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"Cave wrote in his response: "Since its launch in November last year many people, most buzzing with a kind of algorithmic awe, have sent me songs 'in the style of Nick Cave' created by ChatGPT. There have been dozens of them. Suffice to say, I do not feel the same enthusiasm around this technology. I understand that ChatGPT is in its infancy but perhaps that is the emerging horror of AI - that it will forever be in its infancy, as it will always have further to go, and the direction is always forward, always faster.""

"But by far the most immediate danger is the role that AI data analysis and generation plays in spreading disinformation and extremism on social media. This technology powers bots and amplification algorithms. These have played a direct role in fomenting conflict in many countries. They are helping to intensify racism, conspiracy theories, political extremism and a plethora of violent, irrationalist movements."

"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."

"MIT researcher's at CSAIL have developed a lane-changing algorithm for self-driving cars. the algorithm allows for aggressive lane changes much like the kind only real drivers would be capable of.   it works by computing 'buffer zones' around autonomous vehicles and reassessing them on the fly. MIT uses a mathematically efficient approach which calculates new buffer zones if the default buffer zones lead to performance that's far worse than a human's driver."

"Computer scientists and machine learning researchers are tackling the pandemic the way they know how: compiling datasets and building algorithms to learn from them."

"Brain scans, however, are quite telling, especially when analyzed with an algorithm, Brent and his colleagues discovered. "We're trying to figure out what's going on in somebody's brain when they're thinking about suicide," says Brent.  These scans, taken using fMRI, or functional magnetic resonance imaging, show that strong words such as 'death,' 'trouble,' 'carefree,' and 'praise,' trigger different patterns of brain activity in people who are suicidal, compared with people who are not. That means that people at risk of suicide think about those concepts differently than everyone else-evidenced by the levels and patterns of brain activity, or neural signatures."

"But users began to spot flaws in the feature over the weekend. The first to highlight the issue was PhD student Colin Madland, who discovered the issue while highlighting a different racial bias in the video-conference software Zoom. When Madland, who is white, posted an image of himself and a black colleague who had been erased from a Zoom call after its algorithm failed to recognise his face, Twitter automatically cropped the image to only show Madland."

"Admission algorithms can make or break your academic plans. A Washington Post investigation found 44 schools use prediction software to give applicants a score out of 100 for its admissions process. The score considers different aspects of a student's application from test scores, home address, transcripts, and even what websites they've visited. That's all calculated to rate how strong of a match a student is for a school."

"As it turns out, the best way to beat YouTube horrid algorithm (and protect yourself from accidentally getting radicalized by some Antisemitic Flat-Earth Groomer bullshit) is to simply not interact with the platform, except to watch the video you went there to watch."

"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."

"Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030."

"OpenAI has "discussed and debated quite extensively" when to release a tool that can determine whether an image was made with DALL-E 3, OpenAI's generative AI art model, or not. But the startup isn't close to making a decision anytime soon. That's according to Sandhini Agarwal, an OpenAI researcher who focuses on safety and policy, who spoke with TechCrunch in a phone interview this week. She said that, while the classifier tool's accuracy is "really good" - at least by her estimation - it hasn't met OpenAI's threshold for quality."

"The question of why AI generates fake academic papers relates to how large language models work: they are probabilistic, in that they map the probability over sequences of words. As Dr David Smerdon of the University of Queensland puts it: "Given the start of a sentence, it will try to guess the most likely words to come next.""

""Could teaching be trumped by a learning machine? Are we beginning to glimpse the possibility of machines that teach themselves to teach? They learn what works, what doesn't and deliver ever better performance. We see the embryonic evidence for this in adaptive learning systems, that are truly algorithmic, and do use machine learning, to improve as they deliver. The more students they teach, the better they get. They even tech themselves. This is not science fiction. This is real AI, in real software, delivering real courses, in real institutions." - Donald Clark"

"An algorithm has no tastebuds; a neural net never gets the munchies. So can a robot brain really tell us what we'll want to eat? The question is whether AI systems will be able to excel in the sensual, creative work of tasting and developing new foods - and what we stand to gain or lose by inventing foods that really have our number."

"In October, American teachers prevailed in a lawsuit with their school district over a computer program that assessed their performance. The system rated teachers in Houston by comparing their students' test scores against state averages. Those with high ratings won praise and even bonuses. Those who fared poorly faced the sack. The program did not please everyone. Some teachers felt that the system marked them down without good reason. But they had no way of checking if the program was fair or faulty: the company that built the software, the SAS Institute, regards its algorithm a trade secret and would not disclose its workings."

""It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive. "These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems." Advertisement Venki Ramakrishnan, the president of the Royal Society, called the work "a stunning advance" that had occurred "decades before many people in the field would have predicted"."

"After being set an overarching goal like maximizing profit, they develop their own strategies based on experience of the market, often with little human oversight. The most advanced also use forms of AI whose workings are opaque even if humans wanted to peer inside."

"A startup in Israel is tackling the problem, and saving lives, with a sophisticated algorithm that can analyze the almost infinite number of possible conflicts. Ask any doctor or pharmacist and they'll tell you about the minefield called "drug interaction". That's when a medicine designed to resolve one problem causes an adverse reaction with another medicine designed to resolve another problem. Or with anything else the patient puts in their body."

"Google should know better, given that it already had a "hallucination problem" with its featured snippets(Opens in a new tab) at the top of search results back in 2017. The snippets algorithm seemed to particularly enjoy telling lies about U.S. presidents. Again, what could go wrong?"