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 5 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
Bloom: Very few data science teams prioritize model explainability
“Explainability or interpretability turned out to be a very, very important optimization that very few data science teams will be cognizant of unless they're really thinking about it.”
Bloom: ML platform decisions must center on production and maintenance costs
“The other ones that I think are critical when making those decisions about which platforms to use and implement within your own organizations is, what is that cost and time to actually put this into production? And then once you put it into production, what is…”
Bloom: Support headcount still scales linearly with incoming ticket volume
“Support is still that last sort of place where you wind up having to scale the total number of people in your support system and support operations by the number of incoming tickets.”
Bloom: Feedback loops transition ML from human augmentation to automation
“Over time, if you build the appropriate feedback loops into your systems, the system itself will wind up learning from those processes and get better and better, so you can actually start automating those processes.”
Bloom: Netflix and Google build fault tolerance to handle ML errors
“Netflix and Google, some of the best machine learning companies in the world, and these are their core products, and they still make mistakes. Yet these are not fatal mistakes. They've built fault tolerance into the machine learning.”