Jeremy Hadfield

Portrait of Jeremy Hadfield

Jeremy Hadfield currently works in Applied AI at Anthropic. He completed his undergraduate studies in philosophy, neuroscience, and computer science, as well as a Master of Engineering Management, at Dartmouth College. His academic interests have long been intertwined with the exploration of consciousness, happiness, and suffering, themes he addresses with a rigorous, multidisciplinary approach.

Previously, as an intern at the Qualia Research Institute (QRI), Jeremy contributed to groundbreaking research focused on quantifying consciousness and developing a consistent, meaningful understanding of valence—the spectrum of experience from happiness to suffering. His work at QRI involved exploring the “symmetry theory of valence,” an innovative framework for understanding these fundamental aspects of human experience.

Jeremy’s past presentation at the MTAConf 2020 demonstrated his unique ability to bridge scientific inquiry with theological considerations. He explored how scriptures address happiness and suffering within the wider context of neuroscience, computer science, and philosophical inquiry.

Videos by Jeremy Hadfield

Nature and Consciousness
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Jeremy Hadfield

Nature and Consciousness

Jeremy Hadfield, a philosophy, neuroscience, and computer science student at Dartmouth and intern at the Qualia Research Institute, presents the symmetry theory of valence—a framework for quantifying happiness and suffering. He proposes that mental states feel good or bad based on the symmetry of brain processes: consonant, symmetrical neural oscillations correlate with positive experiences, while dissonant patterns correlate with suffering. Using connectome-specific harmonic waves—mathematical tools borrowed from acoustics and applied to brain imaging—researchers can measure these patterns and potentially test the theory. Hadfield explores applications including understanding why meditation increases well-being, why exposure to nature improves mood, and how this framework might eventually allow us to communicate what “good” and “bad” mean to artificial intelligence in precise, measurable terms.