Applications of Shaped-Charge Learning.

It is well known that deep learning (DNN) has strong limitations due to a lack of explainability and weak defense against possible adversarial attacks. These attacks would be a concern for autonomous teams producing a state of high entropy for the team's structure. In our first article for this Special Issue, we propose a meta-learning/DNN → kNN architecture that overcomes these limitations by integrating deep learning with explainable nearest neighbor learning (kNN). This architecture is named "shaped charge". The focus of the current article is the empirical validation of "shaped charge". We evaluate the proposed architecture for summarization, question answering, and content creation tasks and observe a significant improvement in performance along with enhanced usability by team members. We observe a substantial improvement in question answering accuracy and also the truthfulness of the generated content due to the application of the shaped-charge learning approach.

Shaped-Charge Learning Architecture for the Human-Machine Teams.

In spite of great progress in recent years, deep learning (DNN) and transformers have strong limitations for supporting human-machine teams due to a lack of explainability, information on what exactly was generalized, and machinery to be integrated with various reasoning techniques, and weak defense against possible adversarial attacks of opponent team members. Due to these shortcomings, stand-alone DNNs have limited support for human-machine teams. We propose a Meta-learning/DNN → kNN architecture that overcomes these limitations by integrating deep learning with explainable nearest neighbor learning (kNN) to form the object level, having a deductive reasoning-based meta-level control learning process, and performing validation and correction of predictions in a way that is more interpretable by peer team members. We address our proposal from structural and maximum entropy production perspectives.

Applying hybrid reasoning to mine for associative features in biological data.

We develop the means to mine for associative features in biological data. The hybrid reasoning schema for deterministic machine learning and its implementation via logic programming is presented. The methodology of mining for correlation between features is illustrated by the prediction tasks for protein secondary structure and phylogenetic profiles. The suggested methodology leads to a clearer approach to hierarchical classification of proteins and a novel way to represent evolutionary relationships. Comparative analysis of Jasmine and other statistical and deterministic systems (including Explanation-Based Learning and Inductive Logic Programming) are outlined. Advantages of using deterministic versus statistical data mining approaches for high-level exploration of correlation structure are analyzed.

Revealing the set of mutually correlated positions for the protein families of immunoglobulin fold.

In this study, I explain the observation that a rather limited number of residues (about 10) establishes the immunoglobulin fold for the sequences of about 100 residues. Immunoglobulin fold proteins (IgF) comprise SCOP protein superfamilies with rather different functions and with less than 10% sequence identity; their alignment can be accomplished only taking into account the 3D structure. Therefore, I believe that discovering the additional common features of the sequences is necessary to explain the existence of a common fold for these SCOP superfamilies. We propose a method for analysis of pair-wise interconnections between residues of the multiple sequence alignment which helps us to reveal the set of mutually correlated positions, inherent to almost every superfamily of this protein fold. Hence, the set of constant positions (comprising the hydrophobic common core) and the set of variable but mutually correlated ones can serve as a basis of having the common 3D structure for rather distinct protein sequences.