Holistic Learning from TINY data from TINY devices using TINY AI agents
How to grow data and data flows?
TINY devices that provide logical data, smart scales, fitness tracker and blood biomarkers. Then expand on two fronts, environment, air quality and space lived in and genetics.
Learning like an immune system
Core to this is evolutionary and genetic algorithms that change and adapt as new information in cycles comes in. Cells are like tiny aI agents, cycling through combinations that make the whole better.
LAB
The LAB’s mission is to create a gaia intelligences. This will require work to give peers their data and will demonstrate machine learning ‘that works’ on a peer to peer level. Overall, the lab provides the research on all matters small and decentralized.
Ideas being built upon and learning from others
PARTS
HOP - DML
History: Health Oracle Protocol (HOP) founding mission was to find a way to allow decentralized machine learning or machine learning peer to peer. The first usecase was performing sport science on swimmers usin competition and training times. From this data the following question was posed: Can an AI produce a better training programme for swimmers than a human coach?
Each swimmer had a smart stopwatch collecting time data and when wearables came along this data was added. Sure, all the data could be pushed to a database in the cloud but the goal of DML is to perform the learning peer to peer. Is this possible? If it is, it starts with sovereign data. This is the core ingredients of machine learning. For a peer to peer network, at least two peers are required to be connected with permissionless access to join. The goal is to learn continuously, when an improvement is verified then all can be notified. There is no demand to collected all data from day one. Learn from the data available and like a baby becoming a toddler to child to an adult; be patient and allow learning and intelligence to emerge with time. Allow a range of learning techniques to be included, a besearch cycle that will conclude for each peer and network of peers if a better AI has been established. Be open ended; give the learning space to explore new techniques, take leaps of faith in new directions. Not all the time but enough to explore new search spaces, concepts, ideas and computational techniques. Be mindful that intelligence at all scales will be need. Tiny bottom up or top down, give enough information for self organization. Lastly, be mindful that this approach needs to be continuously assessed for risks of an AI taking its own path; act like an oracle responding to human.
continuous
Artificial intelligence needs better deep learning methods because current algorithms fail in continual learning settings, losing plasticity, forgetting cata…
Grounded knowledge way to digital gaia intelligence
Multi agents
The Shift from Models to Compound AI Systems
The BAIR Blog
Needs to be distributed
The Next AI Revolution: Yann LeCun’s Vision Beyond LLMs At the AI Action Summit in Paris, Yann LeCun underscored a fundamental shift in artificial intellige…
Yann LeCun, Meta, gives the AMS Josiah Willard Gibbs Lecture at the 2025 Joint Mathematics Meetings on “Mathematical Obstacles on the Way to Human-Level AI.”…
Evolution everywhere
Don’t invent faster horses - Prof. Jeff Clune
AI professor Jeff Clune ruminates on open-ended evolutionary algorithms—systems designed to generate novel and interesting outcomes forever. Drawing inspirat…
Beyond knowledge graphs
TINY and orchestrate
Risks to this approach
Meet Professor Geoffrey Hinton, AI pioneer and 2024 Nobel Prize in Physics Laureate, in a panel discussion on AI development, humanity, and the future, along…
How to trust each peers proof of work.
How to know which peer to collaborate with.
Can combined learning inject malicious data or code over time?