Using Machine Learning and the power of the crowd to accurately place digital ads in relevant and brand-safe contexts.
What’s been super helpful is to tell my customer success manager what it is I want to achieve, and look to Figure Eight to help me with the job design, creation, and coding.
– Erica Nishimura
Data Curator, GumGum
GumGum is an artificial intelligence (AI) company with a focus on computer vision (CV) and natural language processing (NLP). For the past 10 years, it has applied its patented capabilities to solving hard problems in a variety of industries, from professional sports to healthcare, but the company built its name with solutions for the digital advertising industry. It was for that industry that GumGum developed one of its most exciting proprietary offerings: webpage content analysis technology.
GumGum’s technology reviews webpages, identifying and classifying the content it finds in order to help advertisers place digital ads in relevant and brand-safe contexts. Rather than rely on behavioral targeting, which targets ads at users based on their personal online history, GumGum’s contextual targeting technology serves ads that are aligned with users’ interests without infringing on users’ data privacy. It also ensures that a brand’s ads do not appear adjacent to context that is offensive or harmful to brand reputation.
Erica Nishimura, data curator at GumGum, said,
To provide accurate contextual intelligence for digital ad placements, our technology has to be able to look at images and text on webpages and identify what’s in them. For an image that means we first need to determine if it’s safe.
We’ll look for things like hate symbols, violence, nudity, drugs, etc. If we see those things, we prevent ads from being placed. If we determine it’s safe, we’ll then identify whether it’s a person’s face, a specific celebrity’s face, a dog, or whatever may be relevant to the ad. There’s a more complex but similar process for analyzing text.”
For GumGum’s algorithms to understand what they are seeing and reading, they must be fed large volumes of relevant annotated training data. Initially, GumGum worked with two full-time annotators who could, at best, annotate 15,000 rows of text data or 50,000 images per month.
GumGum’s CV and NLP scientists, who work on the company’s algorithms, needed a better way to perform text classification, image classification, and image annotation in order to efficiently create the high-quality structured data used to train the company’s advanced machine learning models.
GumGum selected Figure Eight for its robust training data platform. Figure Eight offers GumGum data scientists solutions, such as Machine Learning (ML)-Assisted Data Annotation.
With this functionality, GumGum is now able to annotate, depending on the task or language, 10,000 rows of data in just a few days—and sometimes within just a few hours—a fraction of the time it previously required for annotating a similarly sized data set. This efficiency freed up their data scientists to work on research for their NLP and CV technology instead of spending the extra time and effort on in-house data annotation.
The Figure Eight platform also allows GumGum team members with no prior coding experience or engineering background to set up a new annotation job, especially when the annotation job does become more complicated. “What’s been super helpful is to tell my customer success manager what it is I want to achieve, and look to Figure Eight to help me with the job design, creation, and coding.” Nishimura said.
Furthermore, with Figure Eight, GumGum can create foreign language data annotation tasks for NLP-related projects. Figure Eight has annotators who are native or fluent in those languages and can work on the annotation. In the past, Figure Eight has successfully completed annotation tasks in Spanish, French, German and Japanese. Nishimura added that
“GumGum is especially happy with the Japanese annotation quality and support, which Figure Eight has improved tremendously over the past year.”
Not only can GumGum create high-quality datasets more efficiently, but it also has the flexibility to customize annotation jobs for specific use cases and leverage Figure Eight’s expertise for guidance. GumGum has found a one-stop shop for high-quality ML training data creation, ensuring its employees can focus on growing the business and supporting its customers.