Shimon Edelman

The proponents of machine consciousness predicate the mental life of a machine, if any, exclusively on its formal, organizational structure, rather than on its physical composition. Given that matter is organized on a range of levels in time and space, this generic stance must be further constrained by a principled choice of levels on which the posited structure is supposed to reside. Indeed, not only must the formal structure fit well the physical system that realizes it, but it must do so in a manner that is determined by the system itself, simply because the mental life of a machine cannot be up to an external observer. To illustrate just how tall this order is, we carefully analyze the scenario in which a digital computer simulates a network of neurons. We show that the formal correspondence between the two systems thereby established is at best partial, and, furthermore, that it is fundamentally incapable of realizing both some of the essential properties of actual neuronal systems and some of the fundamental properties of experience. Our analysis suggests that, if machine consciousness is at all possible, conscious experience can only be instantiated in a class of machines that are entirely different from digital computers, namely, time-continuous, open, analog dynamical systems.

A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must satisfy for it to constitute not just a sufficient but a necessary, and therefore naturalistic and intrinsic, explanation of qualia. We show that a common assumption behind many neurocomputational theories of the mind, according to which mind states can be formalized solely in terms of instantaneous vectors of activities of representational units such as neurons, does not meet the requisite conditions, in part because it relies on inactive units to shape presently experienced qualia and implies a homogeneous representation space, which is devoid of intrinsic structure. We then sketch a naturalistic computational theory of qualia, which posits that experience is realized by dynamical activity-space trajectories (rather than points) and that its richness is measured by the representational capacity of the trajectory space in which it unfolds.

Scientific theories of consciousness identify its contents with the spatiotemporal structure of neural population activity. We follow up on this approach by stating and motivating Dynamical Emergence Theory, which defines the amount and structure of experience in terms of the intrinsic topology and geometry of a physical system’s collective dynamics. Specifically, we posit that distinct perceptual states correspond to coarse-grained macrostates reflecting an optimal partitioning of the system’s state space—a notion that aligns with several ideas and results from computational neuroscience and cognitive psychology. We relate DET to existing work, offer predictions for empirical studies, and outline future research directions.

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.

To the extent that the performance of embodied and situated cognitive agents is predicated on fore- thought;such agents must remember; and learn from; the past to predict the future. In complex; non-stationaryenvironments; such learning is facilitated by an intrinsic motivation to seek novelty. A significant part of anagent’s identity is thus constituted by its remembered distilled cumulative life experience; which the agent isdriven to constantly expand. The combination of the drive to novelty with practical limits on memorycapacity posits a problem. On the one hand; because novelty seekers are unhappy when bored; merelyreliving past positive experiences soon loses its appeal: happiness can only be attained sporadically; via anopen-ended pursuit of new experience. On the other hand; because the experiencer’s memory is finite;longevity and continued novelty; taken together; imply eventual loss of at least some of the stored content;and with it a disruption of the constructed identity. In this essay; I examine the biobehavioral and cognitive-computational circumstances that give rise to this problem and explore its implications for the humancondition.

We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learning structured knowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm represents sentences as paths on a graph whose vertices are words. Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are represented by trees composed of significant patterns and their associated equivalence classes. An input module allows the algorithm to be subjected to a standard test of English as a Second Language profi- ciency. The results are encouraging: the model attains a level of performance considered to be “intermediate” for 9th-grade students, despite having been trained on a corpus containing transcribed speech of parents directed to small children

The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of functions in perception (vision, hearing, olfaction) and action (motor control). More recently, these principles have been applied to the analysis of cognitive tasks that require dealing with structured information, such as visual scene understanding and analogical reasoning. Insofar as language relies on cognition-general principles and mechanisms, it should be possible to capitalize on the recent advances in the computational study of cognition by extending its methods to linguistics.

We describe a method for automatic word sense disambiguation using a text corpus and a machine- readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method performs well, and can learn even from very sparse training data.

Nearest-neighbor correlation-based similarity computation in the space of outputs of complex-type receptive elds can support robust recognition of 3D objects. Our experiments with four collections of objects resulted in mean recognition rates between 84% and 94%, over a 40 40 range of viewpoints, centered on a stored canonical view and related to it by rotations in depth. This result has interesting implications for the design of a front end to an arti cial object recognition system, and for the understanding of the faculty of object recognition in primate vision

It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined concept of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in multidimensional scaling. The present preliminary study appears to indicate that this concept of representation is computationally viable, and is compatible with psychological and neurobiological data

The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic structures from corpus data. We extend this principle by applying it recursively, and by using mutual information for estimating category coherence. The resulting model learns, in an unsupervised fashion, highly structured, distributed representations of syntactic knowledge from corpora. It also exhibits promising behavior in tasks usually thought to require representations anchored in a grammar, such as systematicity.

differentiaily rated pairwise similarity when confronted with two pairs of objects, each revolving in a separate window on a computer screen. Subject data were pooled using individually weighted MDS (ref. 11; in all the experiments, the solutions were consistent among subjects). In each trial, the subject had to select among two pairs of shapes the one consisting of the most similar shapes. The subjects were allowed to respond at will; most responded within 10 sec. Proximity (that is, perceived similarity) tables derived from the judgments were processed to verify their degree of transitivity (4% of all triplets were found intransitive) and then submitted to MDS. In the long-term memory (LTM) variant of this experiment, the subjects were first trained to associate a label (a three-letter nonsensical string, such as "BON" or "POM") with each object and then carried out the pairs of pairs comparison task from memory, prompted by the object labels rather than by the objects themselves. Six subjects participated in each of the two LTM experiments (Star and Triangle). The subjects were taught each shape in a separate session and had to discriminate between that shape and six similar nontargets from various viewpoints. Training continued until the recognition rate reached 90%, over a period of several days. The subjects were never exposed to more than one target in one session and were not told the ultimate purpose of the experiment. After 2 to 3 days of rest, they were tested with questions such as: "is the BON more similar to POM than TOC to ROX?", for all pairs of pairs of stimuli. In the LTM experiments, 8% of the..

The statistical structure of a class of objects such as human faces can be exploited to recognize familiar faces from novel viewpoints and under variable illumination conditions. We present computational and psychophysical data concerning the extent to which class-based learning transfers or generalizes within the class of faces. We rst examine the computational prerequisite for generalization across views of novel faces, namely, the similarity of di erent faces to each other. We next describe two computational models which exploit the similarity structure of the class of faces. The performance of these models constrains hypotheses about the nature of face representation in human vision, and supports the notion that human face processing operates in a class-based fashion. Finally, we relate the computational data to well-established ndings in the human memory literature concerning the relationship between the typicality and recognizability of faces.

Construction-based approaches to syntax (Croft, 2001; Goldberg, 2003) posit a lexicon populated by units of various sizes, as envisaged by (Langacker, 1987). Constructions may be specified completely, as in the case of simple morphemes or idioms such as take it to the bank, or partially, as in the expression what’s X doing Y?, where X and Y are slots that admit fillers of particular types (Kay and Fillmore, 1999). Constructions offer an intriguing alternative to traditional rule-based syntax by hinting at the extent to which the complexity of language can stem from a rich repertoire of stored, more or less entrenched (Harris, 1998) representations that address both syntactic and semantic issues, and encompass, in addition to general rules, “totally idiosyncratic forms and patterns of all intermediate degrees of generality” (Langacker, 1987, p.46). Because constructions are by their very nature language-specific, the question of acquisition in Construction Grammar is especially poignant. We address this issue by offering an unsupervised algorithm that learns constructions from raw corpora.

Converging evidence from anatomical studies (Maunsell, 1983) and functional analyses (Hubel & Wisesel, 1968) of the nervous system suggests that the feed-forward pathway of the mammalian perceptual system follows a largely hierarchic organization scheme. This may be because hierarchic structures are intrinsically more viable and thus more likely to evolve (Simon, 2002). But it may also be because objects in our environment have a hierarchic structure and the perceptual system has evolved to match it. We conducted a behavioral experiment to investigate the effect of the degree of hierarchy of the generative probabilistic structure in categorization. We generated one set of stimuli using a hierarchic underlying probability distribution, and another set according to a non-hierarchic one. Participants were instructed to categorize these images into one of the two possible categories a. Our results suggest that participants perform more accurately in the case of hierarchically structured stimuli.

We examined the role of fitness, commonly assumed without proof to be conferred by the mastery of language, in shaping the dynamics of language evolution. To that end, we introduced island migration (a concept borrowed from population genetics) into the shared lexicon model of communication (Nowak et al., 1999). The effect of fitness linear in language coherence was compared to a control condition of neutral drift. We found that in the neutral condition (no coherence-dependent fitness) even a small migration rate – less than 1% – suffices for one language to become dominant, albeit after a long time. In comparison, when fitness-based selection is introduced, the subpopulations stabilize quite rapidly to form several distinct languages. Our findings support the notion that language confers increased fitness. The possibility that a shared language evolved as a result of neutral drift appears less likely, unless migration rates over evolutionary times were extremely small.

We compare our model of unsupervised learning of linguistic structures, ADIOS [1, 2, 3], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction Grammar in its general philosophy (e.g., in its reliance on structural generalizations rather than on syntax projected by the lexicon, as in the current generative theories), and the Tree Adjoining Grammar in its computational characteristics (e.g., in its apparent affinity with Mildly Context Sensitive Languages). The representations learned by our algorithm are truly emergent from the (unannotated) corpus data, whereas those found in published works on cognitive and construction grammars and on TAGs are hand-tailored. Thus, our results complement and extend both the computational and the more linguistically oriented research into language acquisition. We conclude by suggesting how empirical and formal study of language can be best integrated.