conductor(generic_systems_fields): Phase 3 OCR - 33 frames OCR'd via winsdk in 1.9s
This commit is contained in:
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# OCR Results
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## frame_00001.jpg
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```
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O
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How diverse is intelligence?
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What are the limits?
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How do we find out?
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M. Levin, 10.1002/aisy.202401034
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```
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## frame_00002.jpg
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```
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"Intelligence is a fixed goal with variable means of achieving it."
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— William James
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Does any goal count?
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Are any means allowed?
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Does anything fall outside this definition?
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```
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## frame_00003.jpg
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```
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Biological Theory
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https•J/doiorg/10.1007/s13752Q505234
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ORIGINAL ARTICLE
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Mind Everywhere: A Framework for Conceptualizing Goal-
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Directedness in Biology and Other Domains—Part One
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Michael Levin12(tY • David B. Resnik
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Received: 31 March 2025 / Accepted: 22 November 2025
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@Konrad Lorenz Institute for Evolution and Cognition Research 2025
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Abstract
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What makes a system—evolved, engineered, or hybrid—describable by teleological and mentalistic terms such as intel-
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ligent, goal-directed, cognitive, and intentional? In this two-part article, we review classical thought on teleology in the
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life sciences and defend a new approach to goal-directedness that stems from an emerging field—diverse intelligence. This
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field seeks to characterize what all active agents, regardless of their composition or provenance, have in common. Our
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approach emphasizes: (l) empirical testability (not philosophical commitments to linguistic categories). (2) fecundity in
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discovery of new capabilities (not just reductive mechanistic explanations of results after they are made, but worldviews
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that facilitate and enable novel research), (3) operationalization of terminology by reference to conceptual and empiri-
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cal toolkits shown to be effective for a given system (cognitive and teleological claims are really hypotheses of optimal
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interaction protocols), and (4) continuity of human goal-directedness with our unicellular origins (which implies a need
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for models of scaling of cognition). In Part One, we review historical and contemporary debates about teleology in biol-
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```
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## frame_00004.jpg
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```
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Physics of Life Reviews 47 (2023) 35-62
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Contents lists available at ScienceDirect
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Physics of Life Reviews
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journal homepage: www.elsevier.com/locate/plrev
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ELSEVIER
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Review
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Path integrals, particular kinds, and strange things
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a.b.c.*
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Dalton A.R. Sakthivadivel e
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Karl Friston a-c, Lancelot Da Costa
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Conor Heinsc f g, Grigorios A. Pavliotis b, Maxwell Ramstead LC, Thomas Parra
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The free energy principle (FEP) describes a simple relationship between the dynamics of a
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random dynamical system and a description of its behaviour as engaging in inference. The FEP
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originated in neuroscience as an attempt to describe brain function and behaviour (Friston et al.
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2006) and has since been extended to describe several kinds of things in the biological and
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physical realms (Friston 2013; Friston et al. 2021) through a special kind of mechanics—a
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Bayesian mechanics—that shares the same foundations with quantum, statistical, and classical
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mechanics (Friston 2019; Friston et al. 2022). This paper is part of a series of technical papers
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describing the FEP in progressively simpler and more qualified terms (Friston, 2013; Friston,
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2019; Friston et al., 2022).
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```
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## frame_00005.jpg
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```
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Inert particles
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with no acuve states
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Active particles
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with active states
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Conservative particles
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with classical dynanucs
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Strange particles
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with hidden active states
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External states
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s
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Sensory states
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s
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a
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q, (11)
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Active states
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Internal states
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Ihi - v p
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A causal sink .
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violates Newton's
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3rd Law
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Fig. 2
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```
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## frame_00006.jpg
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```
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Physics first, intuitions later ...
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How do we guarantee that this interaction
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respects all physical symmetries?
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We want the generic case, that describes
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any interacting systems, regardless of
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scale or structure.
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```
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## frame_00007.jpg
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```
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Answer: Start with the generic case
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U, an isolated system
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(no environment,
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no interaction)
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The internal dynamics "PU must respect Newton's 3rd Law everywhere.
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No sources, no sinks — because U has no environment.
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QU conserves momentum, energy, information (so is unitary).
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```
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## frame_00008.jpg
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```
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An isolated (no environment) U is a productive assumption.
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This gives us conservation of information, so unitarity, so linearity.
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Hence is a linear operator on the state space , a Hilbert space.
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In background time t, we can write TU(t) = exp((-i/h)Hu(t)), where
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h is an action and HU is a Hamiltonian (energy) linear operator on YLJ.
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A finite value of h 4-+ no singularities.
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This is quantum theory (QT). "Isolation is all you need."
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```
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## frame_00009.jpg
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```
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Because everything in sight is linear, we can do a linear decomposition.
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Boundary "B
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U =AÄ, HU = HA + HÄ + HAÄ. HAÄ is the interaction between A and Ä.
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This is the generic, symmetry-preserving interaction we wanted.
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```
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## frame_00010.jpg
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```
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Without loss of generality,
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HAÄ = kBTkEINßk(oz, zki)
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where k = A or Ä, Oz is a
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z-spin operator, and
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System A
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zki is a local z reference
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frame.
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This makes "B an N-qubit
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holographic screen.
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4
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Prepare
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Measure
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(Oz, z 1)
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Measure
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Prepare
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Prepare
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Measure
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Prepare
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Measure
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Measure
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(Oz, z 2)
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Prepare
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Measure
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(Oz, z N)
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Prepare
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System Ä
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(Oz, Z
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Boundary
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```
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## frame_00011.jpg
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```
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HAÄ tells of how A and Ä act on each other.
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We want it to tell us how they influence each other.
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These are the same if but only if A and Ä have conditionally-
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independent states.
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So we need to require that II...J> = IAÄ> = This is state
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separability = absence of entanglement.
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Separability requires weak (or sparse) coupling. Formally, the
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dimension of HAÄ is small: dim(HAÄ) << dim(HA), dim(HÄ).
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Intuitively, the evolutions of A and Ä are almost independent.
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```
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## frame_00012.jpg
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```
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Minimal physics .....................+ FEP
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If A and Ä are separable, dim(HAÄ) << dim(HA), dim(HÄ):
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• HAÄ fully describes information exchange between A and Ä;
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• The boundary "B functions as a Markov Blanket;
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• Variational free energy (VFE) measures interaction strength;
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• Minimizing VFE is keeping HAÄ weak while allowing
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thermodynamic exchange;
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• Predictability = constrained interaction.
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A and Ä maintain their identities as distinct systems only while
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their boundary "B remains intact — no rips, no explosions!
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```
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## frame_00013.jpg
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```
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"Intelligence is a fixed goal with variable means of achieving it."
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— William James
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Does any goal count?
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>> There's always at least one: continuing to exist as an entity.
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Are any means allowed?
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>> Whatever the internal dynamics HA and HÄ are capable of.
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Does anything fall outside this definition?
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>> No, it's completely generic.
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```
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## frame_00014.jpg
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```
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We have a generic, symmetry-preserving description.
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It is consistent with and even explains the FEP.
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But is the behavior that counts as "intelligent" interesting?
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Are there limits on what kinds of systems can exhibit interesting
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behavior?
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How do we find out?
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```
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## frame_00015.jpg
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```
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What is interesting behavior?
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• Surprising, unpredictable in practice
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• Only approximately predictable (only predictable if coarse-grained)
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• Unpredictable in principle
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• Learns from experience
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• Memory-dependent
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• Context-dependent
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• Distributions of outcome values violate Kolmogorov, outcome
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probabilities undefined in principle
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```
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## frame_00016.jpg
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```
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Operationally,
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State transition probabilities derived from finite observations
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do not converge to predictive adequacy.
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Induction from finite data doesn't work.
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19th Century "mechanical" expectations are violated.
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We know Life violates them. What else does?
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```
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## frame_00017.jpg
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```
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Hint: Moore's theorem (1956):
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Finite input-output experiments cannot uniquely determine the
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"machine table" (internal state-transition probabilities) of a generic
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classical Black Box.
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Example: Box with an internal clock, e.g. time bomb.
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```
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## frame_00018.jpg
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```
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Hint: Conway-Kochen "free will" theorem (2006, 2009):
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Special relativity and quantum theory together rule out local
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(past light cone) determinism.
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"If experimenters make choices, electrons do too."
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```
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## frame_00019.jpg
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```
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Hint: QT from singularity removal (Tipler, 2014):
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The simplest formal removal of singularities from classical
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physics reproduces Bohm's "quantum potential."
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(N. Gisin: Newton-Laplace physics wasn't singular because
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it wasn't local. Einstein introduced strict locality.)
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```
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## frame_00020.jpg
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```
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These all suggest:
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Generic systems (can) display interesting behavior.
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How do we make this precise?
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How do we understand it?
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How can we use it to explain and/or predict?
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```
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## frame_00021.jpg
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```
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The setting:
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A's measurements
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and action choices
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are computed by HA
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(In FEP, A's GM).
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Observations
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Boundary 'B
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and actions (1/0)
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live here, on "B.
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dim(HAÄ) << dim(HA), dim(HÄ)
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Inputs and outputs are much less complex than the computations that
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generate them.
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```
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## frame_00022.jpg
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```
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This dimensionality/complexity difference immediately tells us:
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Recurrence of I'B> does not imply recurrence of IÄ>.
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Behavior generically depends on "hidden" internal states, i.e. on
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Ä's memory or internal context.
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We can represent this formally as an internal "geometric" or Berry phase.
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Chris Fuchs: all physical systems have "interiority."
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```
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## frame_00023.jpg
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```
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Geometric phase changes are
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introduced by transports along
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curves in state spaces. These
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are "holonomy" operations.
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E.g. Blattner (2026):
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1
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7
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2
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3
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6
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5
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4
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Hidden regenerative state in planarians:
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A geometric model of bioelectric memory using Tangential
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Action Spaces
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Nlarcel Blattner
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```
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## frame_00024.jpg
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```
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Why is this important?
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Non-trivial geometric phase dependence — non-trivial holonomy — is
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a sufficient resource for universal quantum computation.
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It constructs a map: I'B> 1B IÄ' > for arbitrary I'B'>.
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• Zanardi, P. and Rasetti, M. Holonomic quantum computation. Phys. Lett.
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A 264 (1999), 94-99.
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• Pachos, J. and Zanardi, P. Quantum holonomies for quantum computing.
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Int. J. Mod. Phys. B 15 (2001), 1257-1286.
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```
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## frame_00025.jpg
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```
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What is physically implemented computation?
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Input ....................+ Output
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Input -.......................+ Output
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"P(t) implements fon Input if and only if these diagrams commute.
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The "interpretation" is a projection/inverse embedding: = 8-1.
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```
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## frame_00026.jpg
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```
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Why is this important?
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Embeddings are injective: one to many.
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Polycomputation is generic.
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Indeed, managing thermodynamic flow requires that "informative"
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sector projections are proper samples of We never look at
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everything the computer is doing.
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```
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## frame_00027.jpg
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```
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We can think of computation as "scattering" in data-structure space
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(CF et al., 2509.19772).
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An ideal classical computer implementing an algorithm for f looks like:
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"Ready" state
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Input
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Output
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"Ready" state
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This is a useful coarse-graining, but the observed "Ready state"
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(a proper projection of does not pick out a unique machine state
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IÄ>. This is Moore's Theorem from 1956, updated.
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```
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## frame_00028.jpg
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```
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Even a classical OS
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accumulates internal
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state changes as it
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executes.
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"Side projects" are
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inevitable in generic
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systems.
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' 'Ready"
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State
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Input 1
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Input 2
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Input N
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Output 1
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Output 2
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Output N
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Final
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state
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```
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## frame_00029.jpg
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```
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We can also represent geometric phase changes as reference frame
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changes:
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(Oz, )
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Holonomy
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If Ä exhibits non-trivial holonomy, how it acts on 'B will change.
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```
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## frame_00030.jpg
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```
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Why is this important?
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(Oz, ) and (Oz, don't commute! (Oz, )( Oz,
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Non-commuting QRFs generate non-causal context dependence.
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In this case, joint probability distributions on observational
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outcomes are undefined (violate Kolmogorov).
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Fields and Glazebrook, Int. J. Theor. Phys. 62 (2023) 159
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```
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## frame_00031.jpg
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```
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All of these "interesting" kinds of behavior are generic!
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• Surprising, unpredictable in practice
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• Only approximately predictable (only predictable if coarse-grained)
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• Unpredictable in principle
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• Learns from experience
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• Memory-dependent
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• Context-dependent
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• Distributions of outcome values violate Kolmogorov, outcome
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probabilities undefined in principle
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They all result from separability: big systems with small boundaries.
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```
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## frame_00032.jpg
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```
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O
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QT provides a precise,
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general, strongly empirically
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validated foundation for
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Diverse Intelligence.
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It tells us that intelligence
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and persistent observability
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||||
go hand in hand.
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||||
M. Levin, 10.1002/aisy.202401034
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||||
```
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## frame_00033.jpg
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```
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Thank you
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Questions?
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Thanks to Jim Glazebrook.
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```
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||||
@@ -0,0 +1,2 @@
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Phase 2 Keyframes for C:\projects\manual_slop\conductor\tracks\video_analysis_generic_systems_fields_20260621\artifacts\video.mp4
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OK: kept 33 frames
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@@ -0,0 +1,2 @@
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Phase 3 OCR for C:\projects\manual_slop\conductor\tracks\video_analysis_generic_systems_fields_20260621\artifacts\frames (winsdk)
|
||||
OK: OCR'd 33 frames in 1.9s
|
||||
Reference in New Issue
Block a user