Can we define what it means to be "creative," and if so, can our definition drive artificial intelligence (AI) systems to feats of creativity indistinguishable from human efforts? This mixed question is considered from technological and social perspectives. Beginning with an exploration of the value we attach to authenticity in works of art, the article considers the ability of AI to detect forgeries of renowned paintings and, in so doing, somehow reveal the quiddity of a work of art. We conclude by considering whether evolving technical capability can revise traditional relationships among art, artist, and the market.

We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering. Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents.

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.

Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. Based on 36 qualitative interviews and a discourse analysis of algorithmic deployments in India, we find that several assumptions of algorithmic fairness are challenged. We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration. We contend that localising model fairness alone can be window dressing in India, where the distance between models and oppressed communities is large. Instead, we re-imagine algorithmic fairness in India and provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.

Nowadays, many of the images captured are "observed" by machines only and not by humans, for example, robots' or autonomous cars' cameras. High-level machine vision models, such as object recognition, assume images are transformed to some canonical image space by the camera ISP. However, the camera ISP is optimized for producing visually pleasing images to human observers and not for machines, thus, one may spare the ISP compute time and apply the vision models directly to the raw data. Yet, it has been shown that training such models directly on the RAW images results in a performance drop. To mitigate this drop in performance (without the need to annotate RAW data), we use a dataset of RAW and RGB image pairs, which can be easily acquired with no human labeling. We then train a model that is applied directly to the RAW data by using knowledge distillation such that the model predictions for RAW images will be aligned with the predictions of an off-the-shelf pre-trained model for processed RGB images. Our experiments show that our performance on RAW images is significantly better than a model trained on labeled RAW images. It also reasonably matches the predictions of a pre-trained model on processed RGB images, while saving the ISP compute overhead.

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Disney is defending the host of a new "Star Wars" web series amid backlash to tweets some deemed to be racist toward White people. Krystina Arielle announced this month that she will host "The High Republic Show," a web series offering news and insights into the latest multimedia subseries of the immensely popular science fiction franchise. However, shortly after announcing Arielle as the host of the new bi-monthly show, some combed through her past tweets and found several that spoke in somewhat harsh terms about White people's role in dismantling racism.

Google and Facebook have been in a long-running fight with Australian politicians, regulators and media companies over whether they should pay news organizations for showing their stories in search results. The battle reached a new level of intensity when a Google executive threatened to pull out of the country during testimony at the Australian Senate.

Q Acoustics builds mighty-fine loudspeakers, and for its first self-powered offering, the company could have modified any of its existing designs by bolting on an amplifier and calling it a day. What it has wrought instead is a complete high-end audio system that can accommodate nearly any source: analog or digital, wired or wireless, streaming or locally sourced; one that can be incorporated into any of the most common home-audio and smart-home ecosystems. The Q Active 200 system consists of a pair of self-amplified, wireless two-way bookshelf speakers and the Q Active Control Hub (the company will soon offer the same technology in a tower speaker system, the Q Active 400). The broad range of audio sources the Hub can handle range from a server on your network, to most of the popular streaming services, to a turntable equipped with a moving-magnet cartridge. It can then send that music both to its own speakers and to other audio systems on your network, using Apple AirPlay 2 or Google Chromecast.

Google's testimony to an Australian Senate committee on Friday threatening to withdraw its search services from Australia is chilling to anyone who cares about democracy. It marks the latest escalation in the globally significant effort to regulate the way the big tech platforms use news content to drive their advertising businesses and the catastrophic impact on the news media across the world. The news bargaining code, which would require Google and Facebook to negotiate a fair price for the use of news content, is the product of an 18-month process driven by the competition regulator. That legislation is currently before the Australian parliament, where a Senate committee is taking final submissions from interested parties. The Google bombshell makes explicit what has been a slowly escalating threat that a binding code would not be tenable.