A new survey done by image authenticity app TRUEPIC revealed Thursday said 93 percent of Americans believe that online photos have been edited. Over the past two decades, content has become more visually available and more people follow visual content such as images and videos. At the same time, it is also a fact many, if not most, of the photos posted online have been subjected to some level of editing. On social media and dating sites, this might have been done for making a person appear more visually attractive and vibrant and engaging to the other people. But it seems to be having the opposite effect.

This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes.

Using artificial intelligence and machine learning in cybersecurity is gaining ground. Most IT leaders are looking at intelligent solutions, according to the May 2017 report "Next Generation Cybersecurity Analytics and Operations Survey." The survey was commissioned by DFLabs, a provider of security automation and orchestration technology, and researched by Enterprise Strategy Group (ESG). "Most of the people [in the survey] were definitely saying that machine learning is something they're evaluating from a strong security standpoint," said Dario Forte, CEO of survey sponsor DFLabs. The report, based on a survey of 412 IT and cybersecurity professionals, found that 93% of IT leaders are using or planning to use these types of solutions: 12% of respondents have deployed machine learning technologies designed for security analytics and operations automation and orchestration; another 27% said they're doing so on a limited basis, while 22% said they're adding them.

Back issues are available on-line at www.aimagazine.org The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. Regular features in AI Magazine include feature articles, workshop, symposium, and conference summaries, book reviews, editorials, news about the Association for the Advancement of Artificial Intelligence, letters to the editor, forum discussions, calendar of events, recruitment and product advertising, and columns on various topics including AI in the news. AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and reviews of books.

In this survey, we conduct an investigation on the robustness of several features and classifiers in automatic authorship attribution. Our corpus consists in 25 different documents written by 5 different American philosophers in English. The different documents pass throw a digital conversion into grey-scaled images and several levels of noise are added to corrupt those image documents. The noise consists in a “Salt & Pepper” type, which is randomly added on the surface of the images with the following noise levels: 0%, 1%, 2%, 3%, 4%, 5%, 6% and 7%. Thus, each image goes throw an OCR program (Optical Character Recognition) to extract the text from the image. Then, the obtained text document is kept to be used during the experiments of authorship attribution. Several features and classifiers are employed and evaluated with regards to the classification performances. Results are quite interesting and show that the most robust feature in au-thorship attribution is the character-tetragram, which provides a score of 100% even at a noise level of 7%.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Artificial intelligence (AI) is no longer science fiction. And like any game-changing technology, AI is not exactly what we expected – at least not yet.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

Many experts called 2016 "the year of the chatbots." Thousands of chatbots already help businesses improve customer service, sell more, and increase earnings. This paper reports a Data Monsters overview of research on the best-known platforms for building chatbots. The relevance of this research is proved by the massive deployment of chatbots. Indeed, today chatbots are used to solve a number of business tasks across many industries like E-Commerce, Insurance, Banking, Healthcare, Finance, Legal, Telecom, Logistics, Retail, Auto, Leisure, Travel, Sports, Entertainment, Media and many others.

The vast majority of respondents to the 2014 Future of the Internet canvassing anticipate that robotics and artificial intelligence will permeate wide segments of daily life by 2025, with huge implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. But even as they are largely consistent in their predictions for the evolution of technology itself, they are deeply divided on how advances in AI and robotics will impact the economic and employment picture over the next decade. We call this a canvassing because it is not a representative, randomized survey. Its findings emerge from an "opt in" invitation to experts who have been identified by researching those who are widely quoted as technology builders and analysts and those who have made insightful predictions to our previous queries about the future of the Internet. The economic impact of robotic advances and AI--Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025? Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers--with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.

Deep learning is the field of machine learning that studies and develops artificial neural networks capable of learning several layers of representation (features) from raw data. These methods have delivered new levels of performance in the field of computer vision. More recently, they have become popular in medical imaging systems, such as for the segmentation of various types of tissues in medical imagery. In this tutorial, we will provide an introduction to deep learning, covering both theory and practice. On the theory side, we will describe the most common concepts found in today's deep learning research, with a focus on convolutional neural networks.