Adding in-person event value to AI-assisted research
AI Red Penning - undisciplinary
Adding in-person event value to AI-assisted research
Augmented human intelligence goes beyond the AGI mirage
This paper argues that contemporary Artificial General Intelligence (AGI) approaches face significant challenges that make Augmented Human Intelligence (AHI) a more promising and practically beneficial alternative. Drawing on phenomenological insights from Peter-Paul Verbeek’s technological mediation theory and comprehensive empirical evidence from decades of AI development, we argue that current AGI projects encounter recurring difficulties that may stem from attempting to replace rather than enhance human intelligence. Through analysis of mediation theory’s core concepts, systematic examination of recent AI performance data, and comprehensive review of human–AI collaboration studies, we show why augmentation approaches consistently outperform replacement attempts across domains requiring creativity, judgment, and contextual understanding. Prioritizing sophisticated AHI development over AGI pursuit represents not a compromise but a fundamental reorientation toward more effective and ethically sound approaches to artificial intelligence that better serve human flourishing.
AI-powered research talks
Discover, run, and publish academic talks as AI-enhanced, citable video that increases visibility, engagement and citations.
AI-Assisted Tools in the CHI 2027 Papers Review Process: What They Do, What They Do Not Do, and How Humans Remain Responsible - ACM CHI 2027
AI-Assisted Tools in the CHI 2027 Papers Review Process: What They Do, What They Do Not Do, and How Humans Remain Responsible Authors: Anna Cox, Tony Tang, Thomas Kosch, Erin Solovey, Petra Isenberg, Regan MandrykDate: 2026-08-29 Introduction In our recent post explaining the CHI 2027 Papers review process, we said we would pilot several AI-assisted […]
Introducing Elicit Research Agent: Rigor that powers decisions - Elicit
Elicit Research Agent is an AI environment built for high-stakes decisions
Mapping Citizen Science through the Lens of Human-Centered AI
Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.
ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI
Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is problem-driven autonomous research: given a fundamental challenge by a human expert, the AI independently navigates the scientific landscape, uncovers theoretical and empirical bottlenecks, and systematically expands the frontier of knowledge. In this paper, we introduce ScientistTwo, a fully autonomous multi-agent framework designed to realize this vision. Specifically, ScientistTwo takes an initial problem as input, establishes state-of-the-art baselines, formulates novel hypotheses, and coordinates specialized agents to orchestrate an end-to-end discovery cycle without human intervention. Moreover, the framework rigorously conducts experiments using diverse datasets and metrics, refines methodologies through automated ablation studies, and validates research findings via a closed-loop simulated peer-review rebuttal engine. To evaluate ScientistTwo's capabilities against the highest standards of human scientific achievement, we benchmark it across papers accepted at top-tier conferences such as ICLR, ICML, and NeurIPS. As a result, ScientistTwo autonomously generates expert-level, publishable papers and fully verified, executable codebases. Its solutions consistently outperform human state-of-the-art models, and achieve higher average review ratings than human-authored papers under automated AI review agents. These results show that ScientistTwo is not merely an assistive tool but an autonomous scientific pioneer capable of pushing the frontiers of human discovery. Project website: https://scientist-two.github.io/
Human scientists trounce the best AI agents on complex tasks
State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.
AI Is Changing Who Wins Research Grants
A new study from the Northwestern Innovation Institute finds that proposals showing stronger signs of AI-assisted writing were more likely to receive support from the National Institutes of Health, while also tending to align more closely with ideas that had already been funded.
The Room Where AI Happens
A weekend at the Curve conference—an AI insider gathering in Berkeley revealed how people think about AGI timelines, China, creativity, and writing
Meet the Tech Reporters Using AI to Help Write and Edit Their Stories
Independent writers are using AI agents all throughout their reporting process. What’s the value of a human journalist, anyway?
Human–AI Collaboration at Scale: Task Criticality, Agency, and Friction Across 250,000 Conversations
Stanford University researchers analyzed nearly 250,000 real-world human-AI conversations from Claude.ai to understand collaborative dynamics, finding that over half of interactions involve...
Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation
Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.
How Pew Research Center is – and is not – using AI in our work
In this post, we’ll share our current guidelines for the internal use of artificial intelligence and potential areas of experimentation.
How public involvement can improve the science of AI
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have included demands from affected parties to be given a seat at the table of AI evaluation. What, if anything, can public involvement add to the science of AI? In this perspective, we summarize the sociotechnical challenge of evaluating AI systems, which often adapt to multiple layers of social context that shape their outcomes. We then offer guidance for improving the science of AI by engaging lived-experience experts in the design, data collection, and interpretation of scientific evaluations. This article reviews common models of public engagement in AI research alongside common concerns about participatory methods, including questions about generalizable knowledge, subjectivity, reliability, and practical logistics. To address these questions, we summarize the literature on participatory science, discuss case studies from AI in healthcare, and share our own experience evaluating AI in areas from policing systems to social media algorithms. Overall, we describe five parts of any quantitative evaluation where public participation can improve the science of AI: equipoise, explanation, measurement, inference, and interpretation. We conclude with reflections on the role that participatory science can play in trustworthy AI by supporting trustworthy science.
