The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.

The application of fuzzy theory to deep learning is limited 1) under the realm of deep neural networks; 2) to the parametric form of modeling; and 3) relying on gradient-descent based numerical algorithms for optimization because of lack of analytical solutions. This study fills this gap by providing an analytical nonparametric deep modeling solution based on the mathematical analysis of membership functions assigned to model variables. The nonparametric approach is based on the concept of representing the unknown mappings (between input and output variables) through a fuzzy set with Student-t type membership function such that the dimension of membership function increases with an increasing data size. This concept of function representation is referred to as “Student-t fuzzy-mapping” in this study. The most significant feature of this paper is to analytically derive the mathematical expressions for membership functions (which quantify uncertainties regarding the values of variables) using variational optimization such that the degree-of-belongingness of given data to the considered data-model is maximized. The study focuses on the modeling of image features where a layer of the deep-model first projects the feature vector onto a lower dimensional subspace and then construct the output feature vector through Student-t fuzzy-mappings. Numerous image classification experiments are provided to support the proposed approach.

This study presents an explainable fuzzy theoretic nonparametric deep model for an analysis of heart rate variability in application to stress assessment. We are concerned with the development of a model that evaluates and explains a short-time (3-5 minutes long) heartbeat interval sequence of an individual to estimate the level of acute perceived stress on a numerical scale from 0 to 100 via monitoring the functioning of the autonomic nervous system. The salient features of the approach are: a) A deep model, consisting of a nested composition of mappings, discovers layers of increasingly abstract heartbeat interval data representation. b) An analytical solution of the deep model's learning problem facilitates inducing a mapping from the non-interpretable heartbeat-interval-data-space onto another interpretable domain spanned by a stress index. A given non-interpretable R-R interval feature vector is explained by a) estimating the corresponding stress value, b) providing the weights which must be assigned to the subjective ratings of stress, and c) providing various information about the sympathetic and parasympathetic activities of autonomic nervous system by analyzing R-R interval sequence in frequency domain at different abstraction levels. The proof-of-concept is provided by experimentation on a previously studied dataset of 50 subjects and a new dataset of 100 subjects.

This study, under the variational Bayes (VB) framework, infers the parameters of a Takagi-Sugeno fuzzy filter having deterministic antecedents and stochastic consequents. The aim of this study is to take advantages of the VB framework to design fuzzy-filtering algorithms, which include an automated regularization, incorporation of statistical noise models, and model-comparison capability. The VB method can be easily applied to the linear-in-parameters models. This paper applies the VB method to the nonlinear fuzzy filters without using Taylor expansion for a linear approximation of some nonlinear function. It is assumed that the nonlinear parameters (i.e., antecedents) of the fuzzy filter are deterministic, while linear parameters are stochastic. The VB algorithm, by maximizing a strict lower bound on the data evidence, makes the approximate posterior of linear parameters as close to the true posterior as possible. The nonlinear deterministic parameters are tuned in a way to further increase the lower bound on data evidence. The VB paradigm can be used to design an algorithm that automatically selects the most-suitable fuzzy filter out of the considered finite set of fuzzy filters. This is done by fitting the observed data as a stochastic combination of the different Takagi-Sugeno fuzzy filters such that the individual filters compete with one another to model the data.

The unique identification of children is crucial for information technology supported vaccine delivery to the unprivileged population of third world countries. New robust image matching algorithms are required to match two ear photographs taken under nonstandard real-world conditions such as the presence of unwanted background objects in the photographs. This paper applies stochastic fuzzy models to the robust matching of ear images. The local features of the image regions are extracted using a “force-field-like” transformation. The extracted features of an image region are modeled by a stochastic fuzzy system. A region of an image is matched to a region of another image by matching the features of an image's region with the model of another image's region. As the model is fuzzy as well as stochastic, a robust matching of features' data to a model is facilitated by handling any uncertainties arising from fuzziness and randomness of the image features. The study introduces an information-theoretic index for measuring the degree of matching between image features and a model of the features. Several experiments are performed on a database of 750 ear-photographs of children (0-6 years) to justify the novel stochastic fuzzy image matching method.