BRIDGING THE GAP BETWEEN BIOMECHANICS AND ARTIFICIAL INTELLIGENCE

In contrast to widely researched areas of convergence such as between Artificial Intelligence (AI) and medicine there is minimal evidence of AI in biomechanics for sports. The main focus of this paper is the development of AI coaching systems with a high degree of autonomy that can discover new knowledge from data. This paper relates to areas of AI, biomechanical data, and qualitative analysis of human movement. It first provides an overall rationale for possible AI implementations then reports machine learning related findings from AI golf coaching software and a tennis coaching prototype. A point of view, presented as a scope, is that the future role of AI in sport coaching is about automation, knowledge discovery and enhanced human-like interaction

Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / IRI-921259

Neural network computing using on-chip accelerators

The use of neural networks, machine learning, or artificial intelligence, in its broadest and most controversial sense, has been a tumultuous journey involving three distinct hype cycles and a history dating back to the 1960s. Resurgent, enthusiastic interest in machine learning and its applications bolsters the case for machine learning as a fundamental computational kernel. Furthermore, researchers have demonstrated that machine learning can be utilized as an auxiliary component of applications to enhance or enable new types of computation such as approximate computing or automatic parallelization. In our view, machine learning becomes not the underlying application, but a ubiquitous component of applications. This view necessitates a different approach towards the deployment of machine learning computation that spans not only hardware design of accelerator architectures, but also user and supervisor software to enable the safe, simultaneous use of machine learning accelerator resources. In this dissertation, we propose a multi-transaction model of neural network computation to meet the needs of future machine learning applications. We demonstrate that this model, encompassing a decoupled backend accelerator for inference and learning from hardware and software for managing neural network transactions can be achieved with low overhead and integrated with a modern RISC-V microprocessor. Our extensions span user and supervisor software and data structures and, coupled with our hardware, enable multiple transactions from different address spaces to execute simultaneously, yet safely. Together, our system demonstrates the utility of a multi-transaction model to increase energy efficiency improvements and improve overall accelerator throughput for machine learning applications

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Bell Labs and the ‘neural’ network, 1986–1996

Between 1986 and 1996 researchers at the AT&T Bell Laboratories Adaptive Systems Research Department curated thousands of images of handwritten digits assembled by the United States Postal Service to train and evaluate artificial neural networks. In academic papers and conference literature, and in conversations with the press, Bell Labs researchers, executives and company spokespeople deployed the language of neurophysiology to position the systems as capable of codifying and reproducing feats of perception. Interpretations such as these were pivotal to the formation of brain–computer imaginaries that surrounded the development of the systems, which obscured the institutional infrastructures, clerical and cognitive labour, and the manipulation and maintenance of data on which feats of ‘recognition’ depended. Central to building the group's networks was the development of data sets constructed in consort with the US Postal Service, which arbitrated between the practicality of conducting research and the representation of an extensive catalogue of possible forms and permutations of handwritten digits. These imaginaries, which stressed a likeness with the human brain, were compounded by the promotion of ‘successful applications’ that took place under the AT&T corporate umbrella and with the essential support of US Postal Service workers to correct system errors

The Application of Echo State Networks to Atypical Speech Recognition

Automatic speech recognition (ASR) techniques have improved extensively over the past few years with the rise of new deep learning architectures. Recent sequence-to-sequence models have been shown to have high accuracy by utilizing the attention mechanism, which evaluates and learns the magnitude of element relationships in sequences. Despite being highly accurate, commercial ASR models have a weakness when it comes to accessibility. Current commercial deep learning ASR models find difficulty evaluating and transcribing speech for individuals with unique vocal features, such as those with dysarthria, heavy accents, as well as deaf and hard-of-hearing individuals. Current methodologies for processing vocal data revolve around convolutional feature extraction layers, dulling the sequential nature of the data. Alternatively, reservoir computing has gained popularity for the ability to translate input data to changing network states, which preserves the overall feature complexity of the input. Echo state networks (ESN), a type of reservoir computing mechanism employing a random recurrent neural network, have shown promise in a number of time series classification tasks. This work explores the integration of ESNs into deep learning ASR models. The Listen, Attend and Spell, and Transformer models were utilized as a baseline. A novel approach that used the echo state network as a feature extractor was explored and evaluated using the two models as baseline architectures. The models were trained on 960 hours of LibriSpeech audio data and tuned on various atypical speech data, including the Torgo dysarthric speech dataset and University of Memphis SPAL dataset. The ESN-based Echo, Listen, Attend, and Spell model produced more accurate transcriptions when evaluating on the LibriSpeech test set compared to the ESN-based Transformer. The baseline transformer model achieved a 43.4% word error rate on the Torgo test set after full network tuning. A prototype ASR system was developed to utilize both the developed model as well as commercial smart assistant language models. The system operates on a Raspberry Pi 4 using the Assistant Relay framework

The Baby project: processing character patterns in textual representations of language.

This thesis describes an investigation into a proposed theory of AI. The theory postulates that a machine can be programmed to predict aspects of human behaviour by selecting and processing stored, concrete examples of previously experienced patterns of behaviour. Validity is tested in the domain of natural language. Externalisations that model the resulting theory of NLP entail fuzzy components. Fuzzy formalisms may exhibit inaccuracy and/or over productivity. A research strategy is developed, designed to investigate this aspect of the theory. The strategy includes two experimental hypotheses designed to test, 1) whether the model can process simple language interaction, and 2) the effect of fuzzy processes on such language interaction. Experimental design requires three implementations, each with progressive degrees of fuzziness in their processes. They are respectively named: Nonfuzz Babe, CorrBab and FuzzBabe. Nonfuzz Babe is used to test the first hypothesis and all three implementations are used to test the second hypothesis. A system description is presented for Nonfuzz Babe. Testing the first hypothesis provides results that show NonfuzzBabe is able to process simple language interaction. A system description for CorrBabe and FuzzBabe is presented. Testing the second hypothesis, provides results that show a positive correlation between degree of fuzzy processes and improved simple language performance. FuzzBabe's ability to process more complex language interaction is then investigated and model-intrinsic limitations are found. Research to overcome this problem is designed to illustrate the potential of externalisation of the theory and is conducted less rigorously than previous part of this investigation. Augmenting FuzzBabe to include fuzzy evaluation of non-pattern elements of interaction is hypothesised as a possible solution. The term FuzzyBaby was coined for augmented implementation. Results of a pilot study designed to measure FuzzyBaby's reading comprehension are given. Little research has been conducted that investigates NLP by the fuzzy processing of concrete patterns in language. Consequently, it is proposed that this research contributes to the intellectual disciplines of NLP and AI in general

Neurocomputing systems for auditory processing

This thesis studies neural computation models and neuromorphic implementations of the auditory pathway with applications to cochlear implants and artificial auditory sensory and processing systems. Very low power analogue computation is addressed through the design of micropower analogue building blocks and an auditory preprocessing module targeted at cochlear implants. The analogue building blocks have been fabricated and tested in a standard Complementary Metal Oxide Silicon (CMOS) process. The auditory pre-processing module design is based on the cochlea signal processing mechanisms and low power microelectronic design methodologies. Compared to existing preprocessing techniques used in cochlear implants, the proposed design has a wider dynamic range and lower power consumption. Furthermore, it provides the phase coding as well as the place coding information that are necessary for enhanced functionality in future cochlear implants. The thesis presents neural computation based approaches to a number of signal-processing problems encountered in cochlear implants. Techniques that can improve the performance of existing devices are also presented. Neural network based models for loudness mapping and pattern recognition based channel selection strategies are described. Compared with state—of—the—art commercial cochlear implants, the thesis results show that the proposed channel selection model produces superior speech sound qualities; and the proposed loudness mapping model consumes substantially smaller amounts of memory. Aside from the applications in cochlear implants, this thesis describes a biologically plausible computational model of the auditory pathways to the superior colliculus based on current neurophysiological findings. The model encapsulates interaural time difference, interaural spectral difference, monaural pathway and auditory space map tuning in the inferior colliculus. A biologically plausible Hebbian-like learning rule is proposed for auditory space neural map tuning, and a reinforcement learning method is used for map alignment with other sensory space maps through activity independent cues. The validity of the proposed auditory pathway model has been verified by simulation using synthetic data. Further, a complete biologically inspired auditory simulation system is implemented in software. The system incorporates models of the external ear, the cochlea, as well as the proposed auditory pathway model. The proposed implementation can mimic the biological auditory sensory system to generate an auditory space map from 3—D sounds. A large amount of real 3-D sound signals including broadband White noise, click noise and speech are used in the simulation experiments. The efiect of the auditory space map developmental plasticity is examined by simulating early auditory space map formation and auditory space map alignment with a distorted visual sensory map. Detailed simulation methods, procedures and results are presented