INSUBCONTINENT EXCLUSIVE:
LightTag, a newly launched startup from a former NLP researcher at Citi, has built a “text annotation platform” designed to assist data
scientists who need to quickly create training data for their AI systems
It’s a classic picks ‘n’ shovels move, in that the bootstrapped Berlin-based company is hoping to take advantage of the current boom
in AI development.Specifically, LightTag aims to solve one of the main bottlenecks of ‘deep learning’-based AI development: what you
get out is only as good as the labeled data you put in
The problem, however, is that labelling data is laborious, and since it’s a job carried out by teams of humans it is prone to inaccuracy
LightTag’s team-based workflow, clever UI, and in-built quality controls is an attempt to mitigate this.“What I’ve taken from [my
previous positions] to LightTag is an understanding that labeled data is more important to success in machine learning than clever
algorithms,” says founder Tal Perry
“The difference in a successful machine learning project often boiled down to how well the gathering and use of labeled data was executed
There is a huge gap in the tooling to consistently do that well, that’s why I built LightTag”.Perry says LightTag’s annotation
interface is designed to keep labellers “effective and engaged”
It also employs its own “AI” to learn from previous labelling and make annotation suggestions
The platform also automates the work of managing a project, in terms of assigning tasks to labellers 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 recognise problems in their project early on
For example, if a specific annotator is performing worse than others”.Meanwhile, Perry says acquiring labeled data is one of the silent
growth sectors in the recent AI boom, but for many sector-specific industries, such as medical, legal or financial, outsourcing the job is
That’s because the data is often too sensitive, or too specialist for non-subject experts to process
To address this, LightTag offers an on-premise version in addition to SaaS.“Every company has huge text datasets that are unstructured
(CRM records, call transcripts, emails etc)
‘Deep Learning’ has made it algorithmically feasible to tap that data, but to use Deep Learning we need to train the model with labeled
Most companies can’t outsource labelling on text because the data is too complicated (biology, finance), regulated (CRM records) or both
(medical records),” explains the LightTag founder.Operating in various pilots and in private beta since December 2018, and publicly
launched this month, LightTag has already been used by the data science team at a large Silicon Valley tech company that wants its AI to
understand free-form text in profiles, as well as by an energy company to analyse logs from oil rigs to predict problems drilling at certain
The startup has also done a pilot with a medical imaging company labelling reports associated with MRI scans.
