Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring’s screenplay.

ome researchers have already used machine learning to identify emotional arcs in stories. One method, developed at the University of Vermont, involved having computers scan text—video scripts or book content—to construct arcs.

We decided to go a step further. Working as part of a broader collaboration between MIT’s Lab for Social Machines and McKinsey’s Consumer Tech and Media team, we developed machine-learning models that rely on deep neural networks to “watch” small slices of video—movies, TV, and short online features—and estimate their positive or negative emotional content by the second.

These models consider all aspects of a video—not just the plot, characters, and dialogue but also more subtle touches, like a close-up of a person’s face or a snippet of music that plays during a car-chase scene. When the content of each slice is considered in total, the story’s emotional arc emerges.

Source: AI in storytelling: Machines as cocreators | McKinsey & Company

There’s a video of Gal Gadot having sex with her stepbrother on the internet. But it’s not really Gadot’s body, and it’s barely her own face. It’s an approximation, face-swapped to look like she’s performing in an existing incest-themed porn video.The video was created with a machine learning algorithm, using easily accessible materials and open-source code that anyone with a working knowledge of deep learning algorithms could put together.

It’s not going to fool anyone who looks closely. Sometimes the face doesn’t track correctly and there’s an uncanny valley effect at play, but at a glance it seems believable. It’s especially striking considering that it’s allegedly the work of one person—a Redditor who goes by the name ‘deepfakes’—not a big special effects studio that can digitally recreate a young Princess Leia in Rogue One using CGI. Instead, deepfakes uses open-source machine learning tools like TensorFlow, which Google makes freely available to researchers, graduate students, and anyone with an interest in machine learning.

Like the Adobe tool that can make people say anything, and the Face2Face algorithm that can swap a recorded video with real-time face tracking, this new type of fake porn shows that we’re on the verge of living in a world where it’s trivially easy to fabricate believable videos of people doing and saying things they never did. Even having sex.

So far, deepfakes has posted hardcore porn videos featuring the faces of Scarlett Johansson, Maisie Williams, Taylor Swift, Aubrey Plaza, and Gal Gadot on Reddit. I’ve reached out to the management companies and/or publicists who represent each of these actors informing them of the fake videos, and will update if I hear back.

Source: AI-Assisted Fake Porn Is Here and We’re All Fucked – Motherboard

In the paper, DeepMind describes how a descendant of the AI program that first conquered the board game Go has taught itself to play a number of other games at a superhuman level. After eight hours of self-play, the program bested the AI that first beat the human world Go champion; and after four hours of training, it beat the current world champion chess-playing program, Stockfish. Then for a victory lap, it trained for just two hours and polished off one of the world’s best shogi-playing programs named Elmo (shogi being a Japanese version of chess that’s played on a bigger board).

One of the key advances here is that the new AI program, named AlphaZero, wasn’t specifically designed to play any of these games. In each case, it was given some basic rules (like how knights move in chess, and so on) but was programmed with no other strategies or tactics. It simply got better by playing itself over and over again at an accelerated pace — a method of training AI known as “reinforcement learning.

”Using reinforcement learning in this way isn’t new in and of itself. DeepMind’s engineers used the same method to create AlphaGo Zero; the AI program that was unveiled this October. But, as this week’s paper describes, the new AlphaZero is a “more generic version” of the same software, meaning it can be applied to a broader range of tasks without being primed beforehand.What’s remarkable here is that in less than 24 hours, the same computer program was able to teach itself how to play three complex board games at superhuman levels. That’s a new feat for the world of AI.

Source: DeepMind’s AI became a superhuman chess player in a few hours – The Verge

Coditany of Timeness” is a convincing lo-fi black metal album, complete with atmospheric interludes, tremolo guitar, frantic blast beats and screeching vocals. But the record, which you can listen to on Bandcamp, wasn’t created by musicians.Instead, it was generated by two musical technologists using a deep learning software that ingests a musical album, processes it, and spits out an imitation of its style.To create Coditany, the software broke “Diotima,” a 2011 album by a New York black metal band called Krallice, into small segments of audio. Then they fed each segment through a neural network — a type of artificial intelligence modeled loosely on a biological brain — and asked it to guess what the waveform of the next individual sample of audio would be. If the guess was right, the network would strengthen the paths of the neural network that led to the correct answer, similar to the way electrical connections between neurons in our brain strengthen as we learn new skills.At first the network just produced washes of textured noise. “Early in its training, the kinds of sounds it produces are very noisy and grotesque and textural,” said CJ Carr, one of the creators of the algorithm. But as it moved through guesses — as many as five million over the course of three days — the network started to sound a lot like Krallice. “As it improves its training, you start hearing elements of the original music it was trained on come through more and more.”As someone who used to listen to lo-fi black metal, I found Coditany of Timeness not only convincing — it sounds like a real human band — but even potentially enjoyable. The neural network managed to capture the genre’s penchant for long intros broken by frantic drums and distorted vocals. The software’s take on Krallice, which its creators filled out with song titles and album art that were also algorithmically generated, might not garner a glowing review on Pitchfork, but it’s strikingly effective at capturing the aesthetic. If I didn’t know it was generated by an algorithm, I’m not sure I’d be able to tell the difference.

Source: This frostbitten black metal album was created by an artificial intelligence | The Outline

I’m excited to announce the initial release of Mozilla’s open source speech recognition model that has an accuracy approaching what humans can perceive when listening to the same recordings. We are also releasing the world’s second largest publicly available voice dataset, which was contributed to by nearly 20,000 people globally.
[…]
This is why we started DeepSpeech as an open source project. Together with a community of likeminded developers, companies and researchers, we have applied sophisticated machine learning techniques and a variety of innovations to build a speech-to-text engine that has a word error rate of just 6.5% on LibriSpeech’s test-clean dataset.

In our initial release today, we have included pre-built packages for Python, NodeJS and a command-line binary that developers can use right away to experiment with speech recognition.
[…]
Our aim is to make it easy for people to donate their voices to a publicly available database, and in doing so build a voice dataset that everyone can use to train new voice-enabled applications.

Today, we’ve released the first tranche of donated voices: nearly 400,000 recordings, representing 500 hours of speech. Anyone can download this data.
[…]
To this end, while we’ve started with English, we are working hard to ensure that Common Voice will support voice donations in multiple languages beginning in the first half of 2018.

Finally, as we have experienced the challenge of finding publicly available voice datasets, alongside the Common Voice data we have also compiled links to download all the other large voice collections we know about.

Source: Announcing the Initial Release of Mozilla’s Open Source Speech Recognition Model and Voice Dataset – The Mozilla Blog