Decentralized AI + science

Human Augmentation

Knowledge Augmentation

Human Intelligence and Knowledge Augmentation

Human Intelligence Augmentation is the process of improving human cognitive abilities through the use of tools: training humans to use AI systems, developing AI systems that can help humans learn and reason, and using AI to help humans make better decisions.

There are many reasons why Human Knowledge/Intelligence Augmentation is important: as the world becomes increasingly complex, it is becoming more difficult for humans to understand and keep up with all of the information. AI can help humans by providing them with the ability to process and understand large amounts of data. This provides us the ability to make better decisions, faster. And making human collaboration easier through augmention of relationships (recommending the best people to work together on solving a specific challenge, knowing how to best approach them etc.)

Knowledge augmention tools can dramatically improve productivity through assisting with learning, reasoning, and creativity. Playing with, and getting better at prompting OpenAI GPT3 is a hint of human augmentation in writing, and is already starting to add augmentation to art (dall-e2), coding (copilot, ai pair programmer) and other areas.

Max Tegmark describes a personalized learning tool

Given any person’s knowledge and abilities, Prometheus could determine the fastest way for them to learn any new subject in a manner that kept them highly engaged and motivated to continue and produce the corresponding optimized videos, reading materials, exercises, and other learning tools. […] by leveraging Prometheus’ movie-making talents, the video segments would truly engage, providing powerful metaphors that you would relate to, leaving you craving to learn more.” - Life 3.0, Max Tegmark

“Generative AI for science could help reverse the deceleration of innovation in science by making it easier and cheaper to find new ideas. Such models could also provide data-backed warnings of therapeutic hypotheses that are certain to fail, counterbalancing human bias and avoiding billion-dollar, decades-long blind alleys. Finally, such models could combat the reproducibility crisis by mapping, weighing, and contextualizing research results, providing a score on trustability.” - How to Build a GPT-3 for Science

Tools For Thought

Some ideas for intelligence augmention tools

Some enabling technologies

Some tools I like


Elicit by Ought


Github/OpenAI CoPilot (coding assistant)


Remnote(knowledge management tool)


OpenAI Dall-E (image generation)





Brain Computer Interfaces (BCIs)

Another important application will be to actually interface our brains with computers, which I believe will become an increasingly intimate merger in the decades ahead. - Ray Kurzweil