Behind Every AI Headline Is an Annotation Process, and Shaip Can Help Make It Painless
We’ve often called data the fuel for AI projects, but not just any data will do. If you need rocket fuel to help your project achieve liftoff, you can’t put raw oil in the tank. Instead, data (like fuel) needs to be carefully refined to ensure that only the highest-quality information powers your project. That refinement process is called data annotation, and there exist quite a few persistent misconceptions about it.
The companies taking on AI projects are fully bought into the power of automation, which is why many continue to think that auto annotation-driven by AI will be faster and more accurate than annotating manually. For now, the reality is that it takes humans to identify and classify data because accuracy is so important. The additional errors created through automatic labeling will require additional iterations to improve the algorithm’s accuracy, negating any time savings.
Another misconception — and one that’s likely contributing to the adoption of auto annotation — is that small errors don’t have much of an effect on outcomes. Even the smallest errors can produce significant inaccuracies because of a phenomenon called AI drift, where inconsistencies in input data lead an algorithm in a direction that programmers never intended.
AI drift underscores the importance of accurate annotation, which is why crowdsourcing in an effort to scale annotation needs is destined for failure. Crowdsourcing annotation simply replaces a lack of resources with a lack of accuracy, which will cause more problems in the long run. Fortunately, it’s far from the only option.
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Avoiding AI Project Pitfalls
Many organizations are plagued by a lack of in-house annotation resources. Data scientists and engineers are in high demand, and hiring enough of these professionals to take on an AI project means writing a check that is out of reach for most companies. Instead of choosing a budget option (such as crowdsourcing annotation) that will eventually come back to haunt you, consider outsourcing your annotation needs to an experienced external partner. Outsourcing ensures a high degree of accuracy while reducing the bottlenecks of hiring, training, and management that arise when you try to assemble an in-house team.
When you outsource your annotation needs with Shaip specifically, you tap into a powerful force that can accelerate your AI initiative without the shortcuts that will compromise all-important outcomes. We offer a fully managed workforce, which means you can get far greater accuracy than you would achieve through crowdsourcing annotation efforts. The upfront investment might be higher, but it will pay off during the development process when fewer iterations are necessary to achieve the desired result.
Our data services also cover the entire process, including sourcing, which is a capability that most other labeling providers can’t offer. With our experience, you can quickly and easily acquire large volumes of high-quality, geographically diverse data that’s been de-identified and is compliant with all relevant regulations. When you house this data in our cloud-based platform, you also get access to proven tools and workflows that boost the overall efficiency of your project and help you progress faster than you thought possible.
And finally, our in-house industry experts understand your unique needs. Whether you’re building a chatbot or working to apply facial-recognition technology to improve healthcare, we’ve been there and can help develop guidelines that will ensure the annotation process accomplishes the goals outlined for your project.
At Shaip, we aren’t just excited about the new era of AI. We’re helping it along in incredible ways, and our experience has helped us get countless successful projects off the ground. To see what we can do for your own implementation, reach out to us to request a demo today.