Today, we are thrilled to publicly launch Cord and announce our $4.5M seed round led by CRV and including the Y Combinator Continuity Fund, WndrCo, Crane Venture Partners, Harvard Management Company, and Intercom.
When we first started Cord a little more than a year ago, we thought about how similar the state of AI was to the early days of computing and the internet. The potential of the technology was in full sight but the tools and processes surrounding it left much to be desired. Before starting the company, we experienced firsthand how the lack of tools to prepare quality…
For context, I am a cofounder of Cord, a company in the current winter 2021 batch. We are focused on building software to improve data labelling for computer vision.
Before writing this I reviewed as many posts I could find(without having to go past the first page of Google) from people writing about their YC experience. There were fewer than I expected. Most people talk about interviewing and how to get in, which is probably the wider point of interest. I didn’t want this post to be redundant, so I decided to avoid talking about anything that collided with the…
I started academically in physics, but dropped out(sorry, took a leave of absence) of my PhD in the first year and did a long stint in quantitative finance. So out of all the possible topics for my first peer-reviewed published paper: portfolio optimisation, dark matter signatures, density functional theory, I ended up with the topic of…drawing rectangles on a colonoscopy video. I didn’t think it would come to this, but here we are. In reality though, drawing boxes on a colonoscopy video is one of the most interesting problems I have worked on.
The purpose of this tutorial is to demonstrate the power of algorithmic labelling through a real world example that we had to solve ourselves.* If you want to see the resulting full labelled dataset from this process, sign up here.
In a later post we will go over a more thorough description of what algorithmic labelling is, but, in short, algorithmic labelling is about harvesting all existing information in a problem space and converting it into the solution in the form of a program.
Here is an example of a algorithmic labelling that labels a short video of cars: