Choosing a discovery mechanism means choosing assumptions about authority, delegation, privacy, and control. The answer is rarely simple.
Continuous Discovery | Definition and Overview | Product Talk
Weekly touch points with customers by the team building the product, where they conduct small research activities in pursuit of a desired outcome.
Reinventing Discovery: The New Era of Networked Science
In Reinventing Discovery, Michael Nielsen argues that w…
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
Building a Privacy Preserving Intent-Driven Discovery Protocol
Inventing New Nature
Defining the Protocol Institute's research mission
Fulcrum - Leverage for Discovery
We place AI engineers with research labs working on hard scientific problems.
Four Ps for Building Massive Collective Knowledge Systems
Design principles for collective knowledge systems—permanence, provenance, permission, and placement—that enable robust networks for evidence-based decision making.
Four Ps for Building Massive Collective Knowledge Systems
Design principles for collective knowledge systems—permanence, provenance, permission, and placement—that enable robust networks for evidence-based decision making.
Ground the model, or it invents the evidence
Trusted, curated, subject-specific models are needed for academia
Infinite Researchers | AI-Powered Scientific Discovery
What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.
Free tool download: Customer Discovery Toolkit
The Design Gym's favorite discovery tools from 10+ years of helping organizations design and deliver better customer and employee experiences
The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).
Coordination Tech in Science: Letters, Journals, and Whatever Comes Next | shishyko!
To modernize our scientific infrastructure, we need new contextualization and coordination technologies that decouple trust from legacy branding — shifting from gatekeeping on write to algorithmic contextualization on read.
Against theory-motivated experimentation: Can random experimental choice lead to better theories?
Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.
Improving discoverability is the key thing - which a hard ux problem to solve without defaulting to algorithms. Forced algorithms are a bad solution to what needs to actually happen which is actually getting people to engage with others which modern social media has beat out of people
Jim Ray
One of my long held convictions is most people don't care about concepts like "openness" or "decentralization" or "interoperability" (people are busy!) but they do care about what those enable. It's the job of the much smaller number of people who do care to build the experience people will love.
