Group items tagged

"Eck said machine learning, a powerful form of AI, will be integrated into how humans communicate with each other. He raised the idea of "assistive writing" in the future with Google Docs, the company's online word processing software. This may be based on Google's upcoming Smart Compose technology that suggests words and phrases based on what's being typed. Teachers used to worry about whether students used Wikipedia for their homework. Now they may wonder what part of the work the students wrote themselves, Eck said."

"At the heart of the problem that troubles Ming is the training that computer engineers receive and their uncritical faith in AI. Too often, she says, their approach to a problem is to train a neural network on a mass of data and expect the result to work fine. She berates companies for failing to engage with the problem first - applying what is already known about good employees and successful students, for example - before applying the AI."

"Kahun still relies on its own vast repository of medical knowledge - over 30 million insights from trusted sources - but ChatGPT will now allow patients to describe their symptoms in their own words. Until now it's been a structured conversation, with the AI asking a question, the patient responding, and the AI working its way through a series of more detailed questions based on the answers it gets. Integrating ChatGPT puts the patient in control. They describe their symptoms exactly as they would to a doctor, and ChatGPT responds, just as their doctor would."

"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."

""As with all technology, there are caveats around making sure that it is used responsibly and not as a licence to cheat, but none of that is insurmountable," he said. In contrast, New York City schools have already banned the use of ChatGPT on all devices and networks because of concerns it will encourage plagiarism. Dr Thomas Lancaster, a computer scientist working at Imperial College London, best known for his research into academic integrity, contract cheating and plagiarism, said it was in many ways a game changer. He said: "It's certainly a major turning point in education where universities have to make big changes."

"However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself."

"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.""

" "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.""

"YouTube will rely more on machine learning and less on human reviewers during the coronavirus outbreak. Normally, algorithms detect potentially harmful content and send it to human reviewers for assessment. But these are not normal times, and in an effort to reduce the need for employees and contractors to come into an office, YouTube will allow its automated system to remove some content without human review."

"That can include statistical or machine learning algorithms that rely on police records detailing the time, location, and nature of past crimes in a bid to predict if, when, where, and who may commit future infractions. In theory, this should help authorities use resources more wisely and spend more time policing certain neighborhoods that they think will yield higher crime rates."

"We need to look at the nose to tail production of artificial intelligence. The seeds of the data problem were planted in the 1980s, when it became common to use data sets without close knowledge of what was inside, or concern for privacy. It was just "raw" material, reused across thousands of projects."

"The intriguing echo of Eliza in thinking about ChatGPT is that people regard it as magical even though they know how it works - as a "stochastic parrot" (in the words of Timnit Gebru, a well-known researcher) or as a machine for "hi-tech plagiarism" (Noam Chomsky). But actually we do not know the half of it yet - not the CO2 emissions incurred in training its underlying language model or the carbon footprint of all those delighted interactions people are having with it. Or, pace Chomsky, that the technology only exists because of its unauthorised appropriation of the creative work of millions of people that just happened to be lying around on the web? What's the business model behind these tools? And so on. Answer: we don't know."

"In Neural Photo Editing With Introspective Adversarial Networks, a group of University of Edinburgh engineers and a private research colleague describe a method for using "introspective adversarial networks" to edit images in realtime, which they demonstrate in an open project called "Neural Photo Editor" that "enhances" photos by predicting what should be under your brush."

"Microsoft's decision to replace human journalists with robots has backfired, after the tech company's artificial intelligence software illustrated a news story about racism with a photo of the wrong mixed-race member of the band Little Mix."

"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."