Daniel Grimmer
Postdoctoral Researcher in Philosophy, Yale University
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GitHub |
YouTube
Email: daniel.grimmer(at)yale.edu
About Me
I am a Postdoctoral Researcher in Philosophy at Yale University. My research trajectory spans across disciplines, beginning with a Physics (Ph.D., Waterloo) and expanding into the Philosophy of Physics (DPhil, Oxford), Cognitive Science, and Artificial Intelligence.
Currently, my work focuses on Evolutionary Epistemology in silico (Recent Talk). Remarkably, the machine learning technique of Meta-Learning can be used to implement an evolutionarily faithful simulation of Darwinian evolution. We can therefore use artificial neural networks to simulate the evolution of our own cognitive factulties, shedding light on the age-old philosophical debate between Nativism and Empiricism. In aide of this program, I have recently derived a suite of advanced optimization algorithms directly from evolutionary first principles (Recent Paper).
Primary Research Interests
1. Artificial Intelligence & Evolutionary Epistemology: The dominant training regime for neural networks is broadly Empiricist: beginning effectively tabula rasa (i.e., random weights and bias) general-purpose methods (e.g., statistics and associations) are then applied to massive amounts of data. Rejecting hand-coded innate structures, the Bitter Lesson says: scale, scale, scale! But meta-learning (understood as a simulation of Darwinian evolution) allows for Scalable Nativism (see this paper). In principle, we can redeploy these (formerly) empiricist methods to nativist ends. Concretely, we can evolve a wide range of different innate structures within neural networks, everything from Kant's categories, to Jung's archetypes, and Chomsky's Universal Grammer.
2. Metaphysics of Space and Time: What if we could remove and replace the topological underpinnings of our spacetime theories just as easily as we can switch between different coordinate systems? I claim that we can by using the ISE Method of topological redescription which I developed in my DPhil thesis. (See this video and this paper.) I claim that these new topological redescription techniques lead us to a conventionalist/neo-Kantian view of spacetime topology which I call the Dynamics-First View of Spacetime Topology (see this paper). For instance, in cases of spacetime-dualities (e.g., AdS-CFT) two different species might evolve radically different spatial intuitions and, relatedly, different ontologies/mereologies. Who then is right about the world's fundamental metaphysics?
3. Measurement in Quantum Field Theory (QFT): How should we model quantum measurement processes which involve quantum fields? How must our characterization of QFT's observables differ from how we characterize the observables of non-relativistic quantum mechanics (NRQM)? Can we model QFT-involved measurement using PVMs and POVMs as we are used to in NRQM? Perhaps surprisingly, we cannot. This gives rise to what I call the Pragmatic QFT Measurement Problem (Paper, Video Abstract).
Selected Papers
| Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles | (arXiv) |
| Daniel Grimmer. | (GitHub Repo) |
| Evolutionary Meta-Learning in Neural Networks as a Neutral Testing Ground for Nativism and Empiricism | PhilSci Archive |
| Daniel Grimmer. |
| Dualities, Quantum Mechanics, and the Uncommon Common Core | BJPS |
| Daniel Grimmer, Enrico Cinti, Rasmus Jaksland. |
| The Pragmatic QFT Measurement Problem and the need for a Heisenberg-like Cut in QFT | Synthese | (arXiv) |
| Daniel Grimmer. | (Vid.Abs.) |