Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.

The development of graph neural networks (GCN) makes it possible to learn structural features from evolving complex networks. Even though a wide range of realistic networks are directed ones, few existing works investigated the properties of directed and temporal networks. In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM. The proposed model adopts an autoencoder architecture, which utilizes graph attentional layers to capture the structural feature of neighborhood nodes, as well as a set of graph convolutional layers to capture motif features. A graph recurrent unit layer with self-attention is utilized to learn temporal variations in the snapshot sequence. We run comparative experiments on four realistic networks to validate the effectiveness of TSAM. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.

If you happen to fall outside that specified range, navigating the internet, your community, even your own home, can become exponentially more difficult. But it doesn't have to be this way, argues artist, writer and design researcher Sara Hendren. In her new book, What Can a Body Do, Hendren examines the challenges that people with disabilities face on a daily basis in a world that often doesn't take their needs into account and shows that more inclusive design -- from cybernetic prosthetic arms and more accessible city streets to tactile doorbells for the deaf -- isn't just possible, it's already practical. In the excerpt below, Hendren looks at the Jaipur Foot, an unpowered, low-cost prosthetic that has helped nearly two million lower leg amputees in India and other countries regain their ability to walk. From WHAT CAN A BODY DO: How We Meet the Built World by Sara Hendren published on August 18, 2020 by Riverhead, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC.

Global Artificial Intelligence-based Security Market reports provide in-depth analysis of Top Players, Geography, End users, Applications, Competitor analysis, Revenue, Price, Gross Margin, Market Share, Import-Export data, Trends and Forecast. Firstly, the Artificial Intelligence-based Security Market Report provides a basic overview of the industry including definitions, classifications, applications and chain structure. The Artificial Intelligence-based Security market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status. Key Players covered in this report are Nvidia Corporation, Intel Corporation, Xilinx Inc, Samsung Electronics Co., Ltd, Micron Technology, IBM Corporation, Cylance Inc, Threatmetrix, Securonix, Inc, Amazon, Sift Science, Acalvio Technologies, Skycure Inc,. Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions.

Global Marketers presents an updated and Latest Study on Artificial Intelligence In Construction Market 2020-2026. This report comprises a detailed study of the market covering its future predictions by the past year as a reference for the period between 2020 and 2026 as the forecast period. The report breakdowns major segments and highlights wider level geographies. This report also offers an all-inclusive study of the future trends and developments of the market. The key insights and evaluations presented in this Artificial Intelligence In Construction report are worth knowing for any market participant, helping them in ascertaining the superior dynamics and the future trajectories of the global Artificial Intelligence In Construction Market.

DUBAI: The idea of artificial intelligence (AI) has been around for a long time, but in recent decades it has gone from being the stuff of science fiction to something tangible and beneficial. Sir Michael Brady is the interim president of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), an Abu Dhabi-based AI-focused university -- the first of its kind in the world. "Stephen Hawking, Elon Musk, Microsoft, Google, Facebook and many more have contributed to AI's position in today's spotlight," he said. He added: "We have moved from the use of AI in large-scale industry or ground-breaking circumstances, such as NASA space exploration or factory robotics, to commonplace applications such as advertising algorithms or Netflix suggesting what show to watch next." As society transforms under the impact of technology, AI is also rapidly evolving.

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This paper presents an adaptive resonance theory predictive mapping (ARTMAP) model which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of ARTMAP can improve the choices of clusters to which samples are incrementally assigned. These improvements are accomplished by intelligently performing the operations of swapping sample assignments between clusters, splitting and merging clusters, and caching the values of variables when iCVI values need to be recomputed. Using recursive formulations enables iCVI-ARTMAP to considerably reduce the computational burden associated with cluster validity index (CVI)-based offline clustering. Depending on the iCVI and the data set, it can achieve running times up to two orders of magnitude shorter than when using batch CVI computations. In this work, the incremental versions of Calinski-Harabasz, WB-index, Xie-Beni, Davies-Bouldin, Pakhira-Bandyopadhyay-Maulik, and negentropy increment were integrated into fuzzy ARTMAP. Experimental results show that, with proper choice of iCVI, iCVI-ARTMAP outperformed fuzzy adaptive resonance theory (ART), dual vigilance fuzzy ART, kmeans, spectral clustering, Gaussian mixture models and hierarchical agglomerative clustering algorithms in most of the synthetic benchmark data sets. It also performed competitively on real world image benchmark data sets when clustering on projections and on latent spaces generated by a deep clustering model. Naturally, the performance of iCVI-ARTMAP is subject to the selected iCVI and its suitability to the data at hand; fortunately, it is a general model wherein other iCVIs can be easily embedded.

It was the organizers' belief that "significant advances can be made" in at least one, if not several of these specific areas of concern through a joint effort of a "carefully selected group of scientists". And what's more, this advancement could be quicker than many though possible. While there already had been significant developments in automata – machines that can carry preprogrammed and predetermined functions – it was the conference organizers' belief, especially McCarthy, that there was a mountain of potential for the development of truly "intelligent" machines that could essentially, think for themselves. It was his further belief that through the joint effort of likeminded people willing to "devote time to it…could make real progress". It would turn out, he was correct. In the years that followed the crucial conference in the summer of 1956, advancements in artificial intelligence began to become ever-more rapid.

Over the past two years, the development of Artificial Intelligence and the new techniques for using Big Data has become both faster and more widespread. According to the old definition, by Artificial Intelligence we mean teaching a machine to think like a man, while Big Data is such a large mass of data in terms of quantity, speed and variety that it has to enable specific technologies and methods to extrapolate data from news already learned and extract new data and links from the news which seem unrelated to one another. Just to make an example, each buyer wants a specific reward. Currently we also have the possibility of developing Generative Adversarial Networks (GANs), which create objects not existing in reality, but similar to reality, as well as faces that have never been seen before but are quite probable, and objects that do not exist but seem to work well. Not to mention the self-correcting systems based on concepts that are adapted by the machine itself, as well as programs that self-create themselves, starting from a small nucleus.