INSUBCONTINENT EXCLUSIVE:
Every artificial intelligence startup or corporate RD lab has to reinvent the wheel when it comes to how humans annotate training data to
teach algorithms what to look for
Whether it doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling
Often that means wasting six months and as much as a million dollars just developing a training data system
With nearly every type of business racing to adopt AI, that spend in cash and time adds up.Labelbox builds artificial intelligence training
data labeling software so nobody else has to
What Salesforce is to a sales team, Labelbox is to an AI engineering team
The software-as-a-service acts as the interface for human experts or crowdsourced labor to instruct computers how to spot relevant signals
in data by themselves and continuously improve their algorithms& accuracy.Today, Labelbox is emerging from six months in stealth with a $3.9
million seed round led by Kleiner Perkins and joined by First Round and Google Gradient Ventures.&There haven&t been seamless tools to allow
AI teams to transfer institutional knowledge from their brains to software,&says co-founder Manu Sharma
&Now we have over 5,000 customers, and many big companies have replaced their own internal tools with Labelbox.&Kleiner Ilya Fushman
explains that &If you have these tools, you can ramp up to the AI curve much faster, allowing companies to realize the dream of
AI.&Inventing the best wheelSharma knew how annoying it was to try to forge training data systems from scratch because he&d seen it done
before at Planet Labs, a satellite imaging startup
&One of the things that I observed wasthatPlanet Labs has a superb AI team, but that team had been for over six months building labeling and
training tools.Is this really how teams around the worldare approaching building AI,& he wondered.Before that, he&d worked at DroneDeploy
alongside Labelbox co-founder and CTO Daniel Rasmuson, who was leading the aerial data startup developer platform
&Many drone analytics companies that were also building AI were going through the same pain point,& Sharma tells me
In September, the two began to explore the idea and found that 20 other companies big and small were also burning talent and capital on the
&We thought we could make that much smarter so AI teams can focus on algorithms,& Sharma decided.Labelbox team, with co-founders
YsiadFerreiras (third from left), Manu Sharma (fourth from left), Brian Rieger (sixth from left) Daniel Rasmuson (seventh from left)Labelbox
launched its early alpha in January and saw swift pickup from the AI community that immediately asked for additional features
With time, the tool expanded with more and more ways to manually annotate data, from gradation levels like how sick a cow is for judging its
milk production to matching systems like whether a dress fits a fashion brand aesthetic
Rigorous data science is applied to weed out discrepancies between reviewers& decisions and identify edge cases that don&t fit the
models.&There are all these research studies about how to make training data& that Labelbox analyzes and applies, says co-founder and COO
Ysiad Ferreiras, who&d led all of sales and revenue at fast-rising grassroots campaign texting startup Hustle
&We can let people tweak different settings so they can run their own machine learning program the way they want to, instead of being
limited by what they can build really quickly.& When Norway mandated all citizens get colon cancer screenings, it had to build AI for
Instead of spending half a year creating the training tool, they just signed up all the doctors on Labelbox.Any organization can try
Labelbox for free, and Ferreiras claims hundreds have
Once they hit a usage threshold, the startup works with them on appropriate SaaS pricing related to the revenue the client AI will generate
One called Lytx makes DriveCam, a system installed on half a million trucks with cameras that use AI to detect unsafe driver behavior so
they can be coached to improve
Conde Nast is using Labelbox to match runway fashion to related items in their archive of content.Eliminating redundancy, and jobsThe big
challenge is convincing companies that they&re better off leaving the training software to the experts instead of building it in-house where
they&re intimately, though perhaps inefficiently, involved in every step of development
Some turn to crowdsourcing agencies like CrowdFlower, which has their own training data interface, but they only work with generalist labor,
not the experts required for many fields
Labelbox wants to cooperate rather than compete here, serving as the management software that treats outsourcers as just another data
input.
Long-term, the risk for Labelbox is that it arrived too early for the AI revolution
Most potential corporate customers are still in the RD phase around AI, not at scaled deployment into real-world products
The big business isn&t selling the labeling software
Labelbox wants to continuously manage the fine-tuning data to help optimize an algorithm through its entire life cycle
That requires AI being part of the actual engineering process
Right now it often stuck as an experiment in the lab
&We&re not concerned about our ability to build the tool to do that
Our concern is ‘will the industry get there fast enough'&Ferreiras declares.Their investor agrees
Last year big joke in venture capital was that suddenly you couldn&t hear a startup pitch without &AI& being referenced
&There was a big wave where everything was AI
I think at this point it almost a bit implied,& says Fushman
But it corporations that already have plenty of data, and plenty of human jobs to obfuscate, that are Labelbox opportunity
&The bigger question is ‘when does that [AI] reality reach consumers, not just from the Googles and Amazons of the world, but the
mainstream corporations'&Labelbox is willing to wait it out, or better yet, accelerate that arrival — even if it means eliminating jobs
That because the team believes the benefits to humanity will outweigh the transition troubles.&For a colonoscopy or mammogram, you only
haveacertain number of people in the world who can do that
That limits how many of those can be performed
In the future, that could only be limited by the computational power provided so it could be exponentially cheaper& says co-founderBrian
Rieger.With Labelbox, tens of thousands of radiology exams can be quickly ingested to produce cancer-spotting algorithms that he says
studies show can become more accurate than humans
Employment might get tougher to find, but hopefully life will get easier and cheaper too
Meanwhile, improving underwater pipeline inspections could protect the environment from its biggest threat: us.&AI can solve such important
problems in our society,& Sharma concludes
&We want to accelerate that by helping companies tell AI what to learn.&
