In 2018, FireEye – a company based in California – informed Facebook and Google about a large group of fake Iranian social media accounts that was running movements to control the U.S people. As a result, Facebook and Google identified them, along with fake YouTube channels and blogs, using back-end data and then removed them. "Right now, you know something's automated just by the sheer volume of content pushing out," Lee Foster, information operations manager at FireEye, says. "It's not possible for a human to do this, so it's clearly not organically created. Often you'll see automated retweeting of some list of accounts that just to boost out a message. But the situation is about to take a new turn, he claims, as Artificial Intelligence (AI) system that covers its automated heritage is available now. "Imagine having a capability out there that can automate the organic creation of original content effectively enough that it looks real, but you don't even have to have it operate or touch it," Foster says. "In the very near term, the evolution of AI and machine learning, combined with the increasing availability of big data, will begin to transform human communication and interaction in the digital space.

As a millennial, I wholeheartedly embrace the idea of existential dread. But I do find the idea that we might be able to "future-proof" our species--by, say, genetically engineering a smarter human population--somewhat reassuring. Though, as any science-fiction story centered around the idea of creating a "better" human race can tell us, there are innumerable potential pitfalls. Phil Torres describes some of these radical, real-life plans intended to save humanity from future disaster--and their potential for unintended consequences. Elsewhere on Future Tense, we've been covering both public and private surveillance.

Artificial Intelligence has in the past been the topic for many science fiction movies (think I, Robot and Star Wars), however, now science fiction is becoming a reality in front of our very eyes. Developing at a rate that no-one could have expected; it has never been as important for companies to understand AI technology and how it could be utilised to improve business efficiency and growth right across the globe. It has been a reality in many industries for quite a while now. Manufacturing companies have been using automated machines and processes for several years. The healthcare/ medical device industries are using and developing new'Smart' products all the time for treatment, diagnostic and testing purposes.

"Deepfake" is the name being given to videos created through artificially intelligent deep learning techniques. Also referred to as "face-swapping", the process involves inputting a source video of a person into a computer, and then inputting multiple images and videos of another person. The neural network then learns the movements and expressions of the person in the source video in order to map the other's image onto it to look as if they are carrying out the speech or act. This practice was first used extensively in the production of fake pornography in late 2017 – where the faces of famous female celebrities were swapped in. Research has consistently shown that pornography leads the way in technological adoption and advancement when it comes to communication technologies, from the Polaroid camera to the internet.

This post is about retraining Azure Machine Learning models. The mechanisms of continious learning and the feedback of our workflow are very important. Typically this critical feedback is provided by data scientists or the human "caretakers" of the ML solution. Looping the quality and the frequency of the model analytics feedback can determine the success or failure of this technology in the organization. We can create dynamic retraining model implementations by providing new dataset inputs automatically.

Learning visual feature representations for video analysis is a daunting task that requires a large amount of training samples and a proper generalization framework. Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data. In this paper, we introduce a novel multitask encoder-decoder framework for automatic semantic description and captioning of video sequences. In contrast to current approaches, our method relies on distinct decoders that train a visual encoder in a multitask fashion. Our system does not depend solely on multiple labels and allows for a lack of training data working even with datasets where only one single annotation is viable per video. Our method shows improved performance over current state of the art methods in several metrics on multi-caption and single-caption datasets. To the best of our knowledge, our method is the first method to use a multi-task approach for encoding video features. Our method demonstrates its robustness on the Large Scale Movie Description Challenge (LSMDC) 2017 where our method won the movie description task and its results were ranked among other competitors as the most helpful for the visually impaired.

Conversational agents are gaining popularity with the increasing ubiquity of smart devices. However, training agents in a data driven manner is challenging due to a lack of suitable corpora. This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing. Alongside this paper, we include a corpus of 3.6 million words across 23 topics. We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.

From the ancient Greeks to Shakespeare to Hollywood, humans have attempted to understand their fellow man through labeling and categorization. There was Hippocrates's blood, phlegm, yellow and black bile; the classic dramatic archetypes of hero, ingenue, jester and wise man; and, of course, Carrie, Charlotte, Samantha and Miranda from the famous HBO series More rigorously, psychologists have worked to develop empirical tests that assess core aspects of personality. The "Big Five" traits (extroversion, neuroticism, openness, conscientiousness and agreeableness) emerged in the 1940s through studies of the English language for descriptive terms. Those categories were validated in the 1990s as a scientifically backed way to evaluate a person's character. Through a series of questions, researchers learn whether you are high, low, or in between in each one of those qualities.

Did AI write this article?Depositphotos enhanced by CogWorld Imagine how productive Woodward and Bernstein might have been if only they had robots to write their articles for the Washington Post. With a little A.I. on their side, they might have taken down Nixon in days instead of years. "A lot of people don't realize this, but a lot of the news stories you read now are increasingly written by artificial intelligence," said Stephen Ibaraki, social entrepreneur, futurist and chair at REDDS Capital in an interview for my upcoming book, Uber Yourself Before You Get Kodaked: A Modern Primer on A.I. for the Modern Business, co-authored by Michael Ashley. "You get these news releases about things that are happening in sports, for example, or in business. But people are not creating these pieces anymore. Lots of us are spending hours on our mobile phones reading updates about events and news flashes never realizing it's A.I. that's generating this stuff now."