Group items tagged

"Jack Poulson was a senior research scientist at Google whose work on machine learning work was used to improve Google's search results; now he's quit the company over its Project Dragonfly, a once-secret plan to launch a censored Chinese search engine; Poulson called the move a "forfeiture of our values." Tech companies find it hard to qualify skilled engineers at any price, and machine learning specialists are especially prize, commanding salaries of $1MM/year or more. "

"At Imperial College London, Murray Shanahan and colleagues are working on a way around this problem using an old, unfashionable technique called symbolic AI. "Basically this meant an engineer labelled everything for the AI," says Shanahan. His idea is to combine this with modern machine learning. Symbolic AI never took off, because manually describing everything quickly proved overwhelming. Modern AI has overcome that problem by using neural networks, which learn their own representations of the world around them. "They decide what is salient," says Marta Garnelo, also at Imperial College."

"More than just a poetic metaphor, the butterfly effect says that there are some things that even the most advanced science can never predict. Well, that list of things just got a lot shorter. Scientists from the University of Maryland have used machine learning to predict chaos."

"In a paper titled "Towards Causal Representation Learning," researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google Research discuss the challenges arising from the lack of causal representations in machine learning models and provide directions for creating artificial intelligence systems that can learn causal representations."

""This is the first time such machine learning tools have been used in this context," says Fluri, "and we found that the deep artificial neural network enables us to extract more information from the data than previous approaches. We believe that this usage of machine learning in cosmology will have many future applications.""

"The greatest propaganda machine in history. Think about it. Facebook, YouTube and Google, Twitter and others - they reach billions of people. The algorithms these platforms depend on deliberately amplify the type of content that keeps users engaged - stories that appeal to our baser instincts and that trigger outrage and fear. It's why YouTube recommended videos by the conspiracist Alex Jones billions of times. It's why fake news outperforms real news, because studies show that lies spread faster than truth. And it's no surprise that the greatest propaganda machine in history has spread the oldest conspiracy theory in history - the lie that Jews are somehow dangerous. As one headline put it, "Just Think What Goebbels Could Have Done with Facebook.""

"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."

"Autonomous machines capable of deadly force are increasingly prevalent in modern warfare, despite numerous ethical concerns. Is there anything we can do to halt the advance of the killer robots?"

"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."

"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."

"Writing about Artificial Intelligence is a challenge. By and large, there are two directions to take when discussing the subject: focus on the truly remarkable achievements of the technology or dwell on the dangers of what could happen if machines reach a level of Sentient AI, in which self-aware machines reach human level intelligence). "

"As soon as 20 years from now, 45 percent of American jobs may be performed by computers, as many as 10 million self-driving cars will be on the roads, and robots-computerized machines-will infiltrate almost every arena of our daily lives, from healthcare to energy production."

"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

"No arguments there, but the term, which stands for "artificial intelligence", has a more storied history than Spielberg and Kubrick's 2001 film. The concept of artificial intelligence goes back to the birth of computing: in 1950, just 14 years after defining the concept of a general-purpose computer, Alan Turing asked "Can machines think?""

"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 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.""

"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.""