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This book examines the growing importance of algorithms and automation--including emerging forms of artificial intelligence--in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on'reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values.

Said Mr. Abrams, "the Venzee platform is designed to open Artificial Intelligence and Machine Learning tech to simply connect Consumer Brands to …

As the influence and use of artificial intelligence (AI) have grown As the influence and use of artificial intelligence (AI) have grown and its transformative potential has become more apparent, many and its transformative potential has become more apparent [32, 54], questions have been raised regarding the economic, political, social, many questions have been raised regarding the economic, political, and ethical implications of its use. Public opinion plays an important social, and ethical implications of its use [27]. The development role in these discussions, influencing product adoption, commercial and application of AI increasingly features in media, academic, development, research funding, and regulation. In this paper we industrial, regulatory, and public discussions [18, 23, 28], with active present results of an in-depth survey of public opinion of artificial debate on wide-ranging issues such as the impact of automation intelligence conducted with 10,005 respondents spanning eight on the future of work [8, 50, 52], the interaction of AI with human countries and six continents. We report widespread perception rights issues such as privacy and discrimination [1, 4, 10, 16], the that AI will have significant impact on society, accompanied by ethics of autonomous weapons [53, 59], and the development and strong support for the responsible development and use of AI, and availability of dual-use technologies such as synthetic media that also characterize the public's sentiment towards AI with four key may be used for either benevolent or nefarious purposes [48].

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.

Performance of recommender systems (RS) relies heavily on the Many large scale e-commerce platforms (such as Etsy, Overstock, amount of training data available. This poses a chicken-and-egg etc) and online content platforms (such as Spotify, Overstock, Disney, problem for early-stage products, whose amount of data, in turn, Netflix, etc) have such a large inventory of items that showcasing relies on the performance of their RS. On the other hand, zero-shot all of them in front of their users is simply not practical. In learning promises some degree of generalization from an old dataset particular, in the online content category of businesses, it is often to an entirely new dataset. In this paper, we explore the possibility seen that users of their service do not have a crisp intent in mind of zero-shot learning in RS. We develop an algorithm, dubbed ZEro-unlike in the retail shopping experience where the users often have Shot Recommenders (ZESRec), that is trained on an old dataset a crisp intent of purchasing something. The need for personalized and generalize to a new one where there are neither overlapping recommendations therefore arises from the fact that not only it is users nor overlapping items, a setting that contrasts typical crossdomain impractical to show all the items in the catalogue but often times RS that has either overlapping users or items. Different users of such services need help discovering the next best thing from categorical item indices, i.e., item ID, in previous methods, -- be it the new and exciting movie or be it a new music album or ZESRec uses items' natural-language descriptions (or description even a piece of merchandise that they may want to consider for embeddings) as their continuous indices, and therefore naturally future buying if not immediately.

On May 14, the gaming and entertainment news site IGN published an article titled "How to Help Palestinian Civilians." The page listed five organizations to which readers could donate and linked to resources across The Washington Post, Vox and the New York Times outlining the contours of the Israeli-Palestinian conflict. It was not an unusual move for the website, which had run similar activations around Pride Month, as well as in support of movements like Black Lives Matter and Stop AAPI Hate. The website's banner was changed to include a Palestinian flag alongside the IGN logo.

… today they will extend the comment period on the request for information on financial institutions’ use of artificial intelligence (AI) until July 1, 2021.

The agencies are seeking information from the public on how financial institutions use AI in their activities, including fraud prevention, personalization …

In light of this, there are new artificial intelligence (AI) engines that can analyze data—including social vulnerability indices—which can make it …