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"The researchers, Tero Karras, Samuli Laine, and Timo Aila, came up with a new way of constructing a generative adversarial network, or GAN. GANs employ two dueling neural networks to train a computer to learn the nature of a data set well enough to generate convincing fakes. When applied to images, this provides a way to generate often highly realistic fakery. The same Nvidia researchers have previously used the technique to create artificial celebrities (read our profile of the inventor of GANs, Ian Goodfellow)."

"Big data and artificial intelligence are some of today's most popular buzzwords. Both are promised to help deliver insights that were previously too complex for computer systems to calculate. With examples ranging from personalised recommendation systems to automatic facial analyses, user-generated data is now analysed by algorithms to identify patterns and predict outcomes. And the common view is that these developments will have a positive impact on society."

"EFF's Legal Director Corynne McSherry offers five lessons to keep in mind: 1. (Lots of) mistakes will be made: copyright takedowns result in the removal of tons of legitimate content. 2. Robots won't help: automated filtering tools like Content ID have been a disaster, and policing copyright with algorithms is a lot easier than policing "bad speech." 3. These systems need to be transparent and have due process. A system that allows for automated instant censorship and slow, manual review of censorship gives a huge advantage to people who want to abuse the system. 4. Punish abuse. The ability to censor other peoples' speech is no joke. If you're careless or malicious in your takedown requests, you should pay a consequence: maybe a fine, maybe being barred form using the takedown system. 5. Voluntary moderation quickly becomes mandatory. Every voluntary effort to stem copyright infringement has been followed by calls to make those efforts mandatory (and expand them)."

"The platform's automated tools may have mistaken the visuals of the burning building for 9/11 footage, according to Vagelis Papalexakis, an assistant professor of computer science and engineering at the University of California, Riverside who studies machine learning used in similar systems."

"His team trained a machine learning algorithm to spot words and phrases associated with bullying on social media site AskFM, which allows users to ask and answer questions. It managed to detect and block almost two-thirds of insults within almost 114,000 posts in English and was more accurate than a simple keyword search. Still, it did struggle with sarcastic remarks."

"So they trained a deep-learning neural net on tons of examples of deepfaked videos, and produced a model that's better than any previous automated technique at spotting hoaxery. (Their paper documenting the work is here.)"

"The Bank of England has previously highlighted the impact of trading algorithms. "Some markets appear to have become more fragile, as evidenced by episodes of short-term volatility and illiquidity over the past couple of years," Threadneedle Street said last December, warning of a move towards "fast, electronic trading." "

"Hangzhou Zhongheng Electric is just one example of the large-scale application of brain surveillance devices to monitor people's emotions and other mental activities in the workplace, according to scientists and companies involved in the government-backed projects. Concealed in regular safety helmets or uniform hats, these lightweight, wireless sensors constantly monitor the wearer's brainwaves and stream the data to computers that use artificial intelligence algorithms to detect emotional spikes such as depression, anxiety or rage."

"Then came the algorithm, which used automatic speech recognition to detect specific features and patterns in each of the 48 recordings. It was clear from examining the waveforms of the cries that each category had a specific pattern."

"These systems weren't set up to be biased; it's likely that they were simply trained on a subset of the diversity of accents and usages present in the United States. But, as we become ever more reliant on these systems, making them less frustrating for all their users should be a priority."

"We are talking about vast fields of aggregate data, the scale of which is difficult to comprehend; this data can be parsed by the artificial intelligence recommendation algorithms that Google has pioneered, and that now steer everything from employment application processes to dating apps."

"Raza wasn't the only one in her class who felt concerned about new levels of surveillance. Another student in the class, who did not want to be named, said that in addition to privacy worries, they were concerned that they didn't even have enough RAM to run the Proctorio software. Worse, the tool's facial detection algorithm seemed to struggle to recognize them, so they needed to sit in the full light of the window to better expose the contours of their face, in their view an indication that the system might be biased. "

"Lexplore claims its technology is new-particularly the algorithm that separates typical from atypical readers. But the concepts it's based on aren't. Its tech draws from a deep well of previously-conducted research stretching back decades, which is generally supportive of using a combination of eye tracking and machine learning to screen for dyslexia. "Eye movements is one of the best ways to index reading ability at an incredibly in-depth level," says Julie Kirkby, a psychology professor at Bournemouth University in the United Kingdom, who has studied eye tracking and dyslexia for years."

"In a paper titled "Towards Causal Representation Learning," researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google Research discuss the challenges arising from the lack of causal representations in machine learning models and provide directions for creating artificial intelligence systems that can learn causal representations."

"Machines, it would seem, are about to get an awful lot chattier. And we've got our work cut out for us to make sure the conversation's meaningful."

"Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I'm happy. My brain registers "brewing coffee" as an action that leads to a reward. That's the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari's gaming catalog and triumphed over humans in strategy games like Go. Here, an AI "agent" explores the game, trying out different actions and registering ones that let it win."

"In the last year, Facebook adjusted some of the most fundamental rules about what gets posted on its platform, halting algorithmic recommendations of political groups, banning lies about vaccines and removing a number of high-profile figures for spreading misinformation and hate - including Donald Trump. But researchers say the social media platform is not enforcing those policies as effectively when it comes to misinformation in Spanish - a blind spot that may prove deadly as health lies spread through the most vulnerable populations during the global vaccine effort."

"White-collar workers, armed with college degrees and specialized training, once felt relatively safe from automation. But recent advances in A.I. and machine learning have created algorithms capable of outperforming doctors, lawyers and bankers at certain parts of their jobs. And as bots learn to do higher-value tasks, they are climbing the corporate ladder."