This week, Appen is exhibiting at RE•WORK’s Deep Learning in Finance Summit in London. The summit helps business leaders, data scientists, and engineers discover advances in AI & machine learning tools and techniques from the world’s leading innovators across the financial sector. RE•WORK spoke with Appen CTO Wilson Pang to learn about his current work, the challenges of implementing AI, and how high-quality data is crucial for machine learning. (This post first appeared on RE•WORK).
1. Give us a bit of background on Appen and your role there
Appen develops high-quality, human annotated datasets for machine learning and artificial intelligence. We work with leading companies across many different industries to scale their machine learning programs, and our training data helps to improve solutions like chatbots, speech recognition systems, search engines, social media platforms, and more. Most of our clients choose to partner with us because Appen is a one-stop shop for high-quality AI data. We work with our clients to design data collection and annotation programs that are specific to their needs, and our project managers ensure that the data meets our clients’ quality standards. We can scale up quickly because we have a global crowd of over 1 million contractors, working in 130 countries and 180 languages, and we can handle many data types — including sensitive data — depending on our clients’ needs.
My role is Chief Technology Officer, in charge of both product and engineering. My team consists of data scientists, engineers, and product managers. We’re building the world’s leading data labeling platform, which includes an AI-assisted tools system that makes data labeling much faster. The platform also includes a workforce management system which makes it easy to engage and grow our crowd community — as well as providing data insights, data quality assurance, and tools to make our project managers super-efficient.
2. How did you start your work in machine learning?
I started my career in machine learning in search, the first domain where machine learning was widely applied. I was very lucky to be one of the founding leaders of eBay’s Search Science team 10 years ago. We built the Search Science team from scratch and drove huge revenue increases by applying and optimizing machine learning algorithms. Once you see the power of data and machine learning, you can hardly stop. From there, I also a built data science team at eBay that worked on retail science (inventory price setting, supply & gap analysis, trending and seasonality detection, etc.), experimentation, and product experience optimization.
Later on I joined China’s largest online travel agency, CTrip, as Chief Data Officer to lead most of the machine learning and data initiatives in the company. My team drove hundreds of millions of dollars in revenue increases, as well as huge reductions in customer support costs there. In my experience