The Bazaar of Scientific Knowledge | shishyko!

The Bazaar of Scientific Knowledge | shishyko!

Is the current form of the scientific paper still optimal in 2025? How do we preserve, and efficiently leverage, the uncut gems of the scientific process?

It’s time to get rid of the peer-reviewed paper

The peer-reviewed journal article, perhaps the single most important device behind the expansion of scientific knowledge in the last century, is an extraordinarily expensive way to share novelty: e…

Science Must Decentralize

Knowledge production doesn’t happen in a vacuum. Every great scientific breakthrough is built on prior work, and an ongoing exchange with peers in the field. That’s why we need to address the threat

Science has been in a “replication crisis” for a decade. Have we learned anything?

Bad papers are still published. But some other things might be getting better.

Infinite Researchers | AI-Powered Scientific Discovery

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

AI as the Catalyst of the New Paradigm of Science? | Cadmus Journal

The rapid advancement of Artificial Intelligence (AI) is reshaping the landscape of scientific inquiry. This article examines the challenges confronting modern science, including knowledge fragmentation, diminishing returns of current paradigms, and the disconnect between science and society. It explores how AI offers solutions enhancing collaboration and integrating knowledge—as well as considers the possible “darker side” of using AI in research. However, the advent of AI in science not only enhances scientific research but also redefines the role of human researchers and fosters a new paradigm of human understanding. By embracing these transformations, a more cohesive and responsive scientific enterprise can emerge.

Building Collective Intelligence Networks for Flourishing Scientific Communities

Scientific communities are straining under mounting challenges—knowledge fragmentation, outdated publication processes, institutional erosion and funding cuts—that threaten our capacity to address urgent global problems. Traditional scientific process

We can create the future of science right now

Guest article by Paul Litvak

The Engine of Scientific Discovery: How New Methods and Tools Spark Major Breakthroughs

Abstract. How do we spark new scientific discoveries? Why do some breakthroughs seem even accidental? And most importantly, how can we accelerate them and

The new way we’ll do science

Papers should become human-readable views over a graph of data, tools, results, and certificates.

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

Simply Jekyll

A non-linear thread of important ideas weaved together to explore and understand the fabric of knowledge.

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.

Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.