LightTag Helps AI Developers Label Training Data

May 16, 2018

The creators of LightTag are betting on the AI boom, we learn from TechCrunch’s post, “LightTag Is a Text Annotation Platform for Data Scientists Creating AI Training Data.” Built by a former Natural Language researcher for Citigroup, the shiny new startup hopes to assist AI developers with one of their most labor-intensive and error-prone tasks—labeling the data used to train AI systems. Since it is a job carried out by teams of imperfect humans, errors often abound. LightTag’s team-based workflow, user interface, and quality controls are designed to mitigate these imperfections. Writer Steve O’Hear cites founder Tal Perry as he reports:

“Perry says LightTag’s annotation interface is designed to keep labelers ‘effective and engaged’. It also employs its own ‘AI’ to learn from previous labeling and make annotation suggestions. The platform also automates the work of managing a project, in terms of assigning tasks to labelers and making sure there is enough overlap and duplication to keep accuracy and consistency high. ‘We’ve made it dead-simple to annotate with a team (sounds obvious, but nothing else makes it easy),’ he says. ‘To make sure the data is good, LightTag automatically assigns work to team members so that there is overlap between them. This allows project managers to measure agreement and recognize problems in their project early on. For example, if a specific annotator is performing worse than others’.”

For the organizations in certain industries like healthcare, law, and banking that simply cannot risk outsourcing the task, LightTag offers an on-premise version. The write-up includes a couple GIFs of the software at work, so check it out if curious. Though it only recently launched publicly, the beta software has been tried out by select clients, including these noteworthy uses: An energy company is using it to predict drilling issues at certain depths with data from oil-rig logs, and a medical imaging company has used it to label MRI-scan reports. We are curious to see whether the young startup will be able to capitalize on the current AI boom, as Perry predicts.

Cynthia Murrell, May 16, 2018

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