Marily Nika: prediction

Transcript context

…Yeah, my guess is most startups are going to have nowhere near enough data to build their own model and make it something really interesting. So do you have a thought on when it makes sense to try to build your own model, try to train your own GPT type thing versus use something that's already out there like say GPT or aJourney or all those guys. If you are a big tech company and you're offering a service that is going to do speech or permission or then it's going to have their own tragedy, you want to use more data and more diverse data to train and retain and train because if you don't then your quality is going to be the same as every other companies. There are agencies that are selling data, packages of data, that are ready so you can get them and train your models. But the question is if everyone takes that exact dataset, then the quality that every single company is producing is going to be the exact same. So you do want to diversify, you do want to collect your own data. And I guess a good question from my prem perspective is when is the quality of your product good enough to lunch? And that is a really interesting point because it's totally your responsibility as a PM to decide, "Okay, the recognition of whether this folder is a category dog is good enough for the users, it's like 70% accurate, 80% accurate, where is the bar, where do we launch?" And that's why I'm like the AI PM role is so cool because you have problems like that to solve that no one else was tackled before. So it's all on you. We've thrown out these words model and we talk about training models. Do you have a good succinct explanation for what a model is for folks that aren't that technical and then just the general idea of training a model. What is a simple way to think of, here is what a model is.…

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