The fear is so publicised that I was not surprised when I came across a website–Will Robots Take My Job?–that lets you search your job's relevancy against technology over time. Marketing is the latest to buzz with such apprehensions. Clearly, there is no dearth of examples of AI in action, with it being infused across all the four Ps of marketing, however, it is far from competing with marketers or replacing them. Instead, it is boosting our effectiveness and efficiency as it takes care of the mundane, leaving more time for thinking and creativity. And, whether AI remains a friend or becomes a foe really depends on how we use it.

The Council of Europe has recently adopted key resolutions concerning AI and its intersection with media and journalism. Should computers write the news? If AI tools remove deliberately misleading information, is that an infringement of freedom of expression, or does it protect public discourse? During the recent Council of Europe Ministerial Conference (10 and 11 June), a final declaration and four resolutions were adopted to address these worries. The resolution domains included digital technologies, safety of journalists, the changing media and information environment, and the impact of the Covid-19 pandemic on freedom of expression.

The AI engine then analyzes that query for its technical content and then using essentially cosine-similarity-type techniques and vector math, it will …

Deep learning based visual to sound generation systems essentially need to be developed particularly considering the synchronicity aspects of visual and audio features with time. In this research we introduce a novel task of guiding a class conditioned generative adversarial network with the temporal visual information of a video input for visual to sound generation task adapting the synchronicity traits between audio-visual modalities. Our proposed FoleyGAN model is capable of conditioning action sequences of visual events leading towards generating visually aligned realistic sound tracks. We expand our previously proposed Automatic Foley dataset to train with FoleyGAN and evaluate our synthesized sound through human survey that shows noteworthy (on average 81\%) audio-visual synchronicity performance. Our approach also outperforms in statistical experiments compared with other baseline models and audio-visual datasets.

Thanks to Valerio De Stefano, Reuben Binns, Jeremias Adams-Prassl, Barend van Leeuwen, Aislinn Kelly-Lyth, Lilian Edwards, Natali Helberger, Christopher Marsden, Sarah Chander, Corinne Cath-Speth for comments and/or discussion; substantive and editorial input by Ulrich Gasper; and the conveners and participants of several workshops including one convened by Margot Kaminski, one by Burkhard Schäfer, one part of the 2nd ELLIS Workshop in Human-Centric Machine Learning; one between Lund University and the Labour Law Community; and one between Oxford, KU Leuven and UCL. A CC-BY 4.0 license applies to this article after 3 calendar months from publication have elapsed.

We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) the best known privacy/utility trade-off both theoretically, as well as on benchmark data sets. In particular, we provide the first global convergence analysis of ALS with noise introduced to ensure DP, and show that, in comparison to the best known alternative (the Private Frank-Wolfe algorithm by Jain et al. (2018)), our error bounds scale significantly better with respect to the number of items and users, which is critical in practical problems. Extensive validation on standard benchmarks demonstrate that the algorithm, in combination with carefully designed sampling procedures, is significantly more accurate than existing techniques, thus promising to be the first practical DP embedding model.

Developing video understanding intelligence is quite challenging because it requires holistic integration of images, scripts, and sounds based on natural language processing, temporal dependency, and reasoning. Recently, substantial attempts have been made on several video datasets with associated question answering (QA) on a large scale. However, existing evaluation metrics for video question answering (VideoQA) do not provide meaningful analysis. To make progress, we argue that a well-made framework, established on the way humans understand, is required to explain and evaluate the performance of understanding in detail. Then we propose a top-down evaluation system for VideoQA, based on the cognitive process of humans and story elements: Cognitive Modules for Evaluation (CogME). CogME is composed of three cognitive modules: targets, contents, and thinking. The interaction among the modules in the understanding procedure can be expressed in one sentence as follows: "I understand the CONTENT of the TARGET through a way of THINKING." Each module has sub-components derived from the story elements. We can specify the required aspects of understanding by annotating the sub-components to individual questions. CogME thus provides a framework for an elaborated specification of VideoQA datasets. To examine the suitability of a VideoQA dataset for validating video understanding intelligence, we evaluated the baseline model of the DramaQA dataset by applying CogME. The evaluation reveals that story elements are unevenly reflected in the existing dataset, and the model based on the dataset may cause biased predictions. Although this study has only been able to grasp a narrow range of stories, we expect that it offers the first step in considering the cognitive process of humans on the video understanding intelligence of humans and AI.

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.

When we have to talk about the science fiction genre, There are many topics that can be dealt with Among the films of this type. On many occasions, although many of these issues go hand in hand, we can distinguish one or the other. Usually, when there are bots, It doesn't take long for AI to emerge, but often the latter acquires great importance, beyond the propagation of these robotic organisms, since then They can think and yearn for the same things as humans. Currently, evolution In this field it has become enormous The most reasonable results are achieved. From computational techniques to simple problem solving, Neural network techniques that make machines learnAnd artificial intelligence is closer than we think and some cinematic has analyzed some of the results of all this.

Artificial intelligence is a heavy and complex topic with tons of deep ethical … Machine learning (ML) is the first step in optimized data science …