On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity

Breaking Implicit Assumptions of Physical Delay-Feedback Reservoir Computing

The Reservoir Computing (RC) paradigm is a supervised machine learning scheme using the natural computational ability of dynamical systems. Such dynamical systems incorporate time delays showcasing intricate dynamics. This richness in dynamics, particularly the system's transient response to external stimuli makes them suitable for RC. A subset of RCs, Delay-Feedback Reservoir Computing (DFRC), is distinguished by its unique features: a system that consists of a single nonlinear node and a delay-line, with `virtual' nodes defined along the delay-line by time-multiplexing procedure of the input. These characteristics render DFRC particularly useful for hardware integration. In this thesis, the aim is to break the implicit assumptions made in the design of physical DFRC based on Mackey-Glass dynamical system. The first assumption we address is the performance of DFRC is not affected by the attenuation in physcial delay-line as the nodes defined along it are 'virtual'. However, our experimental results contradict this. To mitigate the impact of losses along the delay line, we propose a methodology `Devirtualisation', which describes the procedure of directly tapping into the delay lines at the position of a `virtual' node, rather than at the delay line's end. It trade-offs the DFRC system's read-out frequency and the quantity of output lines. Masking plays a crucial role in DFRC, as it defines `virtual' nodes along the delay-line. The second assumption is that the mask used should randomly generated numbers uniformly distributed between [-u,u]. We experimentally compare Binary Weight Mask (BWM) vs. Random Weight Mask (RWM) under different scenarios; and investigate the randomness of BWM signal distribution's impact. The third implicit assumption is that, DFRC is designed to solve time series prediction tasks involving a single input and output with no external feedback. To break this assumption, we propose two approaches to mix multi-input signals into DFRC; to validate these approaches, a novel task for DFRC that inherently necessitates multiple inputs: the control of a forced Van der Pol oscillator system, is proposed

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Integration of Satellite Data, Physically-based Model, and Deep Neural Networks for Historical Terrestrial Water Storage Reconstruction

Terrestrial water storage (TWS) is an essential part of the global water cycle. Long-term monitoring of observed and modeled TWS is fundamental to analyze droughts, floods, and other meteorological extreme events caused by the effects of climate change on the hydrological cycle. Over the past several decades, hydrologists have been applying physically-based global hydrological model (GHM) and land surface model (LSM) to simulate TWS and the water components (e.g., groundwater storage) composing TWS. However, the reliability of these physically-based models is often affected by uncertainties in climatic forcing data, model parameters, model structure, and mechanisms for physical process representations. Launched in March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission exclusively applies remote sensing techniques to measure the variations in TWS on a global scale. The mission length of GRACE, however, is too short to meet the requirements for analyzing long-term TWS. Therefore, lots of effort has been devoted to the reconstruction of GRACE-like TWS data during the pre-GRACE era. Data-driven methods, such as multilinear regression and machine learning, exhibit a great potential to improve TWS assessments by integrating GRACE observations and physically-based simulations. The advances in artificial intelligence enable adaptive learning of correlations between variables in complex spatiotemporal systems. As for GRACE reconstruction, the applicability of various deep learning techniques has not been well studied previously. Thus, in this study, three deep learning-based models are developed based on the LSM-simulated TWS, to reconstruct the historical TWS in the Canadian landmass from 1979 to 2002. The performance of the models is evaluated against the GRACE-observed TWS anomalies from 2002 to 2004, and 2014 to 2016. The trained models achieve a mean correlation coefficient of 0.96, with a mean RMSE of 53 mm. The results show that the LSM-based deep learning models significantly improve the match between original LSM simulations and GRACE observations