuning + RL. Turns out a mixture of small agents can be much more effective/cheaper than using a large LM as teacher! Blog: https://lnkd.in/gCFZhbdD Paper: https://lnkd.in/gPx5crHU Great job by Junlin W., Roy X., Shang Zhu, Jue Wang, Ben Athiwaratkun, Bhuwan Dhingra, Leon Song, Ce Zhang Together AI!

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