Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. It used to take a lot of time and expertise to realistically falsify videos. For decades, authentic-looking video renderings were only seen in big-budget sci-fi movies films like "Star Wars." However, thanks to the rise in artificial intelligence, doctoring footage has become more accessible than ever, which researchers say poses a threat to national security.

What makes a good video game movie? Is there even such a thing? The curse of the video game movie has long been documented, and the stigma that it's impossible to make a good one regardless of how much money you throw at it or who plays the lead has dogged the genre for years. Video games are more lucrative than Hollywood films overall, yet video game adaptations still struggle to be taken seriously by studio executives, who often misunderstand what makes the source material so popular to begin with. The anatomy of what makes a game-to-film adaptation tick is particularly relevant now with the release of Detective Pikachu, an adaptation of one of the franchise's lesser-known properties, a spinoff crime-solving game by the same name.

Peter Isackson is an author, media producer and chief visionary officer of Fair Observer Training Academy. Elon Musk's effective style of branding relies on making audacious promises most people can't understand and engaging in perennial teasing campaigns. The Daily Devil's Dictionary can always count on Elon Musk to provide it with new material. As a hyperreal celebrity with amazingly deep pockets, Musk has the rare privilege of being in a position to play games with ideas, language and even the law, in an exceptionally creative way. At least to the extent that creativity implies a loose sense of accountability.

Ranking models are typically designed to provide rankings that optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed rankings, from fueling the spread of misinformation and increasing polarization to degrading social discourse. Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones? In this paper, we first introduce a joint representation of rankings and user dynamics using Markov decision processes. Then, we show that this representation greatly simplifies the construction of consequential ranking models that trade off the immediate utility and the long-term welfare. In particular, we can obtain optimal consequential rankings just by applying weighted sampling on the rankings provided by models that maximize measures of immediate utility. However, in practice, such a strategy may be inefficient and impractical, specially in high dimensional scenarios. To overcome this, we introduce an efficient gradient-based algorithm to learn parameterized consequential ranking models that effectively approximate optimal ones. We showcase our methodology using synthetic and real data gathered from Reddit and show that ranking models derived using our methodology provide ranks that may mitigate the spread of misinformation and improve the civility of online discussions.

All the courses are good. Each will push its own tool: Amazon AWS, Google GPC & Tensor Flow etc. But you ought to be able to do it without prior knowledge of these. Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms. Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow.

If we want employers to invest seriously in artificial intelligence and robotics, the economics must be attractive to them. If it's cheaper simply to hire …

Toronto – As artificial intelligence develops the effect of algorithms and machine learning pervade more sectors of society.

There was a time when if anyone needed any news, he or she would wait for the next-day newspaper. However, with the growth of online newspapers who update news almost instantly, people have found a better and faster way to be informed of the matter of his/her interest. Nowadays social-networking systems, online news portals, and other online media have become the main sources of news through which interesting and breaking news are shared at a rapid pace. However, many news portals serve special interest by feeding with distorted, partially correct, and sometimes imaginary news that is likely to attract the attention of a target group of people. Fake news has become a major concern for being destructive sometimes spreading confusion and deliberate disinformation among the people.

There's something soothing about how rubbish Google's new predictive email tools are – if AI can't work out what you want to tell your accounts department, then it won't be organising a Terminator-style insurrection any time soon. So what hope does AI have for composing music, if bland office missives are too creatively challenging? California-based electronic composer Holly Herndon considers this moment of slowly emergent machine learning on her third album. Alongside the musicians in her ensemble is Spawn, an AI she created with husband Mat Dryhurst and developer Jules LaPlace, that listened to what the group was composing and mimicked it to create music of its own. It's not always clear which bits are Spawn-created, but Herndon obviously has the final say over how its contributions are used – and indeed, the whole album feels more like an announcement of human authority rather than a capitulation to machines.