Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment.

Buzzy face-swap video app Reface, which lends users celebrity'superpowers' by turning their selfies into "eerily realistic" famous video clips at the tap of a button, has caught the attention of Andreessen Horowitz. The Silicon Valley venture firm leads a $5.5 million seed round in the deep tech entertainment startup, announced today. Reface tells us its apps (iOS and Android) have been downloaded some 70 million times since it launched in January 2020 -- up from 20M when we spoke to one of its (seven) co-founders back in August. It's also attained'top five' leading app status in around 100 countries, the US included -- as well as bagging a'top app' award in the annual Google Play best of. That kind of viral growth clip has been turning heads all over the place.

Without revealing all its secrets, Netflix has laid out how it uses AI to market shows and predict their success. We already knew that Netflix shuffles and redesigns its interface and show tiles, apparently on the fly, to hook more viewers. But it also uses AI to compare new shows to those its country-by-country viewership watched in the past and to tap into metadata and information on non-Netflix shows, too. The explanation is a little (well, very) dry, but the AI goes beyond Netflix's own data to hedge the company's bets, for less risk, more profit. If, for example, a drama is likely to fare well in Spain, Netflix could increase marketing in the region and prep dubs and subtitles earlier than usual.

Reddit has acquired short-form video platform and TikTok rival Dubsmash for an undisclosed sum, the companies announced. Dubsmash will keep its current platform and brand, while Reddit will integrate Dubsmash's video creation tools. "Dubsmash provides a welcoming platform for creators and users who are under-represented in social media," Reddit said. The site boasted over a billion video views per month in January 2020, according to TechCrunch. Back in 2015, though, Dubsmash was best known as a lip-syncing app you could use to create, but not post, videos.

Addressing bias in AI algorithms will be a top priority causing guidelines to be rolled out for machine learning support of ethnicity for facial recognition.

Lomit Patel, VP of growth at IMVU and best-selling author of Lean AI, shares lessons learned and practical advice for app marketers to unlock open …

… clouds is the opportunity for more functionality, specifically in terms of analytics tools, artificial intelligence (AI), and machine-learning applications.

Artificial intelligence has granted our minds clearance to be children again, but only to reflect on the gadgets from our favorite science fiction books, movies, and television shows that have made their way into reality. You may find it beneficial to bust out a few stretches before reading, as this trip down memory lane may leave you feeling a bit old. The Jetsons, which debuted 57 years ago this week, is often cited when people talk about the future of robots, flying cars, and other household technology. Like The Simpsons, The Jetsons often made predictions of the future with different technologies, such as a household robot or video phone. With new AI tech like Facebook Portal, iRobot Roomba, the world's first robotic vacuum, and Moley Robotic Kitchen, we can live in 2062 far ahead of its time.

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance.

Artificial intelligence, including machine learning, has added a new element to security operations, too, according to Ollmann.