Visual re-identification of individual objects: a core problem for organisms and AI

2016

Systems

Self Cite

Human beings do not observe the world from the outside, but rather are fully embedded in it. The sciences, however, often give the observer both a "god's eye" perspective and substantial a priori knowledge. Motivated by W. Ross Ashby's statement, "the theory of the Black Box is merely the theory of real objects or systems, when close attention is given to the question, relating object and observer, about what information comes from the object, and how it is obtained" (Introduction to Cybernetics, 1956, p. 110), I develop here an alternate picture of the world as a black box to which the observer is coupled. Within this framework I prove purely-classical analogs of the "no-go" theorems of quantum theory. Focussing on the question of identifying macroscopic objects, such as laboratory apparatus or even other observers, I show that the standard quantum formalism of superposition is required to adequately represent the classical information that an observer can obtain. I relate these results to supporting considerations from evolutionary biology, cognitive and developmental psychology, and artificial intelligence.

Section: Cognitive Science and Aimentioning

confidence: 99%

Building the Observer into the System: Toward a Realistic Description of Human Interaction with the World

2016

Systems

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“…The roles of complementary information flows at all scales, from on-center/off-surround networks to the dorsal and ventral attention systems to the interplay of memory and prediction that constructs object histories, exemplify such duality. By taking object identities and object persistence for granted, AI systems have largely neglected the problem of object re-identification that lies at the heart of the frame problem (Fields, 2013(Fields, , 2016. Taking this problem and the dual organization required to solve it into account suggests reconceptualizations of learning and memory as overarching dual processes.…”

mentioning

confidence: 99%

A mosaic of Chu spaces and Channel Theory II: applications to object identification and mereological complexity

Journal of Experimental & Theoretical Artificial Intelligen

Glazebrook

2018

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Chu Spaces and Channel Theory are well established areas of investigation in the general context of category theory. We review a range of examples and applications of these methods in logic and computer science, including Formal Concept Analysis, distributed systems and ontology development. We then employ these methods to describe human object perception, beginning with the construction of uncategorized object files and proceeding through categorization, individual object identification and the tracking of object identity through time. We investigate the relationship between abstraction and mereological categorization, particularly as these affect object identity tracking. This we accomplish in terms of information flow that is semantically structured in terms of local logics, while at the same time this framework also provides an inferential mechanism towards identification and perception. We show how a mereotopology naturally emerges from the representation of classifications by simplicial complexes, and briefly explore the emergence of geometric relations and interactions between objects.The first part of the paper addresses the initial aim of the tool assembly. We begin by defining and reviewing some of the basic properties of Chu spaces in §2. Although Chu spaces have been traditionally applied to fields such as those listed above, they also have a number of other significant applications of interest here. How Chu spaces can be implemented within Formal Concept Analysis and Domain Theory (e.g. to represent information systems and approximable concepts following Hitzler and Zhang (2004); Krötzsch, Hitzler and Zhang (2005); Scott (1982); Zhang and Shen (2006)) is reviewed in §3. In §4 we discuss representations of spaces (and representations by spaces), spatial coarse-graining and finite sampling of information (Gratus and Porter, 2006; Sorkin, 1991a); we then review the representation of sampled information by simplicial complexes constructed "above" the sampled space in §5. The following two sections, §6 and §7 establish a similar working account of Channel Theory. We survey a number of motivating examples and applications, including Distributed Systems (The category-theoretic concepts of cocone and colimit (e.g. Awodey (2010)) naturally arise in both Chu space and Channel Theory descriptions; we review these concepts in §8 with illustrative examples.The second part of the paper presents new results. We begin in §9 with brief reviews of perception, categorization and attention as neurocognitive processes and of multi-layer recurrent network models (e.g. Friston (2010); Grossberg (2013)) of these processes. In §10, we re-describe perception and categorization, using the Chu space and Channel Theory tools assembled in the first part, in a way that makes explicit the dualities between dynamic and static properties, individuals and categories, and states and events. We capture these dualities in a "cone-cocone diagram" that formalizes the inferential steps required to link object tokens together to produce a "...

“…Sets of observables and specified outcome values are therefore, from this perspective, structured knowledge; observers are systems-in this case, cognitive systems-capable of deploying such knowledge. Philosophers and, more recently, psychologists have expended considerable effort characterizing this categorical knowledge, investigating its implementation, and determining how it is used to identify and then re-identify objects over time (for reviews, see [70][71][72][73][74]); we will return to the question of implementation in Section 5 below.…”

Section: What Is Observed?mentioning

confidence: 99%

Sciences of Observation

2018

Philosophies

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Multiple sciences have converged, in the past two decades, on a hitherto mostly unremarked question: what is observation? Here, I examine this evolution, focusing on three sciences: physics, especially quantum information theory, developmental biology, especially its molecular and “evo-devo” branches, and cognitive science, especially perceptual psychology and robotics. I trace the history of this question to the late 19th century, and through the conceptual revolutions of the 20th century. I show how the increasing interdisciplinary focus on the process of extracting information from an environment provides an opportunity for conceptual unification, and sketch an outline of what such a unification might look like.