… security standard will allow enterprises and large organizations to explore new ways to share the data needed to fuel AI and machine learning. 1.

Recent breakthroughs in deep learning have been fuelled by the immense processing power and parallelism of modern graphics processing units …

Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.

Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement's focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.

Reader's Digest is a member of the Independent Press Standards Organisation (which regulates the UK's magazine and newspaper industry). We abide by the Editors' Code of Practice and are committed to upholding the highest standards of journalism. If you think that we have not met those standards, please contact 0203 289 0940. If we are unable to resolve your complaint, or if you would like more information about IPSO or the Editors' Code, contact IPSO on 0300 123 2220 or visit ipso.co.uk

There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.

From the primitive scratchings of ancient cave dwellers, to the super-high-resolution images of far-off galaxies captured by modern telescopes, human beings have always been obsessed with pictures. The history of photography is the progression of society's ability to freeze an image in time, using technology to gradually improve its quality. The word photography is based on the Greek "photos" and "graphe" which together means "drawing with light". While decent-quality photos today are instantly available with the touch of a button on any smart phone, early photography was a laborious process that often delivered poor results. Picture-making dates back to antiquity with the discovery of two principles – camera obscura image projection, and the observation that certain substances can be altered by exposure to light. Camera obscura, the phenomenon that occurs when an image is projected through a small hole onto an opposite surface, was found in the writings of Aristotle and Chinese scholars, dating back to the 4th century BC.

Because of their ability to detect, track, and follow objects of interest while maintaining safe distances, drones have become an important tool for professional and amateur filmmakers alike. This being the case, quadcopters' camera controls remain difficult to master. Drones might take different paths for the same scenes even if their positions, velocities, and angles are carefully tuned, potentially ruining the consistency of a shot. In search of a solution, Carnegie Mellon, University of Sao Paulo, and Facebook researchers developed a framework that enables users to define drone camera shots working from labels like "exciting," "enjoyable," and "establishing." Using a software simulator, they generated a database of video clips with a diverse set of shot types and then leveraged crowdsourcing and AI to learn the relationship between the labels and certain semantic descriptors.

I've searched for a reliable way to autonomously transcribe natural speech for years. I'm a journalist, and I often have hours of taped interviews with sources around the globe to transcribe. Speech to text has been a huge challenge for AI developers, and it's a puzzle that's being closely watched in a variety of industries. The technology has implications far beyond quoting sources; human-machine interfaces in fields like robotics, autonomous vehicles, and personal computing will benefit from computers that can accurately interpret natural speech. Transcription, then, is a kind of technological entry point, a straightforward market need that can help spur development of a technology that will have broad resonance and incalculable implications for how we interact with machines.

In the in the local market, especially if it comes to local journalism and to the event market and the first time we heard that we thought well that, that's interesting and then we I think we went on with our other projects and the second time we heard another customer telling us about the same problem, we got more and more interested in that and then we asked a lot of media companies in Germany, what they are thinking about this specific problem and if we would solve that problem would that be a high would that provide a high value, if we would develop an automated solution for this specific task. So most of of the directors and so on we spoke to.