This lightweight framework is ready to give competition to Google's TensorFlow, and Facebook's PyTorch, and it can scale across devices, cloud, and edge environments. One of the key competitive advantages with'Mindspore' is that it uses 20% fewer codes that its competitors for a function like NLP (Natural language processing). Apart from codes, it can also support parallel training to save training time across hardware. Huawei developed this framework with support from partners like the University of Edinburgh, Peking University, Imperial College London, and robotics startup Milvus. Mindspore maintains and preserves sensitive data by not processing any data itself but ingests only the gradient and model information that has already been processed.

Twitter users will have seen the proliferation of "I have a joke" tweets in their feed over the past few days. The AI community produced some gems so we've collected a selection here for your amusement. I have a reinforcement learning joke, but not sure it's rewarding. I have a stochastic gradient descent joke but the punchline isn't on this saddle point https://t.co/B7GM2tmz5Z I have a deep learning joke but it has a lot of layers to it.

Layered on top of NVIDIA CUDA, RAPIDS is a suite of open-source software libraries and APIs that provide GPU parallelism and high-bandwidth memory speed through DataFrame and graph operations, achieving speedup factors of 50x or more on typical end-to-end data science workflows. For Spark 3.0, new RAPIDS APIs are used by Spark SQL and DataFrames for GPU accelerated memory efficient columnar data processing and query plans. With Spark 3.0 the Catalyst query optimizer has been modified to identify operators within a query plan that can be accelerated with the RAPIDS API, and to schedule those operators on GPUs within the Spark cluster, when executing the query plan. A new Spark shuffle implementation, built upon GPU accelerated communication libraries including Remote direct memory access (RDMA), dramatically reduces the data transfer among Spark processes. RDMA allows GPUs to communicate directly with each other, across nodes, at up to 100Gb/s, operating as if on one massive server.

BriefCam today announced future availability for BriefCam Video Content Analytics on Axis cameras with built-in deep learning processing units. BriefCam's edge analytics initiative complements its portfolio of on-premise and cloud solutions, by enabling greater freedom of choice for flexible deployment architectures through edge-based computing. Through the Axis Application Development Partner Program, BriefCam is one of the first to leverage the AXIS Camera Application Platform (ACAP) to enable comprehensive analytics directly on Axis Communications' upgraded camera series. The first camera to support BriefCam video content analytics is the AXIS Q1615 Mk III featuring a dual chipset, ARTPEC-7, and a deep-learning processing unit (DLPU), for video processing and metadata generation at the edge. By enabling BriefCam analytics on the edge, along with post processing and management capabilities, users experience real-time processing, with reduced costs and complexity, as well as reduced storage and bandwidth requirements. "Axis is proud to forge a deeper technology partnership with BriefCam toward our shared vision for advancing best-in-class video surveillance technologies," said Mats Thulin, director of core technology, Axis Communications AB. "Comprehensive video analytics is a key component to further optimizing surveillance camera investments and enabling new and expanded use cases for video – by deploying analytics at the edge, users have greater flexibility in how they implement and use video analytics."

The ability to accurately identify cancer--and classify cancer types--using machine learning would provide a tremendous advance in cancer diagnostics for both physicians and patients. But that is just one role of many that machine learning can play in cancer. Another application is to predict genomic alterations from morphological characteristics learned from digital slides. The genomicA team at the University of Chicago (UChicago) Medicine Comprehensive Cancer Center, working with colleagues in Europe, created a deep learning algorithm that can infer molecular alterations directly from routine histology images across multiple common tumor types. It also provides spatially resolved tumor and normal tissue distinction.

This very old neural network architecture was developed in 1998 by a French-American computer scientist Yann André LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. This architecture was developed for the recognition of handwritten and machine-printed characters. It is the basis of other deep learning models. The architecture consists of a total of 7 layers consisting- 2 sets of Convolution layers and 2 sets of Average pooling layers which are followed by a flattening convolution layer. After that, we have 2 dense fully connected layers and finally a softmax classifier.

This is a full transcript of the AutoBlog video & matching slides. We hope, you enjoy this as much as the video. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. Also, if you spot mistakes, please let us know! I want to talk to you today about research videos and research presentations. I know that many of you are producing videos like the one I'm producing right now in order to highlight their research.

Pinterest's mission is to bring everyone the inspiration to create a life they love. Shopping is at the core of Pinterest's mission by helping Pinners find and purchase the products they like. Oftentimes when Pinners want to buy something, they are not aware that they can go to a product-only feed, where every Pin is a product from a trustworthy merchant. To increase awareness, we present an explicit portal to this product-only page in the main search results, which we refer to as "shopping upsells" (Fig.1, highlighted in the pink box). Showing upsells to non-shoppable queries like "how to make a mask" is a poor Pinner experience; this shopping upsell appears only when the search query shows high shopping intent, e.g., "white dress".

LinkedIn today released DeText, an open source framework for natural language process-related ranking, classification, and language generation tasks. It leverages semantic matching, using deep neural networks to understand member intents in search and recommender systems. As a general framework, LinkedIn says it can be applied to a range of tasks, including search and recommendation ranking, multi-class classification, and query understanding. According to LinkedIn senior engineering manager Weiwei Guo, DeText was designed with enough flexibility to meet the requirements of different production services. It's powered by "state-of-the-art" algorithms incorporated in an end-to-end model where the variables are jointly updated, but it attempts to balance its overall effectiveness with high efficiency.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Last November, Apple ran into trouble after customers pointed out on Twitter that its credit card service was discriminating against women. David Heinemeir Hansson, the creator of Ruby on Rails, called Apple Card a sexist program. "Apple's black box algorithm thinks I deserve 20x the credit limit [my wife] does," he tweeted. The @AppleCard is such a fucking sexist program.