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"The researchers found that "increasing machine and AI readership … motivates firms to prepare filings that are more friendly to machine parsing and processing". So far, so predictable. But there's more: "firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers""

"It is in the nature of AI that makers do not, and often cannot, predict what their creations do. We know how to make machines learn. But programmers do not understand completely the knowledge that intelligent computing acquires. If we did, we wouldn't need computers to learn to learn."

"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."

""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"

"FOLLOW US Twitter / Facebook / RSS Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. The downside of this powerful approach is that it means machine learning classifiers can be confounded by confusing, out-of-context elements in a scene, as is demonstrated in The Elephant in the Room, a paper from a trio of Toronto-based computer science academics."

"This is the dystopian nightmare that Russell fears if his discipline continues on its current path and succeeds in creating super-intelligent machines. It's the scenario implicit in the philosopher Nick Bostrom's "paperclip apocalypse" thought-experiment and entertainingly simulated in the Universal Paperclips computer game. It is also, of course, heartily derided as implausible and alarmist by both the tech industry and AI researchers. One expert in the field famously joked that he worried about super-intelligent machines in the same way that he fretted about overpopulation on Mars."

"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

"LeCun argues that even self-supervised learning and learnings from neurobiology won't be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That's because intelligence - even human intelligence - is very specialized, he says. "AGI does not exist - there is no such thing as general intelligence," said LeCun. "We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.""

"But ChatGPT and similar programs are, by design, unlimited in what they can "learn" (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility. Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time."

"This is the broad goal of a new field known as embodied AI, and Li's not the only one embracing it. It overlaps with robotics, since robots can be the physical equivalent of embodied AI agents in the real world, and reinforcement learning - which has always trained an interactive agent to learn using long-term rewards as incentive. But Li and others think embodied AI could power a major shift from machines learning straightforward abilities, like recognizing images, to learning how to perform complex humanlike tasks with multiple steps, such as making an omelet."

"What you may not realize, though, is that machine learning is already all around you, and it can exert a surprising degree of influence over your life. Don't believe me? You might be surprised."

" Critics have pointed out that the old computing adage "garbage in, garbage out" also applies to ML. If the data from which a machine "learns" is biased, then the outputs will reflect those biases. And this could become generalised: we may have created a technology that - however good it is at recommending films you might like - may actually morph into a powerful amplifier of social, economic and cultural inequalities."

"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."

""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.""

"These experiments in computational creativity are enabled by the dramatic advances in deep learning over the past decade. Deep learning has several key advantages for creative pursuits. For starters, it's extremely flexible, and it's relatively easy to train deep-learning systems (which we call models) to take on a wide variety of tasks."

"Expert Moshe Vardi told the American Association for the Advancement of Science (AAAS): "We are approaching a time when machines will be able to outperform humans at almost any task. "I believe that society needs to confront this question before it is upon us: if machines are capable of doing almost any work humans can do, what will humans do?""

"We seem to be safe for the moment, however - the MIT team said it has no interest in taking artificially intelligent horror machines to the next level or exploring their darker possibilities. "We wanted to playfully commemorate humanity's fear of AI, which is a growing theme in popular culture, but we currently have no plans to use the immense power of AI to scare people further," Yanardag said. "The world is already pretty scary!""

"As part of her undergraduate research, Alice Xue studied whether a machine could pass a Visual Turing Test by producing images that people cannot tell were made by a machine. Xue trained an algorithm using 2,192 traditional Chinese landscape paintings collected from art museums. The resulting AI-generated paintings were mistaken for being made by humans 55 per cent of the time."