Customer Use Cases

Pinterest

Pinterest is a visual discovery engine for saving and discovering ideas. 

“At Pinterest, we have a growing dataset of billions of ideas, and we're tasked with showing the right idea to the right user at the right time. Taking advantage of Amazon Mechanical Turk’s powerful crowdsourcing platform, we built a high-quality human evaluation system that could scale with our needs.” 

- Veronica Mapes, Technical Program Manager for Human Computation, Pinterest

Zillow

Zillow is a leading real estate and rental marketplace dedicated to empowering consumers with data, inspiration and knowledge around the place they call home. 

“At Zillow we empower consumers with data, inspiration and knowledge about their home. We use MTurk to better understand home owners and their perceptions of their most important asset. These insights help us make sure we’re building the right features and capabilities into the Zillow apps and website.”

- Brian Colwell, Director, Zillow Group Behavioral Sciences


KITT.AI

wikiHow is a collaborative, wiki-style website with goal of teaching everyone in the world how to do anything. wikiHow publishes how-to guides in 18 languages and reaches over 150 million readers each month. The wikiHow team uses Amazon MTurk to assist with quality control on user-submitted questions.

“The very popular Community Q&A feature on wikiHow allows people to ask questions about any article on our site. We receive a large volume of questions every day, on an incredibly wide range of topics. These questions vary greatly in quality – from insightful and helpful to off-topic or unintelligible. We needed a scalable solution to help provide quality control on these questions so that we could share them with our community and readers to answer, and purely algorithmic processing of questions wasn’t up to the task. MTurk provided us with a pool of qualified Workers who were able to help us evaluate the relevance of questions and edit them for concision and clarity. Because MTurk and the MediaWiki software that power wikiHow have robust APIs, we were able to automate the process and scale the solution quickly, seamlessly passing questions from our servers, to MTurk, and back.”

- Chris Hadley, Vice President of Operations, wikiHow

Allen AI

The Allen Institute for Artificial Intelligence (AI2) is a non-profit research institute with the core mission of contributing to human good through high-impact research and engineering in artificial intelligence.  

“At AI2, we're pushing the state of the art of Artificial Intelligence, which often requires human-annotated data to train new systems and measure our progress. In particular, we use crowdsourcing platforms such as Amazon Mechanical Turk to build datasets that help our models learn common sense knowledge, which is often necessary to answer basic questions that are easy for humans but still quite hard for machines. Amazon Mechanical Turk provides a flexible platform that enables us to harness human knowledge to advance machine learning research.” 

- Michael Schmitz, Director of Engineering, The Allen Institute for Artificial Intelligence


C-SATS

C-SATS, part of the Johnson & Johnson Institute, is a performance management system for healthcare professionals to assess and improve continuously, accurately and objectively.

“C-SATS enables surgeons to upload surgical videos for assessment by expert surgeons and reviewers who provide objective and confidential feedback on technical skills. Powered by Amazon Mechanical Turk, this scalable platform will fundamentally change how surgeons learn by giving them the opportunity to anonymously receive input on actual cases to improve their technical skills, which benefits patients, surgeons and health systems.”

- Svetlana, Director of Operations, C-SATS

Baidu

Baidu Research, a division of Baidu Inc, brings together global talent to work on technologies such as image recognition, video understanding, voice recognition, natural language processing, and semantic intelligence.  

“At Baidu Research, we aim to revolutionize human-machine interfaces with the latest artificial intelligence techniques. Voice cloning is a highly desired feature for personalized speech interfaces. We introduce a neural voice cloning system that learns to synthesize a person’s voice from only a few audio samples. Besides evaluations by discriminative models, we were able to quickly stress-test the audio samples by crowdsourcing perceptions. Using Amazon Mechanical Turk, we were able to tap on a large number of listeners to rate the quality of the audio and compare it to original human recording.” 

- Greg Diamos, Senior Researcher, Baidu Research


KITT-AI

KITT.AI, a subsidiary company of Baidu, provides an open platform that enables developers to create voice-based applications that can be used on multiple devices and applications.

“Snowboy is a highly customizable wake word detection engine that makes it possible for users to pick any wake word they want to call their voice assistant into action. Wake word algorithms are based on neural nets. Usually it takes a well-funded team to recruit the thousands of people needed to provide the voice recordings and additional human training to coach a wake word neural net until it works well. Amazon Mechanical Turk provided us with a low cost, scalable, and global workforce that enables us to generate the diverse training sets required for building such AI models.”

- Xuchen Yao, CEO, KITT.AI

Zignal

Zignal Labs offers a media analytics platform that monitors and analyzes – in real-time – brand conversations across social, broadcast, digital and traditional media channels.  

“Today, brands are capable of producing hundreds of millions of social conversations and stories across the digital media spectrum. For Zignal, natural language processing is critical to rapidly synthesizing this massive amount of media data in real-time. Amazon Mechanical Turk makes it possible to generate human-annotated data for machine learning algorithms quickly and at scale. By harnessing the power of the crowd to obtain high-quality labeled data, we were able to measure and build effective models applicable across the media spectrum.” 

- Jeff Fenchel, Sr Software Engineer, Zignal Labs


Radiant

Maxar’s Radiant Solutions is a leading provider of innovative geospatial solutions that reveal insights where and when it matters.

“At Radiant Solutions, we source trillions of satellite pixels every day, and understanding every object, location, and action on this planet is an enormous challenge. Using Amazon Mechanical Turk's crowdsourcing platform, large communities of users sift through massive volumes of data to tag important objects, features, or locations. These labeled datasets serve as ground truth that helps us train and refine our advanced geospatial algorithms.”

- Kevin McGee, AI/ML Production Lead, Radiant Solutions

Food Genius, a subsidiary of US Foods, is a leading foodservice data provider.  

“The F&B industry has always operated at the mercy of changing tastes and preferences of consumers. Our goal is to surface consumer insights and spot emerging trends, so our clients can effectively respond with effective strategies. Workers on Amazon Mechanical Turk respond to our requests to gather information from menus, websites, and other channels. We are able to leverage these human collective insights to better understand customer needs and uncover important market trends.” 

- David Falck, Executive Director, Food Genius / US Foods Data Science


Collider

Collider is a marketing strategy lab for Yum! Brands that helps brands grow through insights, research and innovation.

“As a strategy and innovation group that's constantly inventing new research tools, we love using MTurk to test out prototypes of our new tools. The flexibility of MTurk allows us to quickly try out new things that wouldn't make sense with traditional research panels, such as single question experiments.”

- Greg Dzurik, VP, Marketing & Innovation Strategy, Collider/Yum! Brands