As the artificial intelligence movement accelerates in limitless directions, one thing is certain: High-quality data is the linchpin.
To get data that’s of the highest caliber, you need humans to interpret it with near-perfect accuracy—especially in computer vision environments like autonomous driving. Companies need accurate labeled datasets to train, then continuously validate machine learning algorithms and AIs. No pressure.
Even the smartest AI companies are struggling to do this at scale. The gut reaction is to try to solve it on their own. But why? Because there’s a lot of noise and misconceptions about outsourcing data annotation.
Here are 4 of the biggest myths we hear:
1. My data won’t remain private or secure.
The top reason companies keep their annotation projects in house is the fear of losing control of their data. We get it—there’s some seriously cool (and often secretive) research happening in the world right now. That’s why top AI practitioners look to trusted partners who obsess about security protections.
Our customers can store data in secure locations within their datacenters and give us temporary access that they control. We can also store it in our own secure storage, where it’s encrypted at rest. In either case, your authorized employees get to use the tooling, interface, and other benefits of the Mighty AI platform.
In-house solutions might feel secure, but they don’t scale. They’re a huge time suck for people building products… which brings us to our next point.
2. It’s too expensive to hire a third-party provider.
You already pay the best and brightest data scientists and engineers—why look beyond your office walls? Because training AI models is tough when you’re relying on internal resources. You didn’t hire your team to spend their days tediously collecting and labeling massive amounts of raw data. Especially when you consider that the data volume keeps growing, and they have to keep combing through it.
There’s a better option. Bring in the training data experts.
Once we’re clear on your project expectations, Mighty AI handles everything. We’ve honed a finely tuned platform for annotating, testing, and validating datasets that our customers can use to accelerate and scale training for their AIs. We offer the whole shebang, at a lower level of effort, higher throughput, and fraction of the total time and cost of in-house operations: UIs, workflows, tooling, project management, targeting, training and qualifying our curated community of Fives for tasks, quality assurance, testing, and validation.
3. The annotators aren’t skilled or specialized enough.
We know what you’re thinking: Who is this elusive “community of Fives” training your data? Well, AIs are only as good as the humans who train them. So, unlike other traditional crowdsourcing services that leverage a crowd of unvetted and unmonitored workers, Mighty AI’s Training Data as a Service Platform is driven by data science and a community of known members.
We train and qualify all community members on our tools and annotation tasks. We even target individual tasks at the right people with the right skills and domain expertise.
For example, let’s say you need to label 10,000 images of streets in Germany. We’ll target these tasks only to our users based in Germany because they’re more likely to understand the subtle differences in street signs, indicators, and directions than those in Asia or the U.S.
We also recognize the risk of subconscious bias in data science. Essentially, when humans train computers, their own views may unintentionally creep into the outcomes. Our proprietary machine learning algorithm takes that into account when it sends tasks to hundreds of users. Small teams of data scientists can’t avoid it as easily.
4. My use case is too difficult.
We’ve done complicated! We work with companies across industries, and our projects run the gamut from simple image classifications to full segmentations of complex road scenes.
We break up all projects into short, game-like tasks for people to do in their spare time. If one person were to tackle a complicated task, the annotations would take too long, be too complicated to get all the details right, and could lead to fatigue and a decline in quality over time—but we send the broken-up microtasks to a large set of qualified community members, they complete each task in a few minutes, and then we perform quality control and provide you with their high-quality aggregated results.
Again, our own data science monitors results and quality, so your team doesn’t have to. We guarantee results and service, no matter how intricate the work may be. What are you waiting for?
Connect with us to learn how we can help give your team a break.
image credit: Helloquence (via Unsplash)
