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The Biggest Myths about Data Annotation

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.

Even the smartest AI companies are struggling to do this at scale. But why?

Learn more on our blog.

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The Biggest Myths about Data Annotation

  1. 1. The Biggest Myths about Outsourcing Data Annotation
  2. 2. But even the smartest companies struggle to interpret it at scale. High-quality data is the linchpin to amazing AI models.
  3. 3. 3 Training Data for Computer Vision • You need humans to interpret data 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.
  4. 4. 4 In-House Solutions Don’t Scale • Many companies want to keep their data annotation projects in house. • But why? • Because there’s a lot of myths and misconceptions about third-party options…
  5. 5. Myth 1: “My data won’t remain private or secure
  6. 6. 6 Reality: Choose Trusted Partners Who Obsess about Security Protections • Mighty AI 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. • Authorized employees get to use the tooling, interface, and other benefits of the Mighty AI platform.
  7. 7. Myth 2: “It’s too expensive to hire a third-party provider.”
  8. 8. 8 Reality: You’re Paying The Smartest People to Tedious, Unfulfilling Work • Training AI models is tough when you’re relying on internal resources. So bring in the experts. • Mighty AI handles everything at a lower level of effort, higher throughput, and fraction of the total time and cost of in-house operations. • You get UIs, workflows, tooling, project management, targeting, training and qualifying our curated community of Fives for tasks, quality assurance, testing, and validation.
  9. 9. Myth 3: “The annotators aren’t skilled or specialized enough.”
  10. 10. 10 Reality: AIs are Only as Good as the Humans Who Train Them • 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. • Our proprietary machine learning algorithm protects against the risk of subconscious bias in data science.
  11. 11. Myth 4: “My use case is too difficult.”
  12. 12. 12 Reality: The Experts Excel at Complicated Use Cases • Mighty AI works with companies across industries, and our projects range from simple image classifications to full segmentations of complex road scenes. • With one data scientist, annotations take too long, are too complicated and lead to a decline in quality over time—but we send broken-up microtasks to a large set of qualified community members. • We break up all projects into short, game-like tasks for people to do in their spare time. • Our own data science monitors results and quality, so your team doesn’t have to.
  13. 13. - Brian Kim, VP of Product Management at GumGum “We need very highly specialized annotated datasets. The Mighty AI platform makes it easy.”
  14. 14. Learn more at mty.ai