Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

We are Designers Because We Can Abstract

Organised by: Cranfield UniversityDue to the increasing systems complexity, architecture design became an important issue. It gained interest and its importance was framed in three domains: as a way to understand complex systems, to design them, to manage their manufacturing process and to provide long-term rationality. The purpose of this paper is, firstly, to survey the existing definition approaches on architecture. Secondly, we propose a model for architecture design which articulates the potential linkage between two principle concepts: synthesis and abstraction. Our proposal model focuses on abstraction concept and permits an effective top-down design approach. It helps also designers to more respond to issues that characterize architecture design.Mori Seiki – The Machine Tool Compan

Sound Symbolism in Foreign Language Phonological Acquisition

The paper aims at investigating the idea of a symbolic nature of sounds and its implications for in the acquisition of foreign language phonology. Firstly, it will present an overview of universal trends in phonetic symbolism, i.e. non-arbitrary representations of a phoneme by specific semantic criteria. Secondly, the results of a preliminary study on different manifestations of sound symbolism including emotionally-loaded representations of phonemes and other synaesthetic associations shall be discussed. Finally, practical pedagogical implications of sound symbolism will be explored and a number of innovative classroom activities involving sound symbolic associations will be presented

Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation

How do computers and intelligent agents view the world around them? Feature extraction and representation constitutes one the basic building blocks towards answering this question. Traditionally, this has been done with carefully engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is no ``one size fits all'' approach that satisfies all requirements. In recent years, the rising popularity of deep learning has resulted in a myriad of end-to-end solutions to many computer vision problems. These approaches, while successful, tend to lack scalability and can't easily exploit information learned by other systems. Instead, we propose SAND features, a dedicated deep learning solution to feature extraction capable of providing hierarchical context information. This is achieved by employing sparse relative labels indicating relationships of similarity/dissimilarity between image locations. The nature of these labels results in an almost infinite set of dissimilar examples to choose from. We demonstrate how the selection of negative examples during training can be used to modify the feature space and vary it's properties. To demonstrate the generality of this approach, we apply the proposed features to a multitude of tasks, each requiring different properties. This includes disparity estimation, semantic segmentation, self-localisation and SLAM. In all cases, we show how incorporating SAND features results in better or comparable results to the baseline, whilst requiring little to no additional training. Code can be found at: https://github.com/jspenmar/SAND_featuresComment: CVPR201

Permutation Symmetric Critical Phases in Disordered Non-Abelian Anyonic Chains

Topological phases supporting non-abelian anyonic excitations have been proposed as candidates for topological quantum computation. In this paper, we study disordered non-abelian anyonic chains based on the quantum groups $SU(2)_k$, a hierarchy that includes the $\nu=5/2$ FQH state and the proposed $\nu=12/5$ Fibonacci state, among others. We find that for odd $k$ these anyonic chains realize infinite randomness critical {\it phases} in the same universality class as the $S_k$ permutation symmetric multi-critical points of Damle and Huse (Phys. Rev. Lett. 89, 277203 (2002)). Indeed, we show that the pertinent subspace of these anyonic chains actually sits inside the ${\mathbb Z}_k \subset S_k$ symmetric sector of the Damle-Huse model, and this ${\mathbb Z}_k$ symmetry stabilizes the phase.Comment: 13 page

"And thence as far as Archipelago": mapping Marlowe’s "British shore"

No abstract available