Glimmer · Reproducible AI science

ggml

AI inference at the edge. ggml has 22 repositories available. Follow their code on GitHub.

Termboard - Knowledge Graphs Made Simple

Finally, knowledge graphs anyone can build.

Deep Research, information vs. insight, and the nature of science

What AI will accelerate in the scientific process, what it cannot do, and how we can prepare for new manners of scientific investigation.

Infinite Researchers | AI-Powered Scientific Discovery

What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.

Find Open Datasets for AI and Research | Kaggle

Browse and download hundreds of thousands of open datasets for AI research, model training, and analysis. Join a community of millions of researchers, developers, and builders to share and collaborate on Kaggle.

Lightcone Research

An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.

Google Search's guidance about AI-generated content  |  Google Search Central Blog  |  Google for Developers

In this post, we'll share more about how AI-generated content fits into our long-standing approach to show helpful content to people on Search.

AI has supercharged scientists—but may have shrunk science

Analysis of 41 million papers finds that although AI expands individual impact, it narrows collective scientific exploration

Science that Compounds: The Need for A New Substrate for Research in the Age of AI

This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora

In the current era of information abundance, transforming vast amounts of unstructured data into structured, machine-readable knowledge remains one of the most significant challenges in artificial intelligence. Knowledge Graphs (KGs) have emerged as the cornerstone technology for this transformation Zhao et al. (2024), providing the semantic backbone for applications ranging from search engines and question answering Wu et al. (2024); Chen et al. (2024c); Zong et al. (2024); Sun et al. (2024b) to recommendation systems Lyu et al. (2024) and complex reasoning tasks Li et al. (2024b). Yet despite their critical importance, current KG construction approaches remain hampered by an inherent paradox: they require predefined schemas created by domain experts, which fundamentally limits their scalability, adaptability, and domain coverage.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

Sourcegraph — Code Understanding, Oversight and Evolution

Give humans and agents complete context to understand, oversee, and evolve the world's largest, most complex codebases.