Researchers from Carnegie Mellon University and Facebook Reality Lab are presenting Recycle-GAN, a generative adversarial system for “unsupervised video retargeting” this week at the European Conference on Computer Vision (ECCV) in Germany.

Unlike most methods, Recycle-GAN doesn’t rely on learning an explicit mapping between the images in a source and target video to perform a face swap. Instead, it’s an unsupervised learning method that begins to line up the frames from both videos based on “spatial and temporal information”.

In other words, the content that is transferred from one video to another not only relies on mapping the space but also the order of the frames to make sure both are in sync. The researchers use the comedians Stephen Colbert and John Oliver as an example. Colbert is made to look like he is delivering the same speech as Oliver, as his face is use to mimic the small movements of Oliver’s head nodding or his mouth speaking.

Here’s one where John Oliver is turned into a cartoon character.

It’s not just faces, Recycle-Gan can be used for other scenarios too. Other examples include synching up different flowers so they appear to bloom and die at the same time.

The researchers also play around with wind conditions, turning what looks like a soft breeze blowing into the trees into a more windy day without changing the background.

“I think there are a lot of stories to be told,” said Aayush Bansal, co-author of the research and a PhD. student at CMU.”It’s a tool for the artist that gives them an initial model that they can then improve,” he added.

Recycle-GAN might prove useful in other areas. Simulating various effects for video footage taken from self-driving cars could help them drive under different conditions.

“Such effects might be useful in developing self-driving cars that can navigate at night or in bad weather, Bansal said. These videos might be difficult to obtain or tedious to label, but its something Recycle-GAN might be able to generate automatically.

Source: The eyes don’t have it! AI’s ‘deep-fake’ vids surge ahead in realism • The Register

It’s tricky. Computers have to follow what is being said by whom, the context of the conversation and often some real world facts to understand cultural references. Feeding machines single sentences is often ineffective; it’s a difficult task for humans to detect if individual remarks are cheeky too.

The researchers, therefore, built a system designed to inspect individual sentences as well as the ones before and after it. The model is made up of several bidirectional long-short term memory networks (BiLSTMs) stitched together, and was accurate at spotting a sarcastic comment about 70 per cent of the time.

“Typical LSTMs read and encode the data – a sentence – from left to right. BiLSTMs will process the sentence in a left to right and right to left manner,” Reza Ghaeini, coauthor of the research on arXiv and a PhD student at Oregon State University, explained to The Register this week.

“The outcome of the BiLSTM for each position is the concatenation of forward and backward encodings of each position. Therefore, now each position contains information about the whole sentence (what is seen before and what will be seen after).”

So, where’s the best place to learn sarcasm? Reddit’s message boards, of course. The dataset known as SARC – geddit? – contains hundreds of thousands of sarcastic and non-sarcastic comments and responses.

“It is quite difficult for both machines and humans to distinguish sarcasm without context,” Mikhail Khodak, a graduate student at Princeton who helped compile SARC, previously told El Reg.

“One of the advantages of our corpus is that we provide the text preceding each statement as well as the author of the statement, so algorithms can see whether it is sarcastic in the context of the conversation or in the context of the author’s past statements.”

Source: Wow, great invention: Now AI eggheads teach machines how to be sarcastic using Reddit • The Register

SapFix, which is still under development, is designed to generate fixes automatically for specific bugs before sending them to human engineers for approval.

Facebook, which announced the tool today ahead of its @Scale conference in San Jose, California, for developers building large-scale systems and applications, calls SapFix an “AI hybrid tool.” It uses artificial intelligence to automate the creation of fixes for bugs that have been identified by its software testing tool Sapienz, which is already being used in production.

SapFix will eventually be able to operate independently from Sapienz, but for now it’s still a proof-of-concept that relies on the latter tool to pinpoint bugs first of all.

SapFix can fix bugs in a number of ways, depending on how complex they are, Facebook engineers Yue Jia, Ke Mao and Mark Harman wrote in a blog post announcing the tools. For simpler bugs, SapFix creates patches that revert the code submission that introduced them. In the case of more complicated bugs, SapFix uses a collection of “templated fixes” that were created by human engineers based on previous bug fixes.

And in case those human-designed template fixes aren’t up to the job, SapFix will then attempt what’s called a “mutation-based fix,” which works by continually making small modifications to the code that caused the software to crash, until a solution is found.

SapFix goes further by generating multiple potential fixes for each bug, then submits these for human evaluation. It also performs tests on each of these fixes so engineers can see if they might cause other problems, such as compilation errors and other crashes somewhere else.

Source: Facebook creates an AI-based tool to automate bug fixes – SiliconANGLE