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"This time the model could look at both the surrounding words and the content of the image to fill in the blank. Through millions of repetitions, it could then discover not just the patterns among the words but also the relationships between the words and the elements in each image."

"The model takes a two-pronged approach. First, it focuses on trends present in the region, looking at geostatistics and historical data from Prodes, the annual government monitoring system for deforestation in the Amazon. Understanding what has happened can help make predictions more precise. When already deforested areas are recent, this indicates gangs are operating in the area, so there's a higher risk that nearby forest will soon be wiped out. Second, it looks at variables that put the brakes on deforestation - land protected by Indigenous and quilombola (descendent of rebel slaves) communities, and areas with bodies of water, or other terrain that doesn't lend itself to agricultural expansion, for instance - and variables that make deforestation more likely, including higher population density, the presence of settlements and rural properties, and higher density of road infrastructure, both legal and illegal."

"Getty points to images produced by Stable Diffusion which contain its copyright watermark, suggesting that Stable Diffusion has ingested and is reproducing copyrighted material without permission (Stability AI has not yet commented publicly on the lawsuit). The same level of evidence is harder to come by when examining ChatGPT's text output, but there is no doubt that it has been trained on copyrighted material. OpenAI will be hoping that its text generation is covered by "fair use", a provision in copyright law that allows limited use of copyrighted material for "transformative" purposes. That idea will probably one day be tested in court."

"Most AI programs function like a "black box." "We know exactly what a model does but not why it has now specifically recognized that a picture shows a cat," said computer scientist Kristian Kersting of the Technical University of Darmstadt in Germany to the German-language newspaper Handelsblatt. That dilemma prompted Kersting-along with computer scientists Patrick Schramowski of the Technical University of Darmstadt and Björn Deiseroth, Mayukh Deb and Samuel Weinbach, all at the Heidelberg, Germany-based AI company Aleph Alpha-to introduce an algorithm called AtMan earlier this year. AtMan allows large AI systems such as ChatGPT, Dall-E and Midjourney to finally explain their outputs."

"Can AI and analytics be used in a way that improves operational efficiency without jeopardizing our ethical principles? The answer is "yes" - if moral objectives and constraints, now often treated as an afterthought, are considered from the outset when designing models. We will discuss a recent attempt to combine ethics, analytics, and operational efficiency in the world of organ allocation and examine the lessons it holds for other areas of health care and beyond."

"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."

"A digital twin is a virtual representation of a real system - a building, the power grid, a city, even a human being - that mimics the characteristics of the system. A digital twin is more than just a computer model, however. It receives data from sensors in the real system to constantly parallel the system's state."

"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. "

"This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions."

"As the miles grow, the odds shrink. At some point, a car driving autonomously or semi-autonomously will cause a fatal accident. If their performance is remotely comparable to a human's, that moment could come within the next 18-24 months. If so, by the law of averages it will probably involve a Tesla Model 3. Self-driving cars may be about to have their Driscoll moment."

"Ringing Bells also plans to sell other more expensive handsets - ranging in price up to about $100 (£75) - at a profit. But, with just over a week to go until Freedom 251's launch, critics remain unconvinced. "I find it difficult to believe that any sort of phone can be manufactured for 251 rupees, so it's difficult to see what kind of business model they have," says Pranav Dixit, a tech expert at the news site Factor Daily."

"Each of these community health workers typically works with 100 families, and they used to hand-deliver patient updates to doctors by foot. But by equipping these individuals with $10 cellphones, Medic Mobile helped to create a hub-and-spoke model of health care that's revolutionized the way millions of people get well."

"First, the drones engage in aerial mapping to create detailed three-dimensional terrain models. They then begin "precision planting" by shooting seed pods that have been "pregerminated and covered in a nutritious hydrogel" into the soil. Finally, drones monitor tree growth over the course of a number of "planting audits," designed to track the reforrestation progress. "

The need for schools to prepare for 21st century learning was top of the agenda at this year's BETT conference.They must embrace mobile technologies, games, podcasts and social networking, according to leading educationalist Professor Stephen Heppell.Schools should also break away from traditional classroom and curriculum models, he argued.The gap between those schools embracing technology and those not is getting bigger, he said.Prof Heppell was speaking to delegates at BETT, the world's biggest educational technology show.

"Luckily for the kid, it was only a single tweet. Had several students gotten together to form a study group, they might have been prosecuted for felony interference with a business model and gotten the death penalty. Do you suppose Pearson has hidden microphones set up around the schools so they can also listen in and see if students discuss the tests during lunch? I ask because that is basically the offline version of the student's infraction."

"Teatreneu's administrators found an ingenious solution: partnering with the advertising agency Cyranos McCann, they fitted the back of every seat with fancy tablets that can analyse facial expressions. Under the new model, visitors enter the club for free but have to pay 30 cents for every laugh recognised by the tablet - with a cap of €24 (or 80 laughs) per show. A mobile app makes it easier to complete the payment; the overall ticket prices have reportedly gone up by €6. As a bonus, you can also share your smiling selfie with friends: the path from funny to viral has never been shorter."

"With all the information together, they created a prediction model which can guess with 80% accuracy whether or not that user will go on to be banned from just their first five posts. "

"The founders of Predictim want to be clear with me: Their product-an algorithm that scans the online footprint of a prospective babysitter to determine their "risk" levels for parents-is not racist. It is not biased. "We take ethics and bias extremely seriously," Sal Parsa, Predictim's CEO, tells me warily over the phone. "In fact, in the last 18 months we trained our product, our machine, our algorithm to make sure it was ethical and not biased. We took sensitive attributes, protected classes, sex, gender, race, away from our training set. We continuously audit our model. And on top of that we added a human review process.""

"Amid mounting financial pressure, at least a dozen police forces are using or considering predictive analytics, despite warnings from campaigners that use of algorithms and "predictive policing" models risks locking discrimination into the criminal justice system."