The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 8 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Luis von Ahn: Paying crowdsourced workers is ineffective due to spam
“I have found that paying people is not so good. Then you really have to spend a lot of effort trying to stop people who are just there to, you know, get your money.”
Luis von Ahn: Many AI systems are just fancy ontologies
“A lot of the things that are quote unquote called AI or artificial intelligence, a lot of times are just fancy ontologies.”
Luis von Ahn: Deep learning requires vast human-entered ground truth data
“The other way in which humans are needed here is to create the ground truth. All of these deep learning or deep AI algorithms need A ton of ground truth in order to get very accurate. It has to be entered by humans.”
von Ahn: In 2017, humans still beat AI at basic image recognition
“Computers are better at playing Go than humans are, but humans are still better at recognizing whether a picture has a cat or not.”
Luis von Ahn: Most of the legal system is extremely inconsistent
“Most of our legal system is extremely inconsistent.”
von Ahn: reCAPTCHA digitized 2 to 3 million books annually
“There are a hundred million books that needed to be digitized. That was the total number of books that has ever been written, you know, before the digital era was one hundred million. At the pace that we were going, we were able to digitize about two to three …”
reCAPTCHA required 10 matching user responses to establish ground truth text
“And the way we got the ground truth here is, is simply by having, you know, 10 different people agree with each other. So if you gave the same word to 10 different people and they all agreed that we consider that ground truth that to a very large extent that w…”
von Ahn: Profiling crowdsourced contributor accuracy enables precision with only 3-4 human checks
“When you're doing things that are very large scale and you don't have very many humans, you may have a thousand humans or 10,000 humans. And if you need to label millions of things. You can't afford to start giving, you know, the same thing to all 10,000 human…”