I’m John Salvatier. For the last 5 years I've been thinking about and working with world improvers 1-1.
I am interested in how to get the world on track. The world seems mysteriously not right. But the mystery also seems decipherable. What’s at the center?
A brief piece on how I see the puzzle is here, but very shortly:
Somehow our culture and institutions are constantly trying to work against the grain of reality. We’re disoriented and continually have to play catchup.
But whatever problems we have,
Whatever situation we’re in,
We can respond properly and naturally.
With the right ethos we can work with the grain of reality, rather than against.
I search for scalable and non-scalable tools to train that ethos.
And I run semi-regular workshops and studios. For example, a studio on working with intense questions playfully or a workshop on approaching confusing technical problems.
More about me
- Blog - essays on thinking and doing.
- PyMC3 - simple, efficient and robust Bayesian inference for complex models.
Jan M. Brauner, Sören Mindermann, Mrinank Sharma, David Johnston, John Salvatier et al. (2020) The effectiveness of eight nonpharmaceutical interventions against COVID-19 in 41 countries. Science
Owain Evans, Andreas Stuhlmüller, John Salvatier, and Daniel Filan. Modeling Agents with Probabilistic Programs. http://agentmodels.org.
Abel D, Salvatier J., Stuhlmüller A., Evans O. (2016) Agent-Agnostic Human-in-the-Loop Reinforcement Learning. Future of Interactive Learning Machines Workshop at NIPS 2016
Kreuger D., Leike J., Evans O., Salvatier J. (2016) Active Reinforcement Learning: Observing Rewards at a Cost. Future of Interactive Learning Machines Workshop at NIPS 2016
Salvatier J., Wiecki TV., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55
Photo credit Sandra Sobanska.