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 10 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
Tawakol: Multitasking during meetings drops your cognitive performance by 20 IQ points
“You are literally 20 IQ points dumber when you are multitasking, and everybody's multitasking, and that's the problem.”
Tawakol: AI data flywheels take years to build and create strong moats
“So you start to get much more nuanced and better algorithm results, which lets you extend the surface area of what you're doing way beyond your competitors, which then gives a better experience, and you get a virtuous cycle, and it takes years to develop these…”
Tawakol: Most AI companies forget to label false negatives in their data
“False negatives are harder And most companies will do solutions like this and forget to figure out how to label the false negatives.”
Tawakol: Enterprise AI demands significantly higher accuracy tolerances than consumer AI
“If you tell Alexa to play a song and she plays the wrong one, no problem. You tell Eva to update Salesforce and automatically update Salesforce record, and it's the wrong record, there's a problem.”
Tawakol: AI transcription needs human-in-the-loop systems for acceptable user experiences
“What they don't realize, it's pretty hard for a human to understand the last few percentage points of accuracy, how that impacts the experience, and typically in many areas, like in transcription and other areas, it's a pretty bad impact if you're not, you kno…”
Tawakol: Oracle Data Cloud grew into a $500 million business
“By the time, you know, we went into the Oracle data cloud and continued to grow the business and make acquisitions, it was a five hundred million dollar business.”
Tawakol: AI development teams require statistics experts, not just software engineers
“So these kinds of judgment errors are just very commonplace, and that's why you obviously need people on the team who aren't just You know, ah, really good at writing the code, and you also knew people very familiar with statistics.”
Tawakol: Voicera uses human error correction for 1% of processed audio
“We end up doing the error correct about one percentage of the audio time.”
Tawakol: Over 200 companies are building meeting infrastructure software
“There's like, 220 companies doing that.”
Tawakol: Voicera bootstrapped its voice AI using phone transcripts and Mechanical Turk
“Buying available data assets in terms of You know, voice that's out there, voice over the phone, transcribed voice over the phone, which we did, and then we went out and we bought labels through things like Mechanical Turk.”