One of the most, if not the most, well-known models in Artificial intelligence (AI) is the "YOLO" model series. When I was in school, YOLO used to mean something else. And yet, here I am 15 years later writing an article about it -- who would have thought? YOLO (You Only Look Once) is a popular set of object detection models used for real-time object detection and classification in computer vision. Originally developed by Joseph Redmon, Ali Farhadi, and Santosh Divvala, YOLO aims to achieve high accuracy in object detection with real-time speed.

Data is one of the most important parts of creating innovative computer vision models. That's why the Encord platform has been built from the ground up to make the creation of training data and testing of machine learning models quicker than it's ever been. Encord does this in two ways. Firstly, it makes it easier to collaboratively manage, annotate and evaluate training data. The platform offers powerful automation features to improve labeling efficiency, including interpolation, object tracking and auto-segmentation.

CB Insights named Encord, the platform for data-centric computer vision, on its annual AI 100 ranking, a list showcasing the 100 most promising private artificial intelligence companies in the world. "However, this is just the beginning and we have exciting developments on the horizon – watch this space." "This is the sixth year that CB Insights has recognized the most promising private artificial intelligence companies with the AI 100. This year's cohort spans 13 industries working on everything from recycling plastic waste to improving hearing aids," said Brian Lee, SVP of CB Insights' Intelligence Unit. "Last year's AI 100 companies had a remarkable run, raising more than $6 billion, including 20 mega-rounds worth more than $100 million each. We're excited to watch the companies on this year's list continue to grow and create products and services that meaningfully impact the world around them." "We're honoured to not only be recognized as an innovative AI Startup but also as the only company within the computer vision bracket. The whole team has worked tirelessly from its humble beginnings to get to where we are today and for this effort to be recognized is great kudos to everyone," said Ulrik Stig-Hansen, Co-Founder & CEO at Encord.

Before you can even think about building an algorithm to read an X-ray or interpret a blood smear, the machine has to know what's what in an image. All of the promise of AI in healthcare -- an area that has attracted $11.3 billion in private investment in 2021, can't be realized without carefully labeled data sets that tell machines what exactly they're looking for. Creating those labeled data sets is becoming an industry itself, boasting companies well north of unicorn status. Today, Encord, a small startup just out of Y Combinator, is looking to take a piece of the action. Aiming to generate labeled data sets for computer vision projects, Encord launched its own beta version of an AI-assisted labeling program called CordVision.

After meeting at an entrepreneur matchmaking event, Ulrik Hansen and Eric Landau teamed up to parlay their experience in financial trading systems into a platform for faster data labeling. In 2020, the pair of finance industry veterans founded Encord to adapt micromodels typical in finance to automated data annotation. Micromodels are neural networks that require less time to deploy because they're trained on less data and used for specific tasks. Encord's NVIDIA GPU-driven service promises to automate as much as 99 percent of businesses' manual data labeling with its micromodels. "Instead of building one big model that does everything, we're just combining a lot of smaller models together, and that's very similar to how a lot of these trading systems work," said Landau.