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"In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles-including some you really wouldn't want shared on the Internet. In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh. The images were not taken by a person, but by development versions of iRobot's Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence."

"How that effects systems of governance has yet to be fully explored, but there are cautions. "Algorithms are only as good as the data on which they are based, and the problem with current AI is that it was trained on data that was incomplete or unrepresentative and the risk of bias or unfairness is quite substantial," says West. The fairness and equity of algorithms are only as good as the data-programming that underlie them. "For the last few decades we've allowed the tech companies to decide, so we need better guardrails and to make sure the algorithms respect human values," West says. "We need more oversight.""

"computer programs can accelerate discovery in the sciences faster than scientists can, if the computer is trained to behave like one"

"Google is using romance novels to teach its artificial intelligence (AI) system to better understand how people communicate. Researchers at Google Brain, the company's AI-focused deep learning project, presented a paper earlier this month that detailed techniques they used to teach its AI to write fiction - and the results were unexpectedly haunting."

"London-born Stanford student Joshua Browder created DoNotPay initially to help people appeal against fines for unpaid parking tickets. Dubbed "the world's first robot lawyer", Browder later programmed it to deal with a wider range of legal issues, such as claiming for delayed flights and trains and payment protection insurance (PPI)."

"Train the Deep Learning Ahem Detector with two sets of audio files, "a negative sample with clean voice/sound" (minimum 3 minutes) and "a positive one with 'ahem' sounds concatenated" (minimum 10s) and it will detect "ahems" in any voice sample thereafter."

"An investigation into the death of a 19-year-old French woman who reportedly live-streamed herself taking her own life has opened in France. The woman, who had been using the smartphone app Periscope, reportedly jumped under a train at a station about 25 miles (40 km) south of Paris on Tuesday."

"The NSA boasts in training materials that the program, called XKeyscore, is its "widest-reaching" system for developing intelligence from the internet."

"While the Moocs are associated with high-status universities, Alison's focus is on the vast numbers of people around the world needing to improve their vocational skills and training."

"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)."

"This description is reminiscent of the controversy that erupted back in January 2018 when photojournalist A. M. Ahad shared a behind-the-scenes video of multiple photographers shooting photos of a young man who was leaning out of a train window and striking a prayerful pose." Seems you do not even need Photoshop - would policies have stopped this?

"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."

"The underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set.""

"This month, in an open-access study published in PLOS One, a team reported the first brain implant system that lets patients use their thoughts to navigate an off-the-shelf Android tablet. Compared to previous generations, this system doesn't require training-for example, learning to type on a different, non-QWERTY keyboard-or specialized interface equipment. With just her thoughts, T6 was able to send emails, chat with other paralyzed patients in the trial, Google random questions, and even shop on Amazon. For the first time since she became paralyzed, T6 regained access to the entire commercially-available Google Play ecosystem and the digital world."

"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 observations made at Facebook are the latest in a long line of similar cases. In each instance, an AI being monitored by humans has diverged from its training in English to develop its own language. The resulting phrases appear to be nonsensical gibberish to humans but contain semantic meaning when interpreted by AI "agents." "

"Apart from biases in the training databases, it's hard to know how well face-recognition systems actually perform in the real world, in spite of recent gains. Anil Jain, a professor of computer science at Michigan State University who has worked on face recognition for more than thirty years, told me, "Most of the testing on the private venders' products is done in a laboratory environment under controlled settings. In real practice, you're walking around in the streets of New York. It's a cold winter day, you have a scarf around your face, a cap, maybe your coat is pulled up so your chin is partially hidden, the illumination may not be the most favorable, and the camera isn't capturing a frontal view.""

"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."

"This type of approach can speed up learning times and improve the efficiency of algorithms, says Max Jaderberg at Google's AI company DeepMind. The company used a similar technique last year to teach an AI to explore a virtual maze. Its algorithm learned much more quickly than conventional reinforcement learning approaches. "Our agent is far quicker and requires a lot less experience from the world to train, making it much more data efficient," he says."

""I consider 'bias' a euphemism," says Brandeis Marshall, PhD, data scientist and CEO of DataedX, an edtech and data science firm. "The words that are used are varied: There's fairness, there's responsibility, there's algorithmic bias, there's a number of terms… but really, it's dancing around the real topic… A dataset is inherently entrenched in systemic racism and sexism.""