In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help.

NEW YORK--China is eroding America's military superiority and conventional deterrence through the integration of artificial intelligence (AI) systems …

"Geneva signifies the first stage toward a full new arms race in which artificial intelligence programs of censors and evaders compete with one a further …

Emerging tech such as IoT and AI is the second lowest priority for businesses next year for the third year running, second only to print services. That is outlook from a survey conducted by business tech provider Softcat, which asked its 1,600 customers across 18 different industries about their intentions for tech spending in 2020. Among those industries, real estate, private health and social work, and energy and utilities ranked big data, IoT and AI seventh and eighth priority respectively – the highest ranking by those questioned. The survey also reports that 56 percent of industries rank end user computing and mobility, the technology which allows for remote working, as their second biggest technology priority. The construction, education and healthcare industries ranked this as their number one priority, ahead of cyber security investment.

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.

Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these models grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and memory-intensive, prohibiting the use of longer receptive fields in practice. To increase efficiency, we make use of the "slow feature" hypothesis stating that many features of interest are slowly varying over time. For this, we use a U-Net architecture that computes features at multiple time-scales and adapt it to our auto-regressive scenario by making convolutions causal. We apply our model ("Seq-U-Net") to a variety of tasks including language and audio generation. In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance in all tasks.

With opening of Doctor Sleep this past weekend, we are reminded of the visionary genius of Stanley Kubrick. His powerful impact on art, technology and, yes, even humanity itself remains. Perhaps his most profound contribution was his science-fiction masterpiece 2001: A Space Odyssey, which was produced in 1968 and was based on the novel (and previously, the short story "The Sentinel") by Arthur C. Clarke. The film explores human evolution, existentialism and, notably, artificial intelligence. This is pure visual poetry about the relationship between man and technology. In many ways, this piece of art inspired today's technology, which is now inspiring today's "art."

Fall armyworm, the larval life stage of a fall armyworm moth, impacts maize crops worldwide but particularly in countries like Uganda, where agricultural businesses employ 70% of the population. Studies show the potential impact is between 8.3 and 20.6 million tons per year, with the fallout amounting to between $2.48 million and $6.19 million per year. The threat of devastating losses prompted developers participating in a Google Developer Group in Mbale to create an Android app -- FlatButter -- that identifies signs of fall armyworm damage in maize crops. It's been featured on a national TV station in Uganda and highlighted by the Food Agricultural Organization of the United Nations, as well as by Google in a short film published today. "The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help," wrote Hansu Mobile and Intelligent Innovations CEO Nsubuga Hassan, who led the team that developed the app.

Amazon's Echo has been our long-time favorite in the lineup of Alexa-enabled smart speakers and displays. Since its introduction in 2015, the Echo has stood out in an increasingly congested marketplace for its sound quality, far-field voice technology, and minimal footprint. The new Echo is no exception to that trend with its improved speaker, rounded design, and ever-helpful Alexa. Let's dig a little deeper into what the third-generation Echo is about and whether you should get one. The third-generation Echo can put out decent sound with its 0.8-inch tweeter and 3-inch woofer. While you might find the Echo's entry-level smaller sibling, the Dot, appealing for its lower price point, the Echo is a reasonable place to get started with smart speakers because it manages to produce respectable sound quality for its size and sub-$100 cost.