Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA-SNNs are limited to rate-encoding solutions, and are hence able to detect only spatial patterns. Developing more general training algorithms for arbitrary WTA-SNNs inherits the challenges of training (binary) Spiking Neural Networks (SNNs). These amount, most notably, to the non-differentiability of threshold functions, to the recurrent behavior of spiking neural models, and to the difficulty of implementing backpropagation in neuromorphic hardware. In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits. The method is based on probabilistic generalized linear neural models, control variates, and variational regularization. Experimental results on real-world neuromorphic datasets with multi-valued events demonstrate the advantages of WTA-SNNs over conventional binary SNNs trained with state-of-the-art methods, especially in the presence of limited computing resources.

Predicting the spread and containment of COVID-19 is a challenge of utmost importance that the broader scientific community is currently facing. One of the main sources of difficulty is that a very limited amount of daily COVID-19 case data is available, and with few exceptions, the majority of countries are currently in the "exponential spread stage," and thus there is scarce information available which would enable one to predict the phase transition between spread and containment. In this paper, we propose a novel approach to predicting the spread of COVID-19 based on dictionary learning and online nonnegative matrix factorization (online NMF). The key idea is to learn dictionary patterns of short evolution instances of the new daily cases in multiple countries at the same time, so that their latent correlation structures are captured in the dictionary patterns. We first learn such patterns by minibatch learning from the entire time-series and then further adapt them to the time-series by online NMF. As we progressively adapt and improve the learned dictionary patterns to the more recent observations, we also use them to make one-step predictions by the partial fitting. Lastly, by recursively applying the one-step predictions, we can extrapolate our predictions into the near future. Our prediction results can be directly attributed to the learned dictionary patterns due to their interpretability.

Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach.

"Europe's neonatal units have to cope with around 300,000 premature babies that are born every year across the continent. The main immediate risk for these tiny and vulnerable human beings is infection. This can be fatal in their condition. So how can infections be detected more quickly so that we can take the right decisions? In Rennes, researchers are developing a support tool to aid those medical decisions, powered by an information-collecting artificial intelligence, entitled Digi-Newb. One of the babies in the unit in Rennes is Elea, who was born four months prematurely, weighing only half a kilogram. Her mother, Catheline Quenard, says it's been a difficult and anxious time: "In the first moments, we were living minute by minute, hour by hour.

Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the role AI plays in fighting the Coronavirus, the business impact of AI, and what the American public feels about it. UC San Diego Health developed and applied an artificial intelligence algorithm to more than 2,000 lung X-ray images, helping radiologists more quickly identify signs of early pneumonia in Covid-19 patients [Becker's Hospital Review] Mayo Clinic teamed up with the state's health department to create an artificial intelligence-powered tool that can identify zones of greater Covid-19 transmission in southern Minnesota [Becker's Hospital Review] The FluSense model, developed by researchers at University of Massachusetts Amherst, was tested in campus clinic waiting rooms. The AI platform was able to analyze coughing sounds and crowd size collected by the handheld device in real-time, then use that data to accurately predict daily illness rates in each clinic [Becker's Hospital Review] The Rambam Hospital in Haifa, Israel, has begun a clinical trial of Cordio Medical's app-based AI system that analyzes speech to diagnose and remotely monitor Covid-19 patients [VentureBeat] Kentucky-based Baptist Health is using an AI platform from remote-patient-monitoring startup Current Health Ltd. to track about 20 Covid-19 patients [WSJ] AI startup SparkBeyond will assist Argentina in looking at how the country can allow citizens to return to work and minimize economic impact. The platform will use data from the Argentinian ministry of health, which aggregates travel, demographic and employment data for each citizen, then integrates hundreds of external data sources to create a wider picture of the situation. It is an area where any country, even countries as big as China and the United States, will find it challenging to achieve the necessary scale of data--from tens to hundreds of millions of humans--to train machine-learning applications that generate robust insights into health and disease.

Machine learning is leading to numerous changes in the energy industry. The Department of Energy recently announced that it is taking steps to accelerate the integration of machine learning technology in energy research and development. The head of the Department of Energy announced that they will be investing $30 million in artificial intelligence and machine learning algorithms. The new programs will have multiple purposes. One of the biggest goals is to use machine learning to facilitate the development of new renewable energy technologies.

Marta Michałowska is the Digital Marketing Manager at Synerise. In the industry for four years, Marta knows how to create creative content, what a perfect landing page is, and how to prepare a valuable, aesthetic business presentation. After hours, she is a PhD student and academic lecturer at the University of Warsaw.

This blog article about configuration data and artificial intelligence is written by Dimitris Finas, Technical Director – France, at Sweagle. This blog looks at the ways artificial intelligence and automation can support the management of configuration data change within a DevOps application estate. As human beings, we are all inherently lazy. We try to postpone or avoid tasks because they take too much time, seem too complex, or involve searching out too much information. This is also true in IT and it's a common factor between Dev and Ops.

An Anomaly is by definition something that is outside the norm or what is expected. For data this can mean rare individual outliers or distinct clusters. Anomaly detection is an important capability with broad applicability in many domains such as medical diagnostics or in detection of intrusions, fraud, or false information. All three categories of model training are used for anomalous data; supervised, semi-supervised, and unsupervised. Typically the first go-to methods are statistical and classical machine learning techniques.

Apple and Google's common coronavirus contact tracing solution for smartphones has continued to attract a lot of attention and debate over the past week, and understandably so. It's an unprecedented partnership between the world's dominant smartphone operating system makers, but people are worried about privacy and the notion that tracking tools deployed in the name of coronavirus will outlive the crisis. Debate over Apple and Google's contact tracing solution seems to have opened up an old argument between people who see a tech solution for every problem and those who say tech can't solve all our problems, and can even cause new ones. These debates certainly carry over to the kind of AI being deployed right now and the important question of when a company should ship or shelve a coronavirus solution. A lot of AI solutions are being rushed out in an attempt to save lives and speed up the day when we'll return to something resembling normal life, and you've been able to read about many of these in our coverage.