About
We aim to authoritatively inform and engage our members in the principled understanding and formation of The Sentient Age.
We aim to authoritatively inform and engage our members in the principled understanding and formation of The Sentient Age.
We aim to authoritatively inform and engage our members in the principled understanding and formation of The Sentient Age.
About
We aim to authoritatively inform and engage our members in the principled understanding and formation of The Sentient Age.
Our Mission
If there is to be informed public dialogue on the technologies of “intelligences”, there needs to be a principled scientific basis for communicating, modeling, and applying “intelligences” independent of their monetization, promotion, and exploitation. Such a pursuit should be undertaken as a rigorous scientific inquiry for the creation of “public good” much like Wikipedia, the one ubiquitous and transformative innovation on the Web designed for the common good.
In a global information ecosystem of increasing noise and skepticism, the sole beacon of trusted and verifiable truth claims is the Scientific Method. As John von Neuman and Albert Einstein argued on numerous occasions, scientific innovation thrives in an open environment where proprietary interests cannot stifle shared learning. A principled and evidence-based computational approach to understanding “intelligences” and their implications is needed not only to advance our collective understanding of intelligences but to ensure their independent and credibly informed oversight.
A First Principles First approach begins with the goal of understanding "intelligences", if not "conscious agents" from the fundamentals of physics, beginning with the Hamiltonian principle of Least Action, the Free Energy Principle, proceeding through Quantum Information Theory, and then through the Second Law of Quantum Complexity, and potentially, the mathematics of Markov kernels and Lebesgue logic. Some of these principles are not yet "settled science" and hence, without a widely accepted consensus. The intent of this effort is to determine whether and where there might be a rough consensus about the underlying physics of intelligence, if not consciousness, and then identifying experiments and applications to test such hypotheses.
Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains. The final application of the First Principles approach will be to societal and cognitive constructs such as "collective beliefs" or "ideologies" as manifested in new forms of enterprises, finance, governance, economies, and institutions.
If there is to be informed public dialogue on the technologies of “intelligences”, there needs to be a principled scientific basis for communicating, modeling, and applying “intelligences” independent of their monetization, promotion, and exploitation. Such a pursuit should be undertaken as a rigorous scientific inquiry for the creation of “public good” much like Wikipedia, the one ubiquitous and transformative innovation on the Web designed for the common good.
In a global information ecosystem of increasing noise and skepticism, the sole beacon of trusted and verifiable truth claims is the Scientific Method. As John von Neuman and Albert Einstein argued on numerous occasions, scientific innovation thrives in an open environment where proprietary interests cannot stifle shared learning. A principled and evidence-based computational approach to understanding “intelligences” and their implications is needed not only to advance our collective understanding of intelligences but to ensure their independent and credibly informed oversight.
A First Principles First approach begins with the goal of understanding "intelligences", if not "conscious agents" from the fundamentals of physics, beginning with the Hamiltonian principle of Least Action, the Free Energy Principle, proceeding through Quantum Information Theory, and then through the Second Law of Quantum Complexity, and potentially, the mathematics of Markov kernels and Lebesgue logic. Some of these principles are not yet "settled science" and hence, without a widely accepted consensus. The intent of this effort is to determine whether and where there might be a rough consensus about the underlying physics of intelligence, if not consciousness, and then identifying experiments and applications to test such hypotheses.
Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains. The final application of the First Principles approach will be to societal and cognitive constructs such as "collective beliefs" or "ideologies" as manifested in new forms of enterprises, finance, governance, economies, and institutions.
If there is to be informed public dialogue on the technologies of “intelligences”, there needs to be a principled scientific basis for communicating, modeling, and applying “intelligences” independent of their monetization, promotion, and exploitation. Such a pursuit should be undertaken as a rigorous scientific inquiry for the creation of “public good” much like Wikipedia, the one ubiquitous and transformative innovation on the Web designed for the common good.
In a global information ecosystem of increasing noise and skepticism, the sole beacon of trusted and verifiable truth claims is the Scientific Method. As John von Neuman and Albert Einstein argued on numerous occasions, scientific innovation thrives in an open environment where proprietary interests cannot stifle shared learning. A principled and evidence-based computational approach to understanding “intelligences” and their implications is needed not only to advance our collective understanding of intelligences but to ensure their independent and credibly informed oversight.
A First Principles First approach begins with the goal of understanding "intelligences", if not "conscious agents" from the fundamentals of physics, beginning with the Hamiltonian principle of Least Action, the Free Energy Principle, proceeding through Quantum Information Theory, and then through the Second Law of Quantum Complexity, and potentially, the mathematics of Markov kernels and Lebesgue logic. Some of these principles are not yet "settled science" and hence, without a widely accepted consensus. The intent of this effort is to determine whether and where there might be a rough consensus about the underlying physics of intelligence, if not consciousness, and then identifying experiments and applications to test such hypotheses.
Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains. The final application of the First Principles approach will be to societal and cognitive constructs such as "collective beliefs" or "ideologies" as manifested in new forms of enterprises, finance, governance, economies, and institutions.
If there is to be informed public dialogue on the technologies of “intelligences”, there needs to be a principled scientific basis for communicating, modeling, and applying “intelligences” independent of their monetization, promotion, and exploitation. Such a pursuit should be undertaken as a rigorous scientific inquiry for the creation of “public good” much like Wikipedia, the one ubiquitous and transformative innovation on the Web designed for the common good.
In a global information ecosystem of increasing noise and skepticism, the sole beacon of trusted and verifiable truth claims is the Scientific Method. As John von Neuman and Albert Einstein argued on numerous occasions, scientific innovation thrives in an open environment where proprietary interests cannot stifle shared learning. A principled and evidence-based computational approach to understanding “intelligences” and their implications is needed not only to advance our collective understanding of intelligences but to ensure their independent and credibly informed oversight.
A First Principles First approach begins with the goal of understanding "intelligences", if not "conscious agents" from the fundamentals of physics, beginning with the Hamiltonian principle of Least Action, the Free Energy Principle, proceeding through Quantum Information Theory, and then through the Second Law of Quantum Complexity, and potentially, the mathematics of Markov kernels and Lebesgue logic. Some of these principles are not yet "settled science" and hence, without a widely accepted consensus. The intent of this effort is to determine whether and where there might be a rough consensus about the underlying physics of intelligence, if not consciousness, and then identifying experiments and applications to test such hypotheses.
Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains. The final application of the First Principles approach will be to societal and cognitive constructs such as "collective beliefs" or "ideologies" as manifested in new forms of enterprises, finance, governance, economies, and institutions.
Thought Leaders
Dr. John H. Clippinger
Host, Author and Advocate
Researcher, and entrepreneur in self-organizing agents, AI and computational linguistics, policy advocate for privacy, self-sovereign identity, and advocate for new generation of societal and ecological polities and institutions grounded in scientific first principles.
Dr. John H. Clippinger
Host, Author and Advocate
Researcher, and entrepreneur in self-organizing agents, AI and computational linguistics, policy advocate for privacy, self-sovereign identity, and advocate for new generation of societal and ecological polities and institutions grounded in scientific first principles.
Dr. John H. Clippinger
Host, Author and Advocate
Researcher, and entrepreneur in self-organizing agents, AI and computational linguistics, policy advocate for privacy, self-sovereign identity, and advocate for new generation of societal and ecological polities and institutions grounded in scientific first principles.
Dr. John H. Clippinger
Host, Author and Advocate
Researcher, and entrepreneur in self-organizing agents, AI and computational linguistics, policy advocate for privacy, self-sovereign identity, and advocate for new generation of societal and ecological polities and institutions grounded in scientific first principles.
Dr. Chris Fields
Independent Scientist
Specializing in classical and quantum information-theoretic principles applied to developmental biology and cognitive neuroscience.
Dr. Chris Fields
Independent Scientist
Specializing in classical and quantum information-theoretic principles applied to developmental biology and cognitive neuroscience.
Dr. Chris Fields
Independent Scientist
Specializing in classical and quantum information-theoretic principles applied to developmental biology and cognitive neuroscience.
Dr. Chris Fields
Independent Scientist
Specializing in classical and quantum information-theoretic principles applied to developmental biology and cognitive neuroscience.
Prof. Karl Friston
Neuroscientist and Theoretician
Authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. Father of Active Inferencing and Free Energy Principle.
Prof. Karl Friston
Neuroscientist and Theoretician
Authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. Father of Active Inferencing and Free Energy Principle.
Prof. Karl Friston
Neuroscientist and Theoretician
Authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. Father of Active Inferencing and Free Energy Principle.
Prof. Karl Friston
Neuroscientist and Theoretician
Authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. Father of Active Inferencing and Free Energy Principle.
Prof. David Silbersweig
Psychiatrist, MD. and Neuroscientist
Chairman of the Department of Psychiatry and Co-Director of the Institute for the Neurosciences at Brigham and Women's Hospital, and Stanley Cobb Professor of Psychiatry at Harvard Medical School.
Prof. David Silbersweig
Psychiatrist, MD. and Neuroscientist
Chairman of the Department of Psychiatry and Co-Director of the Institute for the Neurosciences at Brigham and Women's Hospital, and Stanley Cobb Professor of Psychiatry at Harvard Medical School.
Prof. David Silbersweig
Psychiatrist, MD. and Neuroscientist
Chairman of the Department of Psychiatry and Co-Director of the Institute for the Neurosciences at Brigham and Women's Hospital, and Stanley Cobb Professor of Psychiatry at Harvard Medical School.
Prof. David Silbersweig
Psychiatrist, MD. and Neuroscientist
Chairman of the Department of Psychiatry and Co-Director of the Institute for the Neurosciences at Brigham and Women's Hospital, and Stanley Cobb Professor of Psychiatry at Harvard Medical School.
Dr. Don Hoffman
Cognitive Psychologist and Author
Studies consciousness, visual perception and evolutionary psychology using mathematical models and psychophysical experiments.
Dr. Don Hoffman
Cognitive Psychologist and Author
Studies consciousness, visual perception and evolutionary psychology using mathematical models and psychophysical experiments.
Dr. Don Hoffman
Cognitive Psychologist and Author
Studies consciousness, visual perception and evolutionary psychology using mathematical models and psychophysical experiments.
Dr. Don Hoffman
Cognitive Psychologist and Author
Studies consciousness, visual perception and evolutionary psychology using mathematical models and psychophysical experiments.
Dr. Tom Kehler
Scientist and Entrepreneur
Chief Scientist, Co-Founder, and Board Member at CrowdSmart, leveraging 30+ years in AI and collective intelligence technologies.
Dr. Tom Kehler
Scientist and Entrepreneur
Chief Scientist, Co-Founder, and Board Member at CrowdSmart, leveraging 30+ years in AI and collective intelligence technologies.
Dr. Tom Kehler
Scientist and Entrepreneur
Chief Scientist, Co-Founder, and Board Member at CrowdSmart, leveraging 30+ years in AI and collective intelligence technologies.
Dr. Tom Kehler
Scientist and Entrepreneur
Chief Scientist, Co-Founder, and Board Member at CrowdSmart, leveraging 30+ years in AI and collective intelligence technologies.
Prof. Anil Seth
Professor of Cognitive and Computational Neuroscience at the University of Sussex and a proponent of materialist explanations of consciousness, he is currently among the most cited scholars globally on neuroscience and cognitive science topics.
Prof. Anil Seth
Professor of Cognitive and Computational Neuroscience at the University of Sussex and a proponent of materialist explanations of consciousness, he is currently among the most cited scholars globally on neuroscience and cognitive science topics.
Prof. Anil Seth
Professor of Cognitive and Computational Neuroscience at the University of Sussex and a proponent of materialist explanations of consciousness, he is currently among the most cited scholars globally on neuroscience and cognitive science topics.
Prof. Anil Seth
Professor of Cognitive and Computational Neuroscience at the University of Sussex and a proponent of materialist explanations of consciousness, he is currently among the most cited scholars globally on neuroscience and cognitive science topics.
Prof. Bert de Vries
BIASlab, Lazy Dynam
Inspired by computational neuroscience, Bayesian machine learning, and signal processing systems to develop intelligent autonomous agents that learn from their environment to automate novel signal processing and control algorithms.
Prof. Bert de Vries
BIASlab, Lazy Dynam
Inspired by computational neuroscience, Bayesian machine learning, and signal processing systems to develop intelligent autonomous agents that learn from their environment to automate novel signal processing and control algorithms.
Prof. Bert de Vries
BIASlab, Lazy Dynam
Inspired by computational neuroscience, Bayesian machine learning, and signal processing systems to develop intelligent autonomous agents that learn from their environment to automate novel signal processing and control algorithms.
Prof. Bert de Vries
BIASlab, Lazy Dynam
Inspired by computational neuroscience, Bayesian machine learning, and signal processing systems to develop intelligent autonomous agents that learn from their environment to automate novel signal processing and control algorithms.
Daniel Friedman
President and Co-Founder of the Active Inference Institute
Ph.D. from Stanford University, studying the evolution of collective behavior in ants and contributing significantly to research in gene expression evolution in ants and bees. Diverse interests include fractals, burritos, metaphors, and drawing.
Daniel Friedman
President and Co-Founder of the Active Inference Institute
Ph.D. from Stanford University, studying the evolution of collective behavior in ants and contributing significantly to research in gene expression evolution in ants and bees. Diverse interests include fractals, burritos, metaphors, and drawing.
Daniel Friedman
President and Co-Founder of the Active Inference Institute
Ph.D. from Stanford University, studying the evolution of collective behavior in ants and contributing significantly to research in gene expression evolution in ants and bees. Diverse interests include fractals, burritos, metaphors, and drawing.
Daniel Friedman
President and Co-Founder of the Active Inference Institute
Ph.D. from Stanford University, studying the evolution of collective behavior in ants and contributing significantly to research in gene expression evolution in ants and bees. Diverse interests include fractals, burritos, metaphors, and drawing.
Andrew Pasha
Data Analyst, LEARN Charter School Network
Scientist and engineer with expertise in economics and machine learning; MA in Social Sciences from the University of Chicago.
Andrew Pasha
Data Analyst, LEARN Charter School Network
Scientist and engineer with expertise in economics and machine learning; MA in Social Sciences from the University of Chicago.
Andrew Pasha
Data Analyst, LEARN Charter School Network
Scientist and engineer with expertise in economics and machine learning; MA in Social Sciences from the University of Chicago.
Andrew Pasha
Data Analyst, LEARN Charter School Network
Scientist and engineer with expertise in economics and machine learning; MA in Social Sciences from the University of Chicago.
David Lovejoy
Managing Director
A serial entrepreneur with an MBA from the University of British Columbia, founder and CEO of Horizon Search, media producer, business advisor, and former executive tour guide in Japan.
David Lovejoy
Managing Director
A serial entrepreneur with an MBA from the University of British Columbia, founder and CEO of Horizon Search, media producer, business advisor, and former executive tour guide in Japan.
David Lovejoy
Managing Director
A serial entrepreneur with an MBA from the University of British Columbia, founder and CEO of Horizon Search, media producer, business advisor, and former executive tour guide in Japan.
David Lovejoy
Managing Director
A serial entrepreneur with an MBA from the University of British Columbia, founder and CEO of Horizon Search, media producer, business advisor, and former executive tour guide in Japan.
“Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains.”
“Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains.”
“Underlying this First Principles First approach is a collective effort to test the claim—the very significant claim—that such First Principles can be applied across different scales and domains.”
John H. Clippinger
Co-Founder
Co-Founder
Co-Founder
Co-Founder
Join the sentient revolution
Contribute your insights to shape the future of AI and human cognition
Join the sentient revolution
Contribute your insights to shape the future of AI and human cognition
Join the sentient revolution
Contribute your insights to shape the future of AI and human cognition
Join the sentient revolution
Contribute your insights to shape the future of AI and human cognition
First Principles First is at the forefront of developing AI agents that act as proxies for humans, capable of managing tasks autonomously and securely in diverse environments.
Nullius in Verba
Take No One's Word for It
First Principles First is at the forefront of developing AI agents that act as proxies for humans, capable of managing tasks autonomously and securely in diverse environments.
Nullius in Verba
Take No One's Word for It
First Principles First is at the forefront of developing AI agents that act as proxies for humans, capable of managing tasks autonomously and securely in diverse environments.
First Principles First is at the forefront of developing AI agents that act as proxies for humans, capable of managing tasks autonomously and securely in diverse environments.
Nullius in Verba
Take No One's Word for It
