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"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."

"Researchers have shown that given access to only an API, a way to remotely use software without having it on your computer, it's possible to reverse-engineer machine learning algorithms with up to 99% accuracy. In the real world, this would mean being able to steal AI products from companies like Microsoft and IBM, and use them for free. Small companies built around a single machine learning API could lose any competitive advantage."

"O'Neill recounts an exercise to improve service to homeless families in New York City, in which data-analysis was used to identify risk-factors for long-term homelessness. The problem, O'Neill describes, was that many of the factors in the existing data on homelessness were entangled with things like race (and its proxies, like ZIP codes, which map extensively to race in heavily segregated cities like New York). Using data that reflects racism in the system to train a machine-learning algorithm whose conclusions can't be readily understood runs the risk of embedding that racism in a new set of policies, these ones scrubbed clean of the appearance of bias with the application of objective-seeming mathematics. "

"The firm's software uses machine learning (ML), an advanced form of artificial intelligence, to detect patterns in user behaviour that could indicate attempts to cheat. Its technology can also automatically mark multiple-choice answers and mathematics exams. In addition, it checks each exam-sitter's identity using the webcam, to ensure that no-one else is sitting the test for them. The Better Examinations program also temporarily restricts access to the internet, or certain websites and applications on each person's computer. "

"Over the years critics have pointed out their many shortcomings as well. Perhaps the biggest flaw of all is that the laws are vague. If machines become so human that we find it difficult to tell them and us apart, how will a machine tell the difference? Where does humanity end and artificial intelligence begin? And even if an AI can distinguish itself from a human being, we also cannot know what loopholes and reprogramming a robot is capable of. Surely an AI more clever than us could plan a way to access its core and bypass any of its existing limitations."

"Kellin Pelrine, an American player who is one level below the top amateur ranking, beat the machine by taking advantage of a previously unknown flaw that had been identified by another computer. But the head-to-head confrontation in which he won 14 of 15 games was undertaken without direct computer support. The triumph, which has not previously been reported, highlighted a weakness in the best Go computer programs that is shared by most of today's widely used AI systems, including the ChatGPT chatbot created by San Francisco-based OpenAI. The tactics that put a human back on top on the Go board were suggested by a computer program that had probed the AI systems looking for weaknesses. The suggested plan was then ruthlessly delivered by Pelrine."

"His software is not labeled anything as grand as artificial intelligence. It's machine learning, facilitating and extending his own words, his own imagination. At one level, it merely helps him do what fledgling writers have always done - immerse themselves in the works of those they want to emulate. Hunter Thompson, for instance, strived to write in the style of F. Scott Fitzgerald, so he retyped "The Great Gatsby" several times as a shortcut to that objective."

"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.""

"a word that does not exist; it was invented, defined and used by a machine learning algorithm. "

"Do androids dream of electric betrayal? That's just one question looming over this cover of "Jolene," made by the musician Holly Herndon using her "deepfake" digital twin Holly+, built to replicate the artist's own singing voice using machine learning technology."

"He had discovered that human learning was communal and interactive. For a robot, the acquisition of language was abstract and formulaic. For us, it was embodied, emotive, subjective, quivering with life. The future of intelligence wouldn't be found in our machines, but in the development of our own minds."

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

"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."

"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

" "Why isn't my face being detected? We have to look at how we give machines sight," she said in a TED Talk late last year. "Computer vision uses machine-learning techniques to do facial recognition. You create a training set with examples of faces. However, if the training sets aren't really that diverse, any face that deviates too much from the established norm will be harder to detect.""

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"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.""

"In October 2016, algorithms reacted to negative news headlines about Brexit negotiations by sending the pound down 6% against the dollar in under two minutes, before recovering almost immediately. Knowing which particular headline, or which particular algorithm, caused the crash is next to impossible. When one haywire algorithm started placing and cancelling orders that ate up 4% of all traffic in US stocks in October 2012, one commentator was moved to comment wryly that "the motive of the algorithm is still unclear"."

"The development of autonomous military systems - dubbed "killer robots" by campaigners opposed to them - is deeply contentious. Earlier this year, Google withdrew from the Pentagon's Project Maven, which uses machine learning to analyse video feeds from drones, after ethical objections from the tech giant's staff. The government insists it "does not possess fully autonomous weapons and has no intention of developing them". But, since 2015, the UK has declined to support proposals put forward at the UN to ban them. Now, using government data, Freedom of Information requests and open-source information, a year-long investigation reveals that the MoD and defence contractors are funding dozens of artificial intelligence programmes for use in conflict."