A t the beginning of a new decade, these lines from the play Inherit the Wind seem as appropriate for the technology industry as they did for the debate over evolution taking place in the drama. The tech industry is faced with a tricky balancing act: continuing to drive innovative solutions while grappling with the side effects of those solutions in the global economy. The challenge itself is not unique--every industry deals with this tension as it becomes more mature--but the new variables here are the scale that tech is able to achieve and the evolutionary aspect of mixing digital and physical worlds. It's time for the industry to take the next step. There are tremendous benefits available through technology for both business and society, but there are major questions around safety, privacy, sustainability, and trust. The answers to these questions come from combining technical expertise with social awareness. By embracing responsibility for all the changes innovation can bring, the tech industry can be responsible for driving future progress. Learn how people are re-envisioning the functions, processes, and best practices for infrastructure, development, security, and data in their organizations. The IT channel is in flux. This report highlights where today's providers see opportunities and challenges, embrace new technologies, and react to new rivals. This research explores the relevance of technology to SMBs and the factors affecting perceptions, decisions, and investments in established and emerging technologies. Thanks to the vast influx of user-friendly technologies, it has become popular to say that every company is a tech company. But the ubiquity of technology does not necessarily change the underlying business model. While digital transformation is creating new avenues for growth, companies are finding that they cannot simply slap tech labels on their products and practices and automatically reap benefits. On one end of the spectrum, this takes the shape of larger companies going public and struggling with the realities of their industry.

Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a lightweight yet high-performing baseline network inspired by MobileNetV2, which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions. Furthermore, we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search (NAS) paradigm, which first trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost. Experimental results demonstrate that our searched network is competent in ASC tasks, which achieves 90.3% F1-score on the DCASE2018 task 5 evaluation set, marking a new state-of-the-art performance while saving 25% of FLOPs compared to our baseline network.

The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for language modelling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both the self attention and a combination of the two.

Second International Workshop on Automated Reasoning: Challenges, Applications, Directions, Exemplary Achievements (ARCADE 2019) EPTCS 311, 2019, pp. In the last decade, formal logics have been used to model a wide range of ethical theories and principles with the goal of using these models within autonomous systems. Logics for modeling ethical theories, and their automated reasoners, have requirements that are different from modal logics used for other purposes, e.g. for temporal reasoning. Particularly, a quantified modal logic, the deontic cognitive event calculus (DC E C), has been used to model various versions of the doctrine of double effect, akrasia, and virtue ethics. Using a fragment of DC E C, we outline these distinct characteristics and present a sketches of an algorithm that can help with some aspects proof automation forDC E C . 1 Introduction Modal logics have been used for decades to model and study a diverse set of subjects -- e.g.

Increased market for cloud computing 3.5.1.2. Growth associated with artificial intelligence and cognitive computing 3.5.2.

Increased market for cloud computing 3.5.1.2. Growth associated with artificial intelligence and cognitive computing 3.5.2.

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging and all converged NNs are not generating optimized prediction intervals (PIs). Moreover, several groups have proposed different quality criteria for PIs. These raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The optimized average width of PIs, PI-failure distances and the PI coverage probability (PICP) are computed for the test dataset. The performance of the proposed method is examined for the wind power generation and the electricity demand data. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the 'honest' workers. This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd- SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to attain provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd- SAGA over Byzantine attack resilient distributed SGD.

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.

Local partners specialising in artificial intelligence AI, machine learning and automation may want to get ready for a wave of public sector work as the federal government moves to boost its automated decision making capabilities. On 9 October, Australia's Minister for Industry, Science and Technology Karen Andrews and Education Minister Dan Tehan jointly announced that the government would spend $31.8 million to establish a research centre to investigate responsible, ethical, and inclusive automated decision making. The new Centre of Excellence for Automated Decision-Making and Society will be based at RMIT University in Melbourne, and will bring together national and international experts from the humanities, and the social and technological sciences in its efforts to develop a sound basis upon which to build out automated decision making across government. While the controversy around the government's much maligned automated debt collection scheme may have left a bad taste in the public's collective mouth when it comes to automation in public services, Tehan is confident that automated decision making technology can be a force for good in the public sector. "Our Government is funding research into automated decision making to ensure this technology provides the best possible outcomes for society and industry," Tehan said in a statement.