The two-day leading virtual conference, scheduled for September 23 & 24, will give deep learning practitioners a direct line to top machine learning and …

Paris and Amsterdam – Nexyad, the embedded, real-time platform for aggregating on-board data, and HERE Technologies, the leading location data and technology platform, are now working together to apply cognitive AI to road safety. Nexyad uses cognitive AI to aggregate extensive data sources in a vehicle in real time and interprets them to assess whether a certain driving behaviour is appropriate given the surrounding context. Nexyad's assessment, that can easily be delivered to a driver via a mobile phone, can be calculated from four sets of data only: HERE map, Global Navigation Satellite System, electronic horizon and acceleration. Nexyad's platform is also scalable and can aggregate data from Advanced Driving Assistant Systems (ADAS) sensors to include camera, radar and lidar, weather (visibility and temperature), and traffic data. Nexyad's real-time data aggregation platform provides two output values 20 times every second: the lack of caution of the driver and the maximum speed recommended given the road conditions – legal speed limit, road roughness, topography of the road, weather, and traffic.

Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.

This paper proposes a novel way of doing audio synthesis at the waveform level using Transformer architectures. We propose a deep neural network for generating waveforms, similar to wavenet. This is fully probabilistic, auto-regressive, and causal, i.e. each sample generated depends only on the previously observed samples. Our approach outperforms a widely used wavenet architecture by up to 9% on a similar dataset for predicting the next step. Using the attention mechanism, we enable the architecture to learn which audio samples are important for the prediction of the future sample. We show how causal transformer generative models can be used for raw waveform synthesis. We also show that this performance can be improved by another 2% by conditioning samples over a wider context. The flexibility of the current model to synthesize audio from latent representations suggests a large number of potential applications. The novel approach of using generative transformer architectures for raw audio synthesis is, however, still far away from generating any meaningful music, without using latent codes/meta-data to aid the generation process.

Recently, Microsoft announced that it was terminating dozens of journalists and editorial workers at its Microsoft News and MSN organizations. Instead, the company said, it will rely on artificial intelligence to curate and edit news and content that is presented on MSN.com, inside Microsoft's Edge browser, and in the company's Microsoft News apps. Explaining the decision, Microsoft issued a statement to the Verge. The statement reads: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. In a Final Jeopardy question with the category "colleges and universities," guest host Sanjay Gupta asked contestants which school had recently trademarked the word "The." "In 2019 this public university attempted to trademark the word "The" for use on clothing and hats," the question read. 'JEOPARDY!' EP MIKE RICHARDS SAYS A'ROBUST TEAM' IS SEARCHING FOR A NEW HOST All three contestants got the right answer -- "The" Ohio State University -- prompting viewers to bash it as a question so no-duh, it wasn't even fun.

They used deep-learning neural networks — computing systems that mimic the operations of a human brain — to analyze massive amounts of music …

The series' third installment dives deep into artificial intelligence (AI), … have continued to evolve alongside the development of machine–learning …

Jay's extensive track record building innovative data and machine learning-driven products makes him uniquely positioned to lead the company …

Opaque, whose solution integrates with popular data and machine learning libraries like Apache Spark and XGBoost, allows users to share and …