machine learning

“The quality of your machine learning, if you're going to have a single play button needs to be literally a hundred percent or zero prediction error. And that's never the case, right? So let's say that you have, you know, a one in five hits, four out of five th…”

“We have a kind of central machine learning and search team, but there's a lot of people have expertise in this field to build infrastructure that everybody can use. And what we've done is Because the space is evolving so quickly, like literally every month, li…”

“When we teach people to build machine learning models, we're asking them to make predictions. We're asking them to find correlations. Prediction is inherently about correlation. But when we ask people to make decisions, we're asking them to think about causati…”

“I actually think it's probably easier now to break into AI product management than it was before. So let me kind of hit on that point a little bit more, which is before you actually probably needed to have more of a foundation and background in machine learnin…”

“If you don't have measurements, like in the old machine learning, whatever metrics you use, you're not going to advance. You're going to have V-one and then you're going to have V-two, and you have no way to know if you've made a progress.”

“There's a business analogy of this, which is that, or not actually an analogy, it's called Goodhart's law. It's literally the same thing as overfitting, just for businesses. Goodhart's law just says any metric that becomes a goal ceases to be a good metric.”

“For product and engineering and design people and, you know, those kinds of functions, I actually think that if you are just curious about the stuff works, it doesn't matter at all if you've never done it before. In fact, if you were to filter for people who'v…”