Once the dream of automakers and the public, self-driving cars have taken to the road and are now approaching an even bigger milestone: complete autonomy. And it’s only happened because of AI. Plus the data AI uses to make human-like decisions behind the wheel.
While AI is known for bringing hyperintelligence to things like self-driving vehicles, what’s driving AI is a massive amount of high-quality data. But high-quality data doesn’t exist on its own. And this is where the challenge for AI-driven brands arises.
For AI to be effective requires data that has been processed by humans in order to teach the machine learning (ML) models that tell AI what to do and when to do it. Yet putting this data together can be a big and complex process; one that takes lots of skill, time, and resources to develop into the very datasets AI uses to perform at its best.
Empowering AI performance with data labeling
Self-driving cars aren’t the only thing being powered by AI. From science and medicine, to retail and security, to finance and even agriculture, AI is everywhere. And it’s all made possible by meticulously labeled data, which can include everything from text and audio files, to images, videos, and more.
So what is data labeling and how does it help AI do its job effectively? Let’s dig in and find out.
What is data labeling?
Data labeling is the process of annotating, tagging, classifying, moderating, and managing data to create a framework for ML models to learn from and inform the algorithms that operate AI. But even without AI working behind the scenes, data labeling helps companies deliver the right information or products at the right time to users and customers.
Why is data labeling useful?
Brands in every industry use data labeling to enable the automated processes that would otherwise be impossible in real-time interactions or online business.
In the case of self-driving cars for example, labeled data provides AI with information about its surroundings so it can avoid obstacles, recognize street signs, and maintain safe speeds. It also helps AI process inflections in a person’s tone of voice to help it understand the context of a conversation. While in eCommerce, labeled data helps optimize products to find relevant items for customers.
In short, the list of things a business can do with labeled data is already pretty substantial – and it’s only getting bigger, especially as AI gets involved.
Delivering labeled data at speed and scale
Labeling data can be a huge undertaking for any business. Many brands simply don’t have the people, technology, or experience to scale it efficiently and deliver it quickly. This is why we’ve partnered with our parent company, Pactera EDGE, to provide brands with a scalable, cost-effective, and easy-to-implement data labeling solution.
As eCommerce experts, BFM sometimes run into data labeling issues when working with retail clients. One such instance involved a major client with thousands of stores and tens of thousands of products. To help them boost relevancy in search results and present customers with the right product recommendations, we recruited a team of experienced resources to label data and provide a solid AI foundation. Our case study covers the details and results we were able to achieve.
Instead of brands having to venture into data labeling territory on their own, our data labeling solution brings together the experts and technology to curate, process, and verify large volumes of high-quality data – whether it’s used to power AI or for any other application.
What’s more, Pactera EDGE’s proprietary OneForma tool enables our team to recruit from hundreds of thousands of resources in over 200 markets, ensuring datasets get a local, relevant perspective that helps brands expand products and services into more markets.