In the early days of artificial intelligence, computer scientists attempted to recreate aspects of the human mind in the computer. This is the type of intelligence that is the stuff of science fiction--machines that think, more or less, like us. This type of intelligence is called, unsurprisingly, intelligibility. A computer with intelligibility can be used to explore how we reason, learn, judge, perceive, and execute mental actions. Early research on intelligibility focused on modeling parts of the real world and the mind (from the realm of cognitive scientists) in the computer.

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

Biochemical methods that measure affinity and biophysical methods that describe the interaction in atomistic level detail have provided valuable information toward a mechanistic explanation for bimolecular recognition [1]. However, more often than not, compounds with drug potential are discovered serendipitously or by phenotypic drug discovery [2] since this highly specific interaction is still difficult to predict [3]. Protein structure based computational strategies such as docking [4], ultra-large library docking for discovering new chemotypes [5], and molecular dynamics simulations [4] or ligand based strategies such as quantitative structure-activity relationship (QSAR) [6, 7], and molecular similarity [8] have been powerful at narrowing down the list of compounds to be tested experimentally. With the increase in available data, machine learning and deep learning architectures are also starting to play a significant role in cheminformatics and drug discovery [9]. These approaches often require extensive computational resources or they are limited by the availability of 3D information. On the other hand, text based representations of biochemical entities are more readily available as evidenced by the 19,588 biomolecular complexes (3D structures) in PDB-Bind [10] (accessed on Nov 13, 2019) compared with 561,356 (manually annotated and reviewed) protein sequences in Uniprot [11] (accessed on Nov 13, 2019) or 97 million compounds in Pubchem [12] (accessed on Nov 13, 2019). The advances in natural language processing (NLP) methodologies make processing of text based representations of biomolecules an area of intense research interest. The discipline of natural language processing (NLP) comprises a variety of methods that explore a large amount of textual data in order to bring unstructured, latent (or hidden) knowledge to the fore [13]. Advances in this field are beneficial for tasks that use language (textual data) to build insight.

We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics. Specifically, we attempt to utilize machine learning as the essential tool for integrating the use of past data into predictions. In order to facilitate scalability to the common scenario of interest where the spatiotemporally chaotic system is very large and complex, we propose combining two approaches:(i) a parallel machine learning prediction scheme; and (ii) a hybrid technique, for a composite prediction system composed of a knowledge-based component and a machine-learning-based component. We demonstrate that not only can this method combining (i) and (ii) be scaled to give excellent performance for very large systems, but also that the length of time series data needed to train our multiple, parallel machine learning components is dramatically less than that necessary without parallelization. Furthermore, considering cases where computational realization of the knowledge-based component does not resolve subgrid-scale processes, our scheme is able to use training data to incorporate the effect of the unresolved short-scale dynamics upon the resolved longer-scale dynamics ("subgrid-scale closure").

Data gathered from multiple sensors can be effectively fused for accurate monitoring of many engineering applications. In the last few years, one of the most sought after applications for multi sensor fusion has been fault diagnosis. Dempster-Shafer Theory of Evidence along with Dempsters Combination Rule is a very popular method for multi sensor fusion which can be successfully applied to fault diagnosis. But if the information obtained from the different sensors shows high conflict, the classical Dempsters Combination Rule may produce counter-intuitive result. To overcome this shortcoming, this paper proposes an improved combination rule for multi sensor data fusion. Numerical examples have been put forward to show the effectiveness of the proposed method. Comparative analysis has also been carried out with existing methods to show the superiority of the proposed method in multi sensor fault diagnosis.

The common sense problem in AI is actually old, pretty much as old as the field itself. Turing himself alluded to it when he posed the problem of having a conversation--the Turing Test--as the proper aim of a matured and successful AI (though the term "AI" is an anachronism here; it was coined later). Old, or classic AI--all the work on AI done before the Web, basically--tried to tame common sense by adding concepts and rules to reason about concepts, known as "Knowledge Representation and Reasoning." This may seem silly today, but it's plausibly a more intuitive strategy for tackling common sense. One builds a large knowledge base with descriptions of alligators and races and legs and measurements and so on.

As more and more complex AI systems are introduced into our day-to-day lives, it becomes important that everyday users can work and interact with such systems with relative ease. Orchestrating such interactions require the system to be capable of providing explanations and rationale for its decisions and be able to field queries about alternative decisions. A significant hurdle to allowing for such explanatory dialogue could be the mismatch between the complex representations that the systems use to reason about the task and the terms in which the user may be viewing the task. This paper introduces methods that can be leveraged to provide contrastive explanations in terms of user-specified concepts for deterministic sequential decision-making settings where the system dynamics may be best represented in terms of black box simulators. We do this by assuming that system dynamics can at least be partly captured in terms of symbolic planning models, and we provide explanations in terms of these models. We implement this method using a simulator for a popular Atari game (Montezuma's Revenge) and perform user studies to verify whether people would find explanations generated in this form useful.

'Collective intelligence' is defined as the capacity of human communities to cooperate intellectually in creation, innovation and invention. As our society becomes more and more knowledge-dependent, this collective ability becomes of fundamental importance. It is therefore vital to understand, among other things, how collective intelligence processes can be expanded by digital networks. It is one of the keys to success for modern societies. Pierre Lévy is one of the world's leading thinkers, not only in the vast area of cyberculture, but also in the fundamental field of knowledge and its processes. He was essentially the first to focus research on collective intelligence when it became a determining factor in the competitiveness, creativity and human development of knowledge-based societies. Michael Peters (MP): May I call you'Pierre'? Can you tell us something about your education, especially over the three institutions of your experience as a graduate?

Active Learning (AL) is a human-in-the-loop Machine Learning paradigm favored for its ability to learn with fewer labeled instances, but the model's states and progress remain opaque to the annotators. Meanwhile, many recognize the benefits of model transparency for people interacting with ML models, as reflected by the surge of explainable AI (XAI) as a research field. However, explaining an evolving model introduces many open questions regarding its impact on the annotation quality and the annotator's experience. In this paper, we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator. We conduct an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation). Our study reveals benefits--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect and frustration from transparent model limitations--of providing local explanations in AL. We conclude by suggesting directions for developing explanations that better support annotator experience in AL and interactive ML settings.

Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently. Nevertheless, the fine-tuned BERT model trained on our protocol corpus still has a weak performance on the Entity Linking (EL) task. In this paper, we propose a model that joints a fine-tuned language model with an RFC Domain Model. Firstly, we design a Protocol Knowledge Base as the guideline for protocol EL. Secondly, we propose a novel model, PEL-BERT, to link named entities in protocols to categories in Protocol Knowledge Base. Finally, we conduct a comprehensive study on the performance of pre-trained language models on descriptive texts and abstract concepts. Experimental results demonstrate that our model achieves state-of-the-art performance in EL on our annotated dataset, outperforming all the baselines.