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"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."

"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]."

"Whereas most machine learning algorithms can't hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in "neuroplasticity." That is, as it goes about its work-say, in the future, maybe driving a car or directing a robot-it can learn from experience and adjust its connections on the fly."

"If true, this would be a big deal. But, said OpenAI, "due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.""

"Game players-like drivers-often have to reach conclusions without full understanding of what the other players-or drivers-are doing. So more researchers are applying game theory to train self-driving cars how to act in uncertain situations."

"The EU could temporarily ban the use of facial recognition technology in public places such as train stations, sport stadiums and shopping centres over fears about creeping surveillance of European citizens."

"The point of creating this vast portfolio of digital gun art is to feed an algorithm made to detect a firearm as soon as a security camera catches it being drawn by synthetically creating tens of thousands of ways each gun may appear. Arcarithm is one of several companies developing automated active shooter detection technology in the hopes of selling it to schools, hotels, entertainment venues and the owners of any location that could be the site of one of America's 15,000 annual gun murders and 29,000 gun injuries."

"Mentor is made by eDriving, which describes the app on its website as a "smartphone-based solution that collects and analyzes driver behaviors most predictive of crash risk and helps remediate risky behavior by providing engaging, interactive micro-training modules delivered directly to the driver in the smartphone app." But CNBC talked to drivers who said the app mostly invades their privacy or miscalculates dangerous driving behavior. One driver said even though he didn't answer a ringing phone, the app docked points for using a phone while driving. Another worker was flagged for distracted driving at every delivery stop she made. The incorrect tracking has real consequences. ranging from restricted payouts and bonuses to job loss. "

"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."

"There are currently no usable photo data sets of mask-wearing people that can be used to train and evaluate facial recognition systems. The NIST study addressed this problem by superimposing masks (of various colours, sizes and positions) over images of faces, as seen here:"

"However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself."

"As part of her undergraduate research, Alice Xue studied whether a machine could pass a Visual Turing Test by producing images that people cannot tell were made by a machine. Xue trained an algorithm using 2,192 traditional Chinese landscape paintings collected from art museums. The resulting AI-generated paintings were mistaken for being made by humans 55 per cent of the time."

"Elon Musk wants Tesla to be seen as "much more than an electric car company." On Thursday's Tesla AI Day, the CEO described Tesla as a company with "deep AI activity in hardware on the inference level and on the training level" that can be used down the line for applications beyond self-driving cars, including a humanoid robot that Tesla is apparently building."

""Creating greater access to Apple genuine parts gives our customers even more choice if a repair is needed," said Jeff Williams, Apple's chief operating officer. "In the past three years, Apple has nearly doubled the number of service locations with access to Apple genuine parts, tools, and training, and now we're providing an option for those who wish to complete their own repairs.""

"Two authors have filed a lawsuit against OpenAI, the company behind the artificial intelligence tool ChatGPT, claiming that the organisation breached copyright law by "training" its model on novels without the permission of authors. Mona Awad, whose books include Bunny and 13 Ways of Looking at a Fat Girl, and Paul Tremblay, author of The Cabin at the End of the World, filed the class action complaint to a San Francisco federal court last week."

"describing how they had created an A.I. model to instantly translate the ancient glyphs. The team, led by a Google software engineer and an Assyriologist from Ariel University, trained the model on existing cuneiform translations using the same technology that powers Google Translate."

"But the uniqueness of TSMC and its location on an island that the Chinese regime regards as part of the mainland is giving rise to strategic panic. Both the US and the EU are racing to try to ensure that they have 2nm chip-fabrication capability within their respective jurisdictions. The problem is that one cannot conjure up such capacity just by throwing money at it. TSMC itself has built a fabrication plant in Arizona. But in his speech marking the ceremonial opening of the facility last December, Morris Chang, the firm's founder, said that it could not find enough qualified American workers to run it. It was sending every new American recruit to Taiwan for 18 months of training and was even importing engineers from Taiwan to make the Arizona plant operational. Hopefully it will all be up and running before Xi decides to "do a Putin" and we will no longer to be able to have chips with everything."

"One of the many policy-based projects I worked on at Meta was Engagement Bait, which is defined as "a tactic that urges people to interact with Facebook posts through likes, shares, comments, and other actions in order to artificially boost engagement and get greater reach." Accordingly, "Posts and Pages that use this tactic will be demoted." To do this, "models are built off of certain guidelines" trained using "hundreds of thousands of posts" that "teams at Facebook have reviewed and categorized." The examples provided are obvious (eg. a post saying "comment "Yes" if you love rock as much as I do"), but the problem is that there will always be far subtler ways to get people to engage with something artificially. As an example, psychology researchers have a long history of studying negativity bias, which has been shown to operate across a wide array of domains, and to lead to increased online engagement. "

"Similarly to how voice cloning raises fears about the consequences of its potential misuse, increasingly many people worry that the proliferation of AI technology will put professionals' livelihoods at risk. At Eleven, we see a future in which voice actors are able to license their voices to train speech models for specific use, in exchange for fees. Clients and studios will still gladly feature professional voice talent in their projects and using AI will simply contribute to faster turnaround times and greater freedom to experiment and establish direction in early development. The technology will change how spoken audio is designed and recorded but the fact that voice actors no longer need to be physically present for every session really gives them the freedom to be involved in more projects at any one time, as well as to truly immortalize their voices."

"DATA LEVERAGE CAN be deployed through at least four avenues: direct action (for instance, individuals banding together to withhold, "poison," or redirect data), regulatory action (for instance, pushing for data protection policy and legal recognition of "data coalitions"), legal action (for instance, communities adopting new data-licensing regimes or pursuing a lawsuit), and market action (for instance, demanding large language models be trained only with data from consenting creators). "