I unintentionally created a biased AI algorithm 25 years ago - tech companies are still... - 0 views
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How and why do well-educated, well-intentioned scientists produce biased AI systems? Sociological theories of privilege provide one useful lens.
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Scientists also face a nasty subconscious dilemma when incorporating diversity into machine learning models: Diverse, inclusive models perform worse than narrow models.
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fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent evidence of this grim reality. The commercial necessity of building market share led to the premature release of these systems.
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The Misinformation Susceptibility Test - 0 views
virtualeconomics: Twitter just quietly became a TV and radio broadcast platform - 0 views
Official Google Blog: Discover more than 3 million Google eBooks from your choice of bo... - 0 views
Google Researchers' Attack Prompts ChatGPT to Reveal Its Training Data - 0 views
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researchers showed that there are large amounts of privately identifiable information (PII) in OpenAI’s large language models. They also showed that, on a public version of ChatGPT, the chatbot spit out large passages of text scraped verbatim from other places on the internet
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ChatGPT’s “alignment techniques do not eliminate memorization,” meaning that it sometimes spits out training data verbatim. This included PII, entire poems, “cryptographically-random identifiers” like Bitcoin addresses, passages from copyrighted scientific research papers, website addresses, and much more.
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The researchers wrote that they spent $200 to create “over 10,000 unique examples” of training data, which they say is a total of “several megabytes” of training data. The researchers suggest that using this attack, with enough money, they could have extracted gigabytes of training data. The entirety of OpenAI’s training data is unknown, but GPT-3 was trained on anywhere from many hundreds of GB to a few dozen terabytes of text data.
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AI Mind Map Generator Online | Taskade - 0 views
Digital Pedagogy - 0 views
William Davies · How many words does it take to make a mistake? Education, Ed... - 0 views
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The problem waiting round the corner for universities is essays generated by AI, which will leave a textual pattern-spotter like Turnitin in the dust. (Earlier this year, I came across one essay that felt deeply odd in some not quite human way, but I had no tangible evidence that anything untoward had occurred, so that was that.)
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To accuse someone of plagiarism is to make a moral charge regarding intentions. But establishing intent isn’t straightforward. More often than not, the hearings bleed into discussions of issues that could be gathered under the heading of student ‘wellbeing’, which all universities have been struggling to come to terms with in recent years.
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I have heard plenty of dubious excuses for acts of plagiarism during these hearings. But there is one recurring explanation which, it seems to me, deserves more thoughtful consideration: ‘I took too many notes.’ It isn’t just students who are familiar with information overload, one of whose effects is to morph authorship into a desperate form of curatorial management, organising chunks of text on a screen. The discerning scholarly self on which the humanities depend was conceived as the product of transitions between spaces – library, lecture hall, seminar room, study – linked together by work with pen and paper. When all this is replaced by the interface with screen and keyboard, and everything dissolves into a unitary flow of ‘content’, the identity of the author – as distinct from the texts they have read – becomes harder to delineate.
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https://globalanticorruptionblog.com/2022/12/20/the-ftx-collapse-and-the-risks-of-crypt... - 0 views
The Myth Of AI | Edge.org - 0 views
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The distinction between a corporation and an algorithm is fading. Does that make an algorithm a person? Here we have this interesting confluence between two totally different worlds. We have the world of money and politics and the so-called conservative Supreme Court, with this other world of what we can call artificial intelligence, which is a movement within the technical culture to find an equivalence between computers and people. In both cases, there's an intellectual tradition that goes back many decades. Previously they'd been separated; they'd been worlds apart. Now, suddenly they've been intertwined.
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Since our economy has shifted to what I call a surveillance economy, but let's say an economy where algorithms guide people a lot, we have this very odd situation where you have these algorithms that rely on big data in order to figure out who you should date, who you should sleep with, what music you should listen to, what books you should read, and on and on and on. And people often accept that because there's no empirical alternative to compare it to, there's no baseline. It's bad personal science. It's bad self-understanding.
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there's no way to tell where the border is between measurement and manipulation in these systems
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