The design of a neural network compiler

Computer simulation is a flexible and economical way for rapid prototyping and concept evaluation with Neural Network (NN) models. Increasing research on NNs has led to the development of several simulation programs. Not all simulations have the same scope. Some simulations allow only a fixed network model and some are more general. Designing a simulation program for general purpose NN models has become a current trend nowadays because of its flexibility and efficiency. A proper programming language specifically for NN models is preferred since the existing high-level languages such as C are for NN designers from a strong computer background. The program translations for NN languages come from combinations which are either interpreter and/or compiler. There are also various styles of programming languages such as a procedural, functional, descriptive and object-oriented. The main focus of this thesis is to study the feasibility of using a compiler method for the development of a general-purpose simulator - NEUCOMP that compiles the program written as a list of mathematical specifications of the particular NN model and translates it into a chosen target program. The language supported by NEUCOMP is based on a procedural style. Information regarding the list of mathematical statements required by the NN models are written in the program. The mathematical statements used are represented by scalar, vector and matrix assignments. NEUCOMP translates these expressions into actual program loops. NEUCOMP enables compilation of a simulation program written in the NEUCOMP language for any NN model, contains graphical facilities such as portraying the NN architecture and displaying a graph of the result during training and finally to have a program that can run on a parallel shared memory multi-processor system

Residuation and guarded rules for constraint logic programming

A major difficulty with logic programming is combinatorial explosion: since goals are solved with possibly indeterminate (i.e., branching) reductions, the resulting search trees may grow wildly. Constraint logic programming systems try to avoid combinatorial explosion by building in strong determinate (i.e., non-branching) reduction in the form of constraint simplification. In this paper we present two concepts, residuation and guarded rules, for further strengthening determinate reduction. Both concepts apply to constraint logic programming in general and yield an operational semantics that coincides with the declarative semantics. Residuation is a control strategy giving priority to determinate reductions. Guarded rules are logical consequences of programs adding otherwise unavailable determinate reductions

Hierarchical Temporal Memory Cortical Learning Algorithm for Pattern Recognition on Multi-core Architectures

Strongly inspired by an understanding of mammalian cortical structure and function, the Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a promising new approach to problems of recognition and inference in space and time. Only a subset of the theoretical framework of this algorithm has been studied, but it is already clear that there is a need for more information about the performance of HTM CLA with real data and the associated computational costs. For the work presented here, a complete implementation of Numenta\u27s current algorithm was done in C++. In validating the implementation, first and higher order sequence learning was briefly examined, as was algorithm behavior with noisy data doing simple pattern recognition. A pattern recognition task was created using sequences of handwritten digits and performance analysis of the sequential implementation was performed. The analysis indicates that the resulting rapid increase in computing load may impact algorithm scalability, which may, in turn, be an obstacle to widespread adoption of the algorithm. Two critical hotspots in the sequential code were identified and a parallelized version was developed using OpenMP multi-threading. Scalability analysis of the parallel implementation was performed on a state of the art multi-core computing platform. Modest speedup was readily achieved with straightforward parallelization. Parallelization on multi-core systems is an attractive choice for moderate sized applications, but significantly larger ones are likely to remain infeasible without more specialized hardware acceleration accompanied by optimizations to the algorithm

Programming tools for intelligent systems

Les outils de programmation sont des programmes informatiques qui aident les humains à programmer des ordinateurs. Les outils sont de toutes formes et tailles, par exemple les éditeurs, les compilateurs, les débogueurs et les profileurs. Chacun de ces outils facilite une tâche principale dans le flux de travail de programmation qui consomme des ressources cognitives lorsqu’il est effectué manuellement. Dans cette thèse, nous explorons plusieurs outils qui facilitent le processus de construction de systèmes intelligents et qui réduisent l’effort cognitif requis pour concevoir, développer, tester et déployer des systèmes logiciels intelligents. Tout d’abord, nous introduisons un environnement de développement intégré (EDI) pour la programmation d’applications Robot Operating System (ROS), appelé Hatchery (Chapter 2). Deuxièmement, nous décrivons Kotlin∇, un système de langage et de type pour la programmation différenciable, un paradigme émergent dans l’apprentissage automatique (Chapter 3). Troisièmement, nous proposons un nouvel algorithme pour tester automatiquement les programmes différenciables, en nous inspirant des techniques de tests contradictoires et métamorphiques (Chapter 4), et démontrons son efficacité empirique dans le cadre de la régression. Quatrièmement, nous explorons une infrastructure de conteneurs basée sur Docker, qui permet un déploiement reproductible des applications ROS sur la plateforme Duckietown (Chapter 5). Enfin, nous réfléchissons à l’état actuel des outils de programmation pour ces applications et spéculons à quoi pourrait ressembler la programmation de systèmes intelligents à l’avenir (Chapter 6).Programming tools are computer programs which help humans program computers. Tools come in all shapes and forms, from editors and compilers to debuggers and profilers. Each of these tools facilitates a core task in the programming workflow which consumes cognitive resources when performed manually. In this thesis, we explore several tools that facilitate the process of building intelligent systems, and which reduce the cognitive effort required to design, develop, test and deploy intelligent software systems. First, we introduce an integrated development environment (IDE) for programming Robot Operating System (ROS) applications, called Hatchery (Chapter 2). Second, we describe Kotlin∇, a language and type system for differentiable programming, an emerging paradigm in machine learning (Chapter 3). Third, we propose a new algorithm for automatically testing differentiable programs, drawing inspiration from techniques in adversarial and metamorphic testing (Chapter 4), and demonstrate its empirical efficiency in the regression setting. Fourth, we explore a container infrastructure based on Docker, which enables reproducible deployment of ROS applications on the Duckietown platform (Chapter 5). Finally, we reflect on the current state of programming tools for these applications and speculate what intelligent systems programming might look like in the future (Chapter 6)

A neural compiler

AbstractThis paper describes a neural compiler. The input of the compiler is a PASCAL Program. The compiler produces a neural network that computes what is specified by the PASCAL program. The compiler generates an intermediate code called cellular code

FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)

This thesis reviews and discusses certain techniques from the domain of (Music) Information Retrieval, in particular some general data mining algorithms. It also describes their specific adaptations for use as building blocks in the CACE4 software application. The use of Augmented Transition Networks (ATN) from the field of (Music) Information Retrieval is, to a certain extent, adequate as long as one keeps the underlying tonal constraints and rules as a guide to understanding the structure one is looking for. However since a large proportion of algorithmic music, including music composed by the author, is atonal, tonal constraints and rules are of little use. Analysis methods from Hierarchical Clustering Techniques (HCT) such as k-means and Expectation-Maximisation (EM) facilitate other approaches and are better suited for finding (clustered) structures in large data sets. ART2 Neural Networks (Adaptive Resonance Theory) for example, can be used for analysing and categorising these data sets. Statistical tools such as histogram analysis, mean, variance as well as correlation calculations can provide information about connections between members in a data set. Altogether this provides a diverse palette of usable data analysis methods and strategies for creating algorithmic atonal music. Now acting as (software) strategy tools, their use is determined by the quality of their output within a musical context, as demonstrated when developed and programmed into the Computer Assisted Composition Environment: CACE4. Music Information Retrieval techniques are therefore inverted: their specific techniques and associated methods of Information Retrieval and general data mining are used to access the organisation and constraints of abstract (non-specific musical) data in order to use and transform it in a musical composition

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence