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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."

"Her intervention emerged after Sir Paul McCartney became the latest star to back calls for laws to stop mass copyright theft by generative AI companies, warning the technology "could just take over". Bush, who shot to fame with Wuthering Heights in 1978 but whose last album was released in 2011, gave a rare interview this year in which she said she was "very keen" to make a new album, saying: "I've got lots of ideas … it's been a long time.""

"Their tools analyze content using sophisticated algorithms, picking up on subtle signals to distinguish the images made with computers from the ones produced by human photographers and artists. But some tech leaders and misinformation experts have expressed concern that advances in A.I. will always stay a step ahead of the tools. To assess the effectiveness of current A.I.-detection technology, The New York Times tested five new services using more than 100 synthetic images and real photos. The results show that the services are advancing rapidly, but at times fall short."

"This election is the first time we face the danger of post-ChatGPT AI in a high-stakes sociopolitical scenario. That is new. Bots pass the Turing test (which means they write and speak indistinguishably from real people). They're also more persuasive. Image and video generators can make realistic faces, which people use mostly to make jokes but also to plant doubt. We will eventually adapt to perception-altering algorithms but for now, they're a big unsolved problem. Here are five ways the bad guys can weaponize AI to influence the US democratic election."

"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."

"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."

"The European Commission has asked X to hand over internal documents about its algorithms, as it steps up its investigation into whether Elon Musk's social media platform has breached EU rules on content moderation. The EU's executive branch told the company it wanted to see internal documentation about its "recommender system", which makes content suggestions to users, and any recent changes made to it, by 15 February. X has been under investigation since December 2023 under the EU's content law - known as the Digital Services Act (DSA) - over how it tackles the spread of illegal content and information manipulation. The company has been accused of manipulating the platform's systems to give far-right posts and politicians greater visibility over other political groups."

"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.""

"Writing in the journal Science, Summerfield and colleagues from Google DeepMind report how they built the "Habermas Machine" - an AI system named after the German philosopher Jürgen Habermas. The system works by taking written views of individuals within a group and using them to generate a set of group statements designed to be acceptable to all. Group members can then rate these statements, a process that not only trains the system but allows the statement with the greatest endorsement to be selected."

"Many respondents said they used chatbots to help them manage different aspects of their lives, from improving their mental and physical health to advice about existing romantic relationships and experimenting with erotic role play. They can spend between several hours a week to a couple of hours a day interacting with the apps. Worldwide, more than 100 million people use personified chatbots, which include Replika, marketed as "the AI companion who cares" and Nomi, which claims users can "build a meaningful friendship, develop a passionate relationship, or learn from an insightful mentor"."

""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"."

"The paper, co-authored by researchers inside and outside Google, contended that technology companies could do more to ensure AI systems aimed at mimicking human writing and speech do not exacerbate historical gender biases and use of offensive language, according to a draft copy seen by Reuters."

"For higher-complexity problems, however, the models would enter "collapse", failing to generate any correct solutions. In one case, even when provided with an algorithm that would solve the problem, the models failed. The paper said: "Upon approaching a critical threshold - which closely corresponds to their accuracy collapse point - models counterintuitively begin to reduce their reasoning effort despite increasing problem difficulty." The Apple experts said this indicated a "fundamental scaling limitation in the thinking capabilities of current reasoning models"."

"A POPULAR news outlet has been publishing articles written by AI since November, keeping it on the down low. Tech media site CNET has been publishing the articles since November, and lots of readers don't seem to have noticed."

"Unskilled manual laborers have felt the pressure of automation for a long time - but, increasingly, they're not alone. The last few years have been a bonanza of advances in artificial intelligence. As our software gets smarter, it can tackle harder problems, which means white-collar and pink-collar workers are at risk as well. Here are eight jobs expected to be automated (partially or entirely) in the coming decades. Call Center Employees call-center Telemarketing used to happen in a crowded call center, with a group of representatives cold-calling hundreds of prospects every day. Of those, maybe a few dozen could be persuaded to buy the product in question. Today, the idea is largely the same, but the methods are far more efficient. Many of today's telemarketers are not human. In some cases, as you've probably experienced, there's nothing but a recording on the other end of the line. It may prompt you to "press '1' for more information," but nothing you say has any impact on the call - and, usually, that's clear to you. But in other cases, you may get a sales call and have no idea that you're actually speaking to a computer. Everything you say gets an appropriate response - the voice may even laugh. How is that possible? Well, in some cases, there is a human being on the other side, and they're just pressing buttons on a keyboard to walk you through a pre-recorded but highly interactive marketing pitch. It's a more practical version of those funny soundboards that used to be all the rage for prank calls. Using soundboard-assisted calling - regardless of what it says about the state of human interaction - has the potential to make individual call center employees far more productive: in some cases, a single worker will run two or even three calls at the same time. In the not too distant future, computers will be able to man the phones by themselves. At the intersection of big data, artificial intelligence, and advanced

"Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called "AI hallucinations". We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems."

"Or, as the New York Times summarised it, "doctors who were given ChatGPT-4 along with conventional resources did only slightly better than doctors who did not have access to the bot. And, to the researchers' surprise, ChatGPT alone outperformed the doctors." More interesting, though, were two other revelations: the experiment demonstrated doctors' sometimes unwavering belief in a diagnosis they had made, even when ChatGPT suggested a better one; and it also suggested that at least some of the physicians didn't really know how best to exploit the tool's capabilities. Which in turn revealed what AI advocates such as Ethan Mollick have been saying for aeons: that effective "prompt engineering" - knowing what to ask an LLM to get the most out of it - is a subtle and poorly understood art."

"The climate crisis could prove AI's greatest challenge. While Google publicises AI-driven advances in flooding, wildfire and heatwave forecasts, like many big tech companies, it uses more energy than many countries. Today's large models are a major culprit. It can take 10 gigawatt-hours of power to train a single large language model like OpenAI's ChatGPT, enough to supply 1,000 US homes for a year."

"Lexplore claims its technology is new-particularly the algorithm that separates typical from atypical readers. But the concepts it's based on aren't. Its tech draws from a deep well of previously-conducted research stretching back decades, which is generally supportive of using a combination of eye tracking and machine learning to screen for dyslexia. "Eye movements is one of the best ways to index reading ability at an incredibly in-depth level," says Julie Kirkby, a psychology professor at Bournemouth University in the United Kingdom, who has studied eye tracking and dyslexia for years."