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"It's not that Google wants to do this, it's that they didn't anticipate this outcome, and compounded that omission by likewise omitting a way to overrule the algorithm's judgment. As with other examples of algorithmic cruelty, it's not so much this specific example as was it presages for a future in which more and more of our external reality is determined by models derived from machine learning systems whose workings we're not privy to and have no say in. "

"Machine learning is what's taking place with our personal data while we're passive players in the process. "

"In Neural Photo Editing With Introspective Adversarial Networks, a group of University of Edinburgh engineers and a private research colleague describe a method for using "introspective adversarial networks" to edit images in realtime, which they demonstrate in an open project called "Neural Photo Editor" that "enhances" photos by predicting what should be under your brush."

"In the future, the team predict the software could also learn the appropriate emotion to express at a given time. "It could be mostly empathic," said Huang, adding that a challenge would be to avoid the chatbot reinforcing negative feelings such as rage."

"Researchers from MIT and Google recently showed off a machine learning algorithm capable of automatically retouching photos just like a professional photographer. Snap a photo and the neural network identifies exactly how to make it look better-increase contrast a smidge, tone down brightness, whatever-and apply the changes in less than 20 milliseconds."

"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."

"Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed."

"One of the problems with The Social Dilemma is that it supposes that tech did what it claims it did - that these are actually such incredible geniuses that they figured out how to use machine learning to control minds. And that's the problem - the mind control thing they designed to sell you fidget spinners got hijacked to make your uncle racist. But there's another possibility, which is that their claims are rubbish. They just overpromised in their sales material, and that what actually happened with that growth of monopolies and corruption in the public sphere made people cynical, angry, bitter and violent. In which case the problem isn't that their tools were misused. The problem is that the structures in which those tools were developed are intrinsically corrupt and corrupting."

"Researchers at Columbia University sought to shed light on the problem by tasking 400 AI engineers with creating algorithms that made over 8.2 million predictions about 20,000 people. In a study accepted by the NeurIPS 2020 machine learning conference, the researchers conclude that biased predictions are mostly caused by imbalanced data but that the demographics of engineers also play a role."

"How do you maximize food production and prevent waste in your supply chain at a time when climate change and a growing global population are placing an increasing strain on resources?  According to Israeli startup Blue Circle, you do it in the same way you protect your technology from hackers: with artificial intelligence, machine learning and huge amounts of data. "

"The camera never lies. Except, of course, it does - and seemingly more often with each passing day. In the age of the smartphone, digital edits on the fly to improve photos have become commonplace, from boosting colours to tweaking light levels. Now, a new breed of smartphone tools powered by artificial intelligence (AI) are adding to the debate about what it means to photograph reality. Google's latest smartphones released last week, the Pixel 8 and Pixel 8 Pro, go a step further than devices from other companies. They are using AI to help alter people's expressions in photographs. It's an experience we've all had: one person in a group shot looks away from the camera or fails to smile. Google's phones can now look through your photos to mix and match from past expressions, using machine learning to put a smile from a different photo of them into the picture. Google calls it Best Take. "

"Abbott's experience mirrors the situation faced by Africans who don't speak English. Many language models like ChatGPT do not perform well for languages with smaller numbers of speakers, especially African ones. But a new venture called Lelapa AI, a collaboration between Abbott and a biomedical engineer named Pelonomi Moiloa, is trying to use machine learning to create tools that specifically work for Africans."

"Hassabis was a child chess prodigy, who learned the game aged four and was able to beat his dad three weeks later - indeed, when he started playing competitively he was so small he had to bring a pillow with him to reach the board - and became a strong player. Yet in AlphaZero's case there was no human input, other than telling it the rules of each game. "In a matter of a few hours it was superhuman," Hassabis says proudly."

"The reason to look at humans is because there are certain things that humans do much better than deep-learning systems. That doesn't mean humans will ultimately be the right model. We want systems that have some properties of computers and some properties that have been borrowed from people. We don't want our AI systems to have bad memory just because people do. But since people are the only model of a system that can develop a deep understanding of something-literally the only model we've got-we need to take that model seriously."

""As expected, we found evidence of a performance improvement over generations due to social learning," the researchers wrote. "Adding an algorithm with a different problem-solving bias than humans temporarily improved human performance but improvements were not sustained in following generations. While humans did copy solutions from the algorithm, they appeared to do so at a lower rate than they copied other humans' solutions with comparable performance." Brinkmann told Motherboard that while they were surprised superior solutions weren't more commonly adopted, this was in line with other research suggesting human biases in decision-making persist despite social learning. Still, the team is optimistic that future research can yield insight into how to amend this."

"The performance of LipNet compares incredibly favorably to human lipreading experts on GRID corpus, the largest publicly-available sentence-level lipreading dataset. In fact, where human experts got just 52 percent, LipNet scored 93 percent. Its sentence-based approach to lip-reading also smashed the best previous attempt by a machine, which managed 79.6 percent accuracy on the same dataset."

"The algorithmic manager seems to watch everything you do. Ride-hailing platforms track a variety of personalized statistics, including ride acceptance rates, cancellation rates, hours spent logged in to the app and trips completed. And they display selected statistics to individual drivers as motivating tools, like "You're in the top 10 percent of partners!" Uber uses the accelerometer in drivers' phones along with GPS and gyroscope to give them safe driving reports, tracking their performance in granular detail. One driver posted to a forum that a grade of 210 out of 247 "smooth accelerations" earned a "Great work!" from the boss."

""AI can pick up missed cues and suggest nudges to bridge the gap in emotional intelligence and communication styles. It can identify optimal ways to discuss common problems and alleviate common misunderstandings based on these different priorities and ways of viewing the world. We could be looking at a different gender dynamics in a decade.""

"Depending on how legal decisions shake out, AI systems could become a valuable tool to assist creativity, a nuisance ripping off hard-working human musicians, or both."