Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99.9% similar to a benign sample. Given the wide application of DNN-based audio recognition systems, detecting the presence of adversarial examples is of high practical relevance. By applying anomalous pattern detection techniques in the activation space of these models, we show that 2 of the recent and current state-of-the-art adversarial attacks on audio processing systems systematically lead to higher-than-expected activation at some subset of nodes and we can detect these with up to an AUC of 0.98 with no degradation in performance on benign samples.

CAIRO - 12 February 2020: "We are entering the cognitive age. Over the next 25 years, advanced AI [Artificial Intelligence] will be the central element of digital transformation that fundamentally changes how businesses operate," Executive Vice President of global consulting firm Protiviti, Cory Gunderson, once said. Protiviti argues in a report that artificial Intelligence (AI) and Machine Learning (ML) are poised to help companies make dramatic shifts in performance, shareholder value and business development within the next two years. "AI opens the door to analyse massive amounts of data and deliver critical insights that organisations across a wide variety of industries can use to improve processes, drive profitability, and increase their competitive advantage," it stated. The research concluded that companies leading the charge with advanced AI are finding that it is a real game changer, while companies that are still lagging behind will soon experience a major disadvantages.

The "2020 Global Forecast for Artificial Intelligence (Ai) Chipsets (2021-2026 Outlook)-High Tech & Emerging Markets Report" report has been added to ResearchAndMarkets.com's offering. This report contains timely and accurate market statistics and forecasts on the market for over 140 countries. Published annually, it provides a unique and accurate estimate on market sizing for this equipment/material using a proprietary economic model that integrates historical trends (horizontal analysis) and longitudinal analysis of incorporated industries (vertical analysis). Estimates on equipment or material sales (product shipments value) are published historically for 2013 to 2017, projections for 2016 to 2020 and forecasts for 2021 to 2026. Product shipments include the total value of all products produced and shipped by all producers.

Today our lives are governed by technology. From the moment we wake up in the morning to the last thing we do before we go to bed revolves around technology in one way or another. If you thought this was too much to handle and were going on digital detox sprees, then brace yourself for the future. It is only going to become even more pervasive and deeply rooted in our everyday lives. At ServiceNow's annual Future of Work event, which took place in Dubai recently, Ian Khan, Technology Futurist and CEO & Founder, Futuracy, reiterated the ubiquitous role technology will have and explained the different trends that will dominate the way we work and live in the future.

Deploying the multi-relational tensor structure of a high dimensional feature space, more efficiently improves the performance of machine learning algorithms. One encounters the \emph{curse of dimensionality}, and working with vectorized data fails to preserve the data structure. To mitigate the nonlinear relationship of tensor data more economically, we propose the \emph{Tensor Train Multi-way Multi-level Kernel (TT-MMK)}. This technique combines kernel filtering of the initial input data (\emph{Kernelized Tensor Train (KTT)}), stable reparametrization of the KTT in the Canonical Polyadic (CP) format, and the Dual Structure-preserving Support Vector Machine (\emph{SVM}) Kernel for revealing nonlinear relationships. We demonstrate numerically that the TT-MMK method is more reliable computationally, is less sensitive to tuning parameters, and gives higher prediction accuracy in the SVM classification compared to similar tensorised SVM methods.

As the deadly 2019-nCov coronavirus spreads, raising fears of a worldwide pandemic, researchers and startups are using artificial intelligence and other technologies to predict where the virus might appear next -- and even potentially sound the alarm before other new, potentially threatening viruses become public health crises. "What we're doing currently with Coronavirus is really trying to get an understanding of what's happening on the ground through as many sources as we can get our hands on," says John Brownstein, chief innovation officer at Boston Children's Hospital and a professor at Harvard Medical School. After SARS killed 774 people around the world in the mid-2000s, his team built a tool called Healthmap, which scrapes information about new outbreaks from online news reports, chatrooms and more. Healthmap then organizes that previously disparate data, generating visualizations that show how and where communicable diseases like the coronavirus are spreading. Healthmap's output supplements more traditional data-gathering techniques used by organizations like the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).

Whilst the term artificial intelligence (AI) may conjure images of futuristic utopia and modern-day visionary technologies; the concept has actually been a societal forethought for longer than we think. Meanwhile, turn the clocks back considerably further to Ancient Egypt, and you'll find robotic-inspired animated ceremonial statues. Artificial intelligence is, in fact, centuries-old, and its implementation has long been a desire of the human race. Fast-forward to today and the omnipresence of robotics is remarkable. Not only are robots applied to large-scale industrial manufacturing chains (both assembling cars and integrated with vehicles themselves), but they're also found much closer to home on a smaller scale; hoovering our floors, mowing our lawns and, in some cases, stocking our shelves at local supermarkets1.

We've all seen stories about artificial intelligence in the news and on social media. Chat bots, speech recognition, machine translation and self-driving cars are just a few of the real-life examples you may have heard about or even experienced first-hand. The impact that AI decision-making has on the economy, society, education and our emotional well-being is tremendous. This begs the question: how well equipped are today's teachers to prepare their students for a world increasingly impacted by artificial intelligence and machine learning, and what opportunities and concerns do these developments bring to education? All educators are most welcome to join any time after the event.

We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash tokens in a much smaller space, similarly to a Bloom filter. We show that by applying a multi-layer Transformer to these Bloom filter digests, we are able to obtain models with high accuracy. They outperform models of a similar size without hashing and, to a large degree, models of a much larger size trained using sampled softmax with the same computational budget. Our key observation is that it is important to use a multi-layer Transformer for Bloom filter digests to remove ambiguity in the hashed input. We believe this provides an alternative method to solving problems with large vocabulary size.

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise.