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"Most people who use AI-powered translation tools do so for commonplace, relatively unimportant tasks like understanding a single phrase or quote. Those basic services won't do for an enterprise offering technical documents in 15 languages - but Lengoo's custom machine translation models might just do the trick. And with a new $20 million B round, they may be able to build a considerable lead. The translation business is a big one, in the billions, and isn't going anywhere. It's simply too common a task to need to release a document, piece of software or live website in multiple languages - perhaps dozens."

"Researchers involved in a recent study trained an artificial intelligence (AI) model to diagnose type 2 diabetes in patients after six to 10 seconds of listening to their voice. Canadian medical researchers trained the machine-learning AI to recognise 14 vocal differences in the voice of someone with type 2 diabetes compared to someone without diabetes. The auditory features that the AI focussed on included slight changes in pitch and intensity, which human ears cannot distinguish. This was then paired with basic health data gathered by the researchers, such as age, sex, height and weight. Researchers believe that the AI model will drastically lower the cost for people with diabetes to be diagnosed."

"Why do some people get so upset, especially in an age where many people are taking digital detoxes for mental-health breaks, and others are busy juggling life tasks? People still communicate in different ways; some are constantly attached to their phones, while others want to disengage from them for chunks of time. But tensions over reply times may also come down to social norms - or the lack thereof. New developments in digital technology have outpaced the formulation of mutually agreed new communication paradigms, so when a text is sent, we're not all responding according to the same 'rules'."

"A report from the Oxford Martin School's Programme on the Impacts of Future Technology said that 47 percent of all jobs in the U.S. are likely to be replaced by automated systems. Among the jobs soon to be replaced by machines are real estate brokers, animal breeders, tax advisers, data entry workers, receptionists and various personal assistants."

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"On paper, making human beings behave like simple machines might deliver greater efficiency. But modern-day Taylorism threatens something that those kinds of market analyses fail to capture: the value of being human."

"Today we fear a different technological threat, one that centers not on machines but other humans. We see ourselves as imperiled by the terrifying social influence unleashed by the Internet in general and social media in particular. We hear warnings that nothing less than our collective ability to perceive reality is at stake, and that if we do not take corrective action we will lose our freedoms and way of life."

"Vast sums are being spent by governments across the industrialized and developing worlds on automating poverty and in the process, turning the needs of vulnerable citizens into numbers, replacing the judgment of human caseworkers with the cold, bloodless decision-making of machines. "

"For the first time, BBC News published a news story for every constituency that declared election results overnight - all written by a computer. It was the BBC's biggest test of machine-generated journalism so far. Each of nearly 700 articles - most in English but 40 of them in Welsh - was checked by a human editor before publication."

"My job was to use technology to make the low-level content moderators' work more efficient. For example, we created a tool that allowed them to throw a video clip into our database and search for similar content. When I was at ByteDance, we received multiple requests from the bases to develop an algorithm that could automatically detect when a Douyin user spoke Uyghur, and then cut off the livestream session. The moderators had asked for this because they didn't understand the language. Streamers speaking ethnic languages and dialects that Mandarin-speakers don't understand would receive a warning to switch to Mandarin."

"These experiments in computational creativity are enabled by the dramatic advances in deep learning over the past decade. Deep learning has several key advantages for creative pursuits. For starters, it's extremely flexible, and it's relatively easy to train deep-learning systems (which we call models) to take on a wide variety of tasks."

"As the lava lamps bubble and swirl, a video camera on the ceiling monitors their unpredictable changes and connects the footage to a computer, which converts the randomness into a virtually unhackable code.  Why use lava lamps for encryption instead of computer-generated code? Since computer codes are created by machines with relatively predictable patterns, it is entirely possible for hackers to guess their algorithms, posing a security risk. Lava lamps, on the other hand, add to the equation the sheer randomness of the physical world, making it nearly impossible for hackers to break through."

"Creating an algorithm that discriminates or shows bias isn't as hard as it might seem, however. As a first-year graduate student, my advisor asked me to create a machine-learning algorithm to analyze a survey sent to United States physics instructors about teaching computer programming in their courses."

"Why? Because it has an internal app store that puts its young player base to work making virtual stuff and selling it for scrip or peanuts while the company pockets the profits. It even promotes the far-right personalities and groups using it to recruit. It is a ruthless money machine that embodies the perverse incentives of social media, aimed directly at children and operated by amoral reptiles."

"FROM THE BOING BOING SHOP   FOLLOW US Twitter / Facebook / RSS Singhealth, a Singaporean public health service, suffered the worst breach in Singaporean history, losing control of 1.5 million peoples' data; included in the breach was prescription data on 160,000 people, including Singapore's prime minister, Lee Hsien Loong."

"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."

"Beginning in 2017, I did a project with artist Trevor Paglen to look at how people were being labelled. We found horrifying classificatory terms that were misogynist, racist, ableist, and judgmental in the extreme. Pictures of people were being matched to words like kleptomaniac, alcoholic, bad person, closet queen, call girl, slut, drug addict and far more I cannot say here. ImageNet has now removed many of the obviously problematic people categories - certainly an improvement - however, the problem persists because these training sets still circulate on torrent sites [where files are shared between peers]."

""We are seeing deep fakes being used all the time, and the technology is going to allow still images, but ultimately also video images, to be synthesised [more easily] by bad actors," he says. DALL-E has content policy rules in place that prohibit bullying, harassment, the creation of sexual or political content, or creating images of people without their consent. And while Open AI has limited the number of people who can sign up to DALL-E, its lower-grade replica, DALL-E mini, is open access, meaning people can produce anything they want."

"When the field of AI believes it is neutral, it both fails to notice biased data and builds systems that sanctify the status quo and advance the interests of the powerful. What is needed is a field that exposes and critiques systems that concentrate power, while co-creating new systems with impacted communities: AI by and for the people."

"In 2015, a research group fed an AI system called Deep Patient health and medical data from some 700,000 people, and tested whether it could predict diseases. It could, but Deep Patient provides no explanation for the basis of a diagnosis, and the researchers have no idea how it comes to its conclusions. A doctor either can either trust or ignore the computer, but that trust will remain blind."