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A bunch of aspects I hadn't considered in here, from science/tech writer Jon Gertner.
Wikipedia’s Moment of Truth
Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?
The goal of Wikipedia, as its co-founder Jimmy Wales described it in 2004, was to create “a world in which every single person on the planet is given free access to the sum of all human knowledge.” The following year, Wales also stated, “We help the internet not suck.” Wikipedia now has versions in 334 languages and a total of more than 61 million articles. It consistently ranks among the world’s 10 most-visited websites yet is alone among that select group (whose usual leaders are Google, YouTube and Facebook) in eschewing the profit motive.
Despite its stodgy appearance, Wikipedia is more tech-savvy than casual users might assume.
“The ability to generate an answer has fundamentally shifted,” he says, noting that in a ChatGPT answer there is “literally no citation, and no grounding in the literature as to where that information came from.”
Almost certainly, that makes A.I. both more difficult to contend with and potentially more harmful, at least from Wikipedia’s perspective. https://www.nytimes.com/2023/07/18/m...i-chatgpt.html
The goal of Wikipedia, as its co-founder Jimmy Wales described it in 2004, was to create “a world in which every single person on the planet is given free access to the sum of all human knowledge.”
Well, that's laughable if he said that. Much of human knowledge is internal and not subject / possible to be put in print. Think of your life experiences and memories which are certainly 'knowledge' to you, and yet you couldn't put them into print and sufficiently convey the details. There will always be that realm of knowledge that the brain gets and knows, but won't / can't be shared with others.
Sounds like Jimmy has a bit of a God-complex as to his abilities.
Last edited by Thoreau424; 08-08-2023 at 11:42 AM..
ChatGPT needs access to some sort of knowledge base to critique its responses before it prints them out. I'm thinking something like the master and emissary model of hemispheric brain function. Even if that model is inaccurate it might work. Neural networks are based on a flawed understanding of neuroscience but they still work.
Combine the master (knowledge base) with the emissary (ChatGPT) in some sort of GAN arrangement.
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