Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. In this blog, we provide links to popular open-source and public data sets, data visualizations, data analytics resources, and data lakes. A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

The detailed review of Automotive Artificial Intelligence was conducted in the Global Automotive Artificial Intelligence Market 2020 Survey to collect important and substantive data on Automotive Artificial Intelligence market size, growth rate, potential demand, and Automotive Artificial Intelligence sales forecasts from 2021 to 2026. It gives an analysis of the industry chain situation, key market players, market volume, upstream raw material, production cost, and marketing channels, volume, region-wise import/export analysis, and forecast market from 2021-2026. The Automotive Artificial Intelligence market has been changing everywhere throughout the world and we have been seeing an extraordinary development in the Automotive Artificial Intelligence and this growth is expected to be huge by 2026. The report covers Automotive Artificial Intelligence applications, market elements, and the analysis of rising and existing market segments. It shows the market outline, product classification, application, and market volume forecast from 2021-2026. The report includes insightful information about the primary part of the Automotive Artificial Intelligence market.

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A young man exiled to a reeducation camp for the "digitally unsafe" learns to keep his face blank, as cameras everywhere read expressions, and signs of anger and resistance are quickly punished. The elderly victim of an attack feels empty after winning justice from a "panel of metal judges" in a future courtroom beyond human biases. An online karate class is taught by artificial intelligence and robots, but over the decades, even as the sport thrives, much of its crucial human element is forgotten. These tales of AI and its effects on future life -- and many more, from points around the world -- are gathered in "Telling Stories: On Culturally Responsive Artificial Intelligence," presented by the University of Washington Tech Policy Lab. The lab is an interdisciplinary collaboration of the UW Paul G. Allen School of Computer Science & Engineering, Information School and School of Law, to "enhance technology policy through research, education and thoughtful leadership."

Extreme E, the pioneering electric off-road racing series, is teaming up with WSC Sports, the global leader in artificial intelligence (AI)-driven sports video technology ahead of its second X Prix from 29-30 May 2021 at Lac Rose in Dakar, Senegal. Extreme E will have access to WSC Sports' cloud-based live-clipping platform Clipro, allowing the series to create and publish near-live and post-race highlights in a matter of seconds, as well as WSC Sports' innovative Graphics Engine, which automatically adds stunning visuals to videos to help brand and monetise content. During the series Extreme E and WSC Sports will work closely to apply WSC Sports' state-of-the-art AI technology to automatically generate real time highlights for this new sport. WSC Sports has already adapted its technology to support car racing working in 2020 with its partner NASCAR to automatically produce real-time race highlights. WSC Sports will also assist Extreme E in distributing race highlights to all its media partners, as well as drivers' social channels thanks to WSC Sports' partnership with Socialie.

Egocentric visual context detection can support intelligence augmentation applications. We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection. PAL has a wearable device with a camera, heart-rate sensor, on-device deep learning, and audio input/output. PAL also has a mobile/web application for personalized context labeling. We used on-device deep learning models for generic object and face detection, low-shot custom face and context recognition (e.g., activities like brushing teeth), and custom context clustering (e.g., indoor locations). The models had over 80\% accuracy in in-the-wild contexts (~1000 images) and we tested PAL for intelligence augmentation applications like behavior change. We have made PAL is open-source to further support intelligence augmentation using personalized and privacy-preserving egocentric visual contexts.

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When Covid-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson Covid-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For Covid-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

Netflix might be planning to expand into the $150 billion video game industry, according to a media report. The popular streaming company is'excited to do more with interactive entertainment' beyond its popular offerings'from series to documentaries, film, local language originals and reality TV', a spokesperson told DailyMail.com. 'Members also enjoy engaging more directly with stories they love - through interactive shows like Bandersnatch and You v. Wild, or games based on Stranger Things, La Casa de Papel and To All the Boys. So we're excited to do more with interactive entertainment.' The Information, which first broke the news, reports that the Los Gatos, California-based company has approached veteran executives in the industry to lead its efforts.

With the release of the much-anticipated National Security Commission on Artificial Intelligence report, the U.S. must confront an inconvenient truth: America, in the words of co-chairmen Eric Schmidt and Bob Work, "is not prepared to defend or compete in the AI era." Schmidt, the former chief executive of Google, and Work, former deputy secretary of defense, are as deeply versed in this subject as anyone in government or the private sector. Americans should treat this threat as a looming tower. What is the state of play and where does the U.S. go from here? First, let's address the most obvious and concerning opponent in the AI field: China.

Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are generated, what do they mean and what relations exist with the raw inputs. Then users cannot determine whether the predictions provided by DNNs are reliable, and not trust the predictions providing by such "black box" models. Therefore, in this paper, we propose a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms. The proposed model realizes explainable enterprise credit ratings. Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.