BLOB
AI as a community-driven, iterative and locally adaptable system. An exploration of how intelligence can evolve through collective participation, informed by local insights and shared knowledge.
Research Areas
Read Documentation →This research explores collaborative intelligence with ai support as a process that evolves in local environments and is shaped by user interaction. The concept focuses on how intelligence can grow with its users and continuously adapt to their needs and contributions. The aim is to create a framework that is open, sustainable and based on collective knowledge.
Energy awareness
Analyses of different scaled models for different sized computers to achieve high quality results on the lowest possible resources and to decentralize consumption.
Knowledge Exchange
Open and transparent AI training methodologies, ensuring accessibility through a no-code, approach that encourages participation and knowledge sharing.
AI-Powered Analysis
In data processing, the focus is on traceability and ownership of the data origin to enable collaborative workspaces to gain insights from the data originators.
Collaborative Workspace
enabling collaborative conversations with intelligence to stimulate discussion.
Iterative Learning
How an iterative and user-evaluated data analysis prepares data that allows intilligence to grow sustainably.
Principles
The principles that guide everything we build and do.
Reuse
By integrating ollama, standard household computers with little to no graphics power can be reused and the energy consumption can be shared.
Data Ownership
local usability can enable the use of private data, allowing each user to co-decide which data is trained and which is not and what is shared with the system.
Community Governance
Power should be distributed, not centralized. We build tools that allow communities to govern themselves, set their own rules, and determine what matters most to them without relying on central authorities.
Transparent Technology
AI shouldn't be a black box that simply gives answers without explaining where the information comes from. Users should know how decisions are made and have the opportunity to influence these processes.
Accessibility
Advanced technology should be accessible to everyone, not just those with technical expertise or extensive resources. we pursue approaches that are intuitive, affordable and can be used by communities of all kinds without coding.
Environmental Responsibility
Durch verteilte Ansätze wird der Bedarf an riesigen Datenzentren und energieintensiver Datenverarbeitung reduziert. Durch die Nutzung lokaler Ressourcen und die Optimierung der Verarbeitung minimiert das System seinen ökologischen Fußabdruck.
