End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relational information encoded in graphs' structure: raw values, or literals, are either omitted completely or are stripped from their values and treated as regular nodes. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. To avoid this, we must treat literals and non-literals as separate cases. We must also address each modality separately and accordingly: numbers, texts, images, geometries, et cetera. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including images and geometries, which are projected into a joint representation space together with their relational information. We demonstrate our model on a node classification task, and evaluate the effect that each modality has on the overall performance. Our result supports our hypothesis that including information from multiple modalities can help our models obtain a better overall performance.

Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration, and NSGA for sorting local optimum points with consideration of potential areas. The proposed algorithm is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms.

Not only are Deep Neural Networks (DNNs) black box models, but also we frequently conceptualize them as such. We lack good interpretations of the mechanisms linking inputs to outputs. Therefore, we find it difficult to analyze in human-meaningful terms (1) what the network learned and (2) whether the network learned. We present a hierarchical decomposition of the DNN discrete classification map into logical (AND/OR) combinations of intermediate (True/False) classifiers of the input. Those classifiers that can not be further decomposed, called atoms, are (interpretable) linear classifiers. Taken together, we obtain a logical circuit with linear classifier inputs that computes the same label as the DNN. This circuit does not structurally resemble the network architecture, and it may require many fewer parameters, depending on the configuration of weights. In these cases, we obtain simultaneously an interpretation and generalization bound (for the original DNN), connecting two fronts which have historically been investigated separately. Unlike compression techniques, our representation is. We motivate the utility of this perspective by studying DNNs in simple, controlled settings, where we obtain superior generalization bounds despite using only combinatorial information (e.g. no margin information). We demonstrate how to "open the black box" on the MNIST dataset. We show that the learned, internal, logical computations correspond to semantically meaningful (unlabeled) categories that allow DNN descriptions in plain English. We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components the logical circuit that is the DNN.

This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1]. The main goal of this commentary is to expand on some of the issues touched on in that article. Our experience is that hyperdimensional computing is very different from other approaches to computation and that it can take considerable exposure to its concepts before attaining practically useful understanding. Therefore, in order to provide an overview of the area to the first time reader of [1], the commentary includes a brief historic overview as well as connects the findings of the article to a larger body of literature existing in the area. I. INTRODUCTION The recent article by A. Mitrokhin, P. Sutor, C. Fermüller, and Y. Aloimonos, "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception", which appeared in Science Robotics vol. 4 issue 30 (2019), presents a case for using a computation framework called hyperdimensional computing also known as Vector Symbolic Architectures (VSAs) for fusing motoric abilities of a robot with its perception system. The idea of computing with random vectors as basic objects is also known as Holographic Reduced Representation [2], Multiply-Add-Permute [3], Binary Spatter Codes [4], Binary Sparse Distributed Codes [5], Matrix Binding of Additive Terms [6], and Semantic Pointer Architecture [7]. All these frameworks are essentially equivalent. In the light of the present very high level of attention to the area of autonomous AIempowered systems from the industry and the society, we hope and believe that the application of VSAs in robotics will get an appropriately increasing attention from the community of AI/robotics researchers and practitioners. Our own experience with VSAs has shown that due to its considerable difference from the conventional computing paradigms the development of intuition and understanding required for practical applications needs to be supported by extended exposure to the details and interpretation of VSAs.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 294,000 cases as of March 22, 2020 (WHO, 2020). In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research. Executive Summary - There is a broad range of potential applications of AI covering medical and societal challenges created by the COVID-19 pandemic; however, few of them are currently mature enough to show operational impact.

Learning by Demonstration provides a sample efficient way to equip robots with complex sensorimotor skills in supervised manner. Several movement primitive representations can be used for flexible motor representation and learning. A recent state-of-the art approach is Conditional Neural Movement Primitives (CNMP) that can learn non-linear relations between environment parameters and complex multi-modal trajectories from a few expert demonstrations by forming powerful latent space representations. In this study, to improve the applicability of CNMP to changing tasks and/or environments, we couple it with a reinforcement learning agent that exploits the formed representations by the original CNMP network, and learns to generate synthetic demonstrations for further learning. This enables the CNMP network to generalize to new environments by adapting its internal representations. In the current implementation, the reinforcement learning agent is triggered when a failure in task execution is detected, and the CNMP is trained with the newly discovered demonstration (trajectory), which shares essential characteristics with the original demonstrations due to the representation sharing. As a result, the overall system increases its capacity and handle situations in scenarios where the initial CNMP network can not produce a useful trajectory. To show the validity of our proposed model, we compare our approach with original CNMP work and other movement primitives approaches. Furthermore, we presents the experimental results from the implementation of the proposed model on real robotics setups, which indicate the applicability of our approach as an effective adaptive learning by demonstration system.

Machine learning, data mining, and statistical analysis are widely applied to various applications impacting our daily lives. While we celebrate the benefits brought by these applications, to an alarming degree, the algorithms are accessing datasets containing sensitive information such as individual behaviors on the web and health records. By simply tweaking the datasets and leveraging the output of algorithms, it is possible for an adversary to learn information about and even identify certain individuals [FJR15, SSSS17]. In particular, privacy concerns become even more acute when the same dataset is probed by a sequence of algorithms. With knowledge of the dataset from the prior algorithms' output, an adversary can adaptively analyze the dataset to cause additional privacy loss at each round. This reality raises one of the most fundamental problems in the area of private data analysis: How can we accurately and efficiently quantify the cumulative privacy loss under composition of private algorithms?

In 2016, Seattle-based startup Turi Inc. was helping almost 100 customers create and manage software that uses machine learning, a powerful type of artificial intelligence. Its technology was so promising that Apple Inc. snapped it up for $200 million. The deal was a triumph for investors and founders, but one backer thought Turi -- and the broader tech industry -- might be better off if the startup had spurned Apple's advances. Matt McIlwain, managing director at Madrona Venture Group, said it's important that at least some emerging tech businesses remain independent, rather than falling into the arms of Apple, Amazon.com "It is economically beneficial to society to have more stand-alone, independent companies. We generally think that's better than just having these companies consolidated into larger ones," McIlwain said.

In 2016, Seattle-based startup Turi Inc. was helping almost 100 customers create and manage software that uses machine learning, a powerful type of artificial intelligence. Its technology was so promising that Apple Inc. snapped it up for $200 million. The deal was a triumph for investors and founders, but one backer thought Turi -- and the broader tech industry -- might be better off if the startup had spurned Apple's advances. Matt McIlwain, managing director at Madrona Venture Group, said it's important that at least some emerging tech businesses remain independent, rather than falling into the arms of Apple, Amazon.com "It is economically beneficial to society to have more stand-alone, independent companies. We generally think that's better than just having these companies consolidated into larger ones," McIlwain said.