Overcoming the Cold Start Problem: How We Make Intractable Tasks Tractable


This post is by Azin Asgarian from Georgian


by Azin Asgarian and Franziska Kirschner

I woke up this morning (somewhat unsurprisingly). After poking my head out of the covers and feeling a cold chill in the air, I made the executive decision not to proceed with this course of action and promptly withdrew back under the covers. Ten minutes later, my alarm decides it is time to snooze no more and kindly invites me to follow suit. Begrudgingly I got up; another winter morning plagued by the Cold Start Problem.

In the same way owners look like their dogs, Machine Learning Scientists think like their AI. At Tractable, our AI for accident and disaster recovery suffers from the Cold Start Problem too, albeit all year round. Imagine you have an AI that is, say, really good at spotting scratches on Toyota Priuses, because for some reason you chanced upon a large, labelled dataset of Toyota Priuses that may or may not be damaged. Once the AI has trained by looking at all these images, it becomes a pro at knowing when a Prius is damaged and when it’s not.

Picture of a Prius
A Toyota Prius – Photo by Sava Bobov on Unsplash

Now, imagine you want to scale your AI to detect scratches on Lamborghini Aventadors instead. But you don’t have a large, labelled dataset of Lamborghini Aventadors. Your AI can’t really make an accurate judgement, simply because it doesn’t know what a Lamborghini Aventador is meant to look like, scratched or not. The AI, like the researcher who trained it, (Read more...)