Alexandr Wang

Yeah. So, okay. So with these algorithms, one key ingredient for these algorithms is data.

Yeah. So, okay. So with these algorithms, one key ingredient for these algorithms is data.

Yeah. So, okay. So with these algorithms, one key ingredient for these algorithms is data.

Yeah. So basically you kind of have, yeah, you have three, you have three key pieces. Okay. So you have the, you have the computational powers of the chips, you have the chips, you have the data, which is like just tons and tons of data that that's where the algorithms are learning the patterns from. So these algorithms, they aren't just like, they don't just learn to talk randomly.

Yeah. So basically you kind of have, yeah, you have three, you have three key pieces. Okay. So you have the, you have the computational powers of the chips, you have the chips, you have the data, which is like just tons and tons of data that that's where the algorithms are learning the patterns from. So these algorithms, they aren't just like, they don't just learn to talk randomly.

Yeah. So basically you kind of have, yeah, you have three, you have three key pieces. Okay. So you have the, you have the computational powers of the chips, you have the chips, you have the data, which is like just tons and tons of data that that's where the algorithms are learning the patterns from. So these algorithms, they aren't just like, they don't just learn to talk randomly.

They learn it from learning to talk from how humans talk. Right. Got it. So you need tons and tons of data. And then you have the algorithms which learn from all that data, and then they run on top of the chips. Got it. So then one of the big challenges in the industry is, okay, how are you going to produce all this data?

They learn it from learning to talk from how humans talk. Right. Got it. So you need tons and tons of data. And then you have the algorithms which learn from all that data, and then they run on top of the chips. Got it. So then one of the big challenges in the industry is, okay, how are you going to produce all this data?

They learn it from learning to talk from how humans talk. Right. Got it. So you need tons and tons of data. And then you have the algorithms which learn from all that data, and then they run on top of the chips. Got it. So then one of the big challenges in the industry is, okay, how are you going to produce all this data?

Exactly. How do you build all that data and how do you do that in the most effective way? How do you build new data?

Exactly. How do you build all that data and how do you do that in the most effective way? How do you build new data?

Exactly. How do you build all that data and how do you do that in the most effective way? How do you build new data?

That definitely affects the output. So this whole data, so data is, you know, some people say like data is the new oil or data is the new gold. Like data is really, really valuable because it's how the algorithms are learning everything that they're learning. Like anything that the algorithms know or learn or say or do, all that has to come from the data that goes into it.

That definitely affects the output. So this whole data, so data is, you know, some people say like data is the new oil or data is the new gold. Like data is really, really valuable because it's how the algorithms are learning everything that they're learning. Like anything that the algorithms know or learn or say or do, all that has to come from the data that goes into it.

That definitely affects the output. So this whole data, so data is, you know, some people say like data is the new oil or data is the new gold. Like data is really, really valuable because it's how the algorithms are learning everything that they're learning. Like anything that the algorithms know or learn or say or do, all that has to come from the data that goes into it.

Yeah, yeah, yeah. So then we don't spend enough time talking about how are you going to get this data and how are you going to keep making new data. So the angle that we took at scale was to kind of – turn this into an opportunity for people. So we're kind of like the Uber for AI. So just like how Uber, you have riders and drivers. For us, we have the AI systems, the algorithms that need data.

Yeah, yeah, yeah. So then we don't spend enough time talking about how are you going to get this data and how are you going to keep making new data. So the angle that we took at scale was to kind of – turn this into an opportunity for people. So we're kind of like the Uber for AI. So just like how Uber, you have riders and drivers. For us, we have the AI systems, the algorithms that need data.

Yeah, yeah, yeah. So then we don't spend enough time talking about how are you going to get this data and how are you going to keep making new data. So the angle that we took at scale was to kind of – turn this into an opportunity for people. So we're kind of like the Uber for AI. So just like how Uber, you have riders and drivers. For us, we have the AI systems, the algorithms that need data.

And then we have a community of people, a network of people who help produce the data that go into the system. And they get paid to do that.

And then we have a community of people, a network of people who help produce the data that go into the system. And they get paid to do that.