Row 66196

Row ID: 66196 | Dataset Entry | Axioma AXP Content Repository

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In 2017: "My view is throw it all away and start again."

> But Hinton suggested that, to get to where neural networks are able to become intelligent on their own, what is known as "unsupervised learning," "I suspect that means getting rid of back-propagation." > "I don't think it's how the brain works," he said. "We clearly don't need all the labeled data."  https://www.axios.com/2017/12/15/artificial-intelligence-pioneer-says-we-need-to-start-over-1513305524

From last year:

> “I have suddenly switched my views on whether these things are going to be more intelligent than us.”

>For 40 years, Hinton has seen artificial neural networks as a poor attempt to mimic biological ones. Now he thinks that’s changed: in trying to mimic what biological brains do, he thinks, we’ve come up with something better. “It’s scary when you see that,” he says. “It’s a sudden flip.”

>Hinton’s fears will strike many as the stuff of science fiction. But here’s his case.

>As their name suggests, large language models are made from massive neural networks with vast numbers of connections. But they are tiny compared with the brain. “Our brains have 100 trillion connections,” says Hinton. “Large language models have up to half a trillion, a trillion at most. Yet GPT-4 knows hundreds of times more than any one person does. So maybe it’s actually got a much better learning algorithm than us.”

https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/

OP, are you familiar with Adaptive Resonance Theory?

FieldValue
text In 2017: "My view is throw it all away and start again." > But Hinton suggested that, to get to where neural networks are able to become intelligent on their own, what is known as "unsupervised learning," "I suspect that means getting rid of back-propagation." > "I don't think it's how the brain works," he said. "We clearly don't need all the labeled data."  https://www.axios.com/2017/12/15/artificial-intelligence-pioneer-says-we-need-to-start-over-1513305524  From last year: > “I have s…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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Raw Record

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  "text": "In 2017: \"My view is throw it all away and start again.\"\n\n\n> But Hinton suggested that, to get to where neural networks are able to become intelligent on their own, what is known as \"unsupervised learning,\" \"I suspect that means getting rid of back-propagation.\" > \"I don't think it's how the brain works,\" he said. \"We clearly don't need all the labeled data.\"  https://www.axios.com/2017/12/15/artificial-intelligence-pioneer-says-we-need-to-start-over-1513305524 \n\n\n\n\nFrom last year:\n\n\n> “I have suddenly switched my views on whether these things are going to be more intelligent than us.”\n\n\n>For 40 years, Hinton has seen artificial neural networks as a poor attempt to mimic biological ones. Now he thinks that’s changed: in trying to mimic what biological brains do, he thinks, we’ve come up with something better. “It’s scary when you see that,” he says. “It’s a sudden flip.”\n\n\n\n>Hinton’s fears will strike many as the stuff of science fiction. But here’s his case. \n\n\n>As their name suggests, large language models are made from massive neural networks with vast numbers of connections. But they are tiny compared with the brain. “Our brains have 100 trillion connections,” says Hinton. “Large language models have up to half a trillion, a trillion at most. Yet GPT-4 knows hundreds of times more than any one person does. So maybe it’s actually got a much better learning algorithm than us.”\n\n\nhttps://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/ \n\n\n\n\n\n\n\n\nOP, are you familiar with Adaptive Resonance Theory? ",
  "label": "r/machinelearning",
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  "datetime": "2024-05-23",
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