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it's really interesting they do such a good job AWS that when AWS does go down which seems to be like some portion of it goes down you know north east whatever it sometimes is regional or some section of it goes down it's almost like people have a funny joking like it's a snow day response to it for three or four hours
they're not allowed to scrape kora kora doesn't let them index it right I think they're still in the standoff
we interact with lots of machine learning systems already Google search is a is a large federated machine learning system today it's very very influenced by bike or deep learning machine learning etc and and it is extremely reliable it works like running water it's great
on a Waymo vehicle or on a Cruise vehicle or whatnot they very much work together
Google bet the farm on lidar
bigger than the internet and bigger than the silicon chip being you know CPU is being created I think it's more comparable to to the the advent of computing than it is to the advent of the internet
I think we fundamentally view AI and machine learning as kind of a once in a once a generation shift in technology might be once in a species by the way
you would go to a customer is way mo or uber a customer yeah exactly are both customers they are both customers got it and you could say that it's public knowledge yes okay so they're both customers
we do bear a quality responsibility with our customers and that we sign up for the quality of data we give to our customers
we we don't have any customers in China we work with some us arms of Chinese companies god I do for example got it by Drew's got a self-driving they have every us arm that works on self-driving and other machine learning efforts
somebody like waymo could say hey here's here's 10 million miles of driving have at it ... we then also do we also have...
we have a large team of smart well trained humans who can basically go through and spot errors that these that are made
I think now the team is about a hundred fifty folks mosty here in San Francisco Bay Area
we're really providing this sort of this infrastructure layer for machine learning globally or AI globally
the core problem as you just laid out is that machines don't know what to do unless they have data that actually tells them what they're supposed to be doing right and so what that means is one of the the huge bottlenecks for machine learning is is data ends up being like data that tells these algorithms tell these models what they're supposed to be doing and and that's where that's where scale comes in
the core way that our our whole pipeline works is that it's it's a lot of work is done behind the scenes by machines and our own our own AI models originally and then humans basically give input and correct mistakes to make sure that that the end data is extremely accurate because that that ultimately is what's important for the safety of these systems
you would go to a customer is way mo or uber a customer yeah exactly are both customers they are both customers
what we are is sort of this data refinery if you will we we accept a bunch of raw data from our customers we go through and process it and we sort of we tell the Machine what it should be doing