Artificial intelligence promises to help, and maybe even replace, humans to carry out everyday tasks and solve problems that humans have been unable to tackle, yet ironically, building that AI faces a major scaling problem. It’s only as good as the models and data used to train it, so there is a need for sourcing and ingesting ever-larger data troves. But annotating and manipulating that training data takes a lot of time and money, slowing down the work or overall effectiveness, and maybe both.
A startup called V7 Labs believes it’s had a breakthrough in how this is approached. It’s effectively built training models to automate the training of those models. Today it’s announcing $33 million in funding to fuel its growth after seeing a lot of demand for its services.
V7’s focus today is on computer vision and helping identify objects. It says it can learn what to do from just 100 human-annotated exa...