You likely have heard how artificial intelligence is changing the world, from smart phones that keep getting smarter to all the experimenting with driverless vehicles. The rapidly improving technology has also begun replacing workers, especially anyone who performs routine tasks. By some accounts, artificial intelligence could impact half the jobs in the country over the next 25 years. Don--t think of it as a new tool like the cotton gin or the personal computer, so much as a massively disruptive transformation. It--s being called the next industrial revolution.

A team of researchers at Carnegie Mellon University have developed an artificial intelligence system that automatically transfers content from one film into the style of another. In the vein of "deep fakes", the A.I.-augmented videos infamous for superimposing one person's face onto another's body, the CMU system presents another case for how difficult it could be to distinguish fiction from reality in the future. The CMU researches have named their system Recycle-GAN, after a class of algorithms that help make deep fakes possible by applying the style of one image or video to another. In a video released early this month, the researchers demonstrated how a source video of Barack Obama speaking can be processed to make it seem as though Donald Trump is mouthing the word. Or a monologue from John Oliver can be transformed into one from Stephen Colbert. Recycle-GAN isn't limited to human faces either.

The E8 OLED doesn't improve much in image quality over LG's 2017 TVs, but it does have a few key improvements. Play with the settings of your new TV, such as the Samsung QLED line of televisions, to make it picture perfect. You're blown away by the image quality of the televisions on sale at your favorite electronics retailer, but when you take one home, it somehow doesn't look (or sound) as good as the in-store experience. Your new flat-panel (or perhaps curved) television just needs a bit of tweaking for you to get the most out of it. And no, you don't need a degree in electrical engineering to pull it off.

SAPPORO – A Hokkaido newspaper company tested delivery by drone on Friday to determine whether the unmanned aircraft can be relied on to bring news to the public in times of disaster. Two weeks after a level 7 earthquake caused deadly landslides and a prefecture-wide blackout, a group comprising operators of the daily Hokkaido Shimbun's delivery shops successfully flew a drone carrying 10 copies of its newspaper 200 meters across a river in the city of Asahikawa under a hypothetical scenario in which a major quake damages a bridge and severs roads. The magnitude 6.7 quake on Sept. 6 triggered landslides that engulfed homes and shattered roads, while the blackout cut off access to information via TV, computers and mobile devices. "I'm glad we were able to deliver them successfully, as newspapers are an information infrastructure that is necessary when something drastic happens," Takuma Banno, 41-year-old head of the daily's distribution shop in Sapporo, said of the one-minute flight. The idea for the test was hatched in May and planned out before the quake struck.

Disclaimer: This episode of Chips with Everything covers topics that some listeners might find disturbing. Back in April 2017, The Guardian produced a film called Rise of the Sex Robots, a documentary by journalist Jenny Kleeman. In it, Jenny travelled to California to see a new prototype for an artificially intelligent sex robot. Its name, or her name, if you prefer, is Harmony and it can fulfil each user's particular desires through customisable appearance and personality. Fast forward 18 months, and in September 2018 the company behind this sex robot, Abyss Creations, has finally started shipping Harmony to customers.

In the early, optimistic days of the internet, we thought it would be a repository of high-quality information. Instead, it's starting to feel like a bottomless ocean of lies that rewards attention-grabbing disinformation and pollutes the political process. That's the note of alarm that three members of Congress sounded in a letter this week to Daniel Coats, the U.S. director of national intelligence. In it, the lawmakers warned specifically about the technology called deepfake, which lets computer users with little tech savvy create convincing footage of people doing and saying things that they never actually did. "Hyper-realistic digital forgeries -- popularly referred to as'deep fakes' [sic] -- use sophisticated machine learning techniques to produce convincing depictions of individuals doing or saying things they never did, without their consent or knowledge," read the letter.

Artificial intelligence (AI) company Krzana is providing journalists at the UK's largest news publisher Reach (formerly Trinity Mirror) with an AI news gathering platform. Krzana's platform monitors 60,000 sources of news from the major social networks including Twitter, YouTube, local news sites and blogs and identifies newsworthy events before they trend on social media. The user of Krzana sets up search criteria in advance, creating personalised newsfeeds based on interests and geographical locations. The system then sources text, video and images that match each of these newsfeeds as soon as they're uploaded, including posts with zero views. Krzana will enable journalists in Reach's local newsrooms to uncover pre-trending news stories without requiring individuals to constantly monitor social media to search for breaking news events.

I'm trying to train an LSTM to generate song lyrics. For my input data, I downloaded a bunch of song lyrics (1-D list where each entry is one line of a song) and used Keras Tokenization w one-hot. This is is where I'm having trouble setting up the structure. Once I have converted the lyric-lines to one-hot, what should the input to the LSTM look like? When I fit the model, what should I use for my target?

Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.

The real-world application of this algorithm used a series of iterative calculations to determine the best routes for traveling salespeople to use to fully and most efficiently cover their territory. Through the iterative process, this appeared to learn routes and improve efficiency over time. Many scholars considered this a little bit of a stretch on machine learning and artificial intelligence. To quote the introduction in the book, Artificial Intelligence: A Modern Approach (2016 edition): "We call ourselves Homo sapiens – man the wise – because our intelligence is so important to us." And while we consider ourselves intelligent, many people think only living, breathing beings can be intelligent and learn. I would have to disagree with that – thanks to the introduction of neural networks, natural language processing and all the other disciplines that touch artificial intelligence, machine learning is a real thing. Machines learn from the data that passes through them and are capable of processing vast amounts of data faster than us humans can. And those machines have the power to remember all that data, find the hidden patterns, the missing links, etc. – better than people can, and even faster than groups of people working together in enterprise businesses. Think about that device in your back pocket for a moment.