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Name: Wilson Pang

Company: Appen

Job title: Chief Technology Officer

Date started current role: November 2018

Location: San Francisco Bay Area

Wilson Pang operates as the CTO of Appen Limited, the leading provider of high-quality training data for organisations that build effective AI systems at scale. Pang moved from grade school in his village, to middle school in a town, to high school in a small city, to a university in a big city. He joined IBM after graduation where he experienced the best professional training. Pang then moved to the Bay Area to eBay and CTrip where he enjoyed the Silicon Valley culture, even from eBay Shanghai, and got chance to lead the overall data strategy and execution for a China Internet company. These experiences helped Pang understand people with very different views of the world, and this has shaped how he approaches coding, AI and management. Pang published a book with a former Appen colleague, Alyssa Simpson Rochwerger, titled Real World AI: A Practical Guide for Responsible Machine Learning.

What was your first job? I was a developer at IBM, building large systems for banks, telecom operators and securities exchange companies.

Did you always want to work in IT? I fell in love with technology during high school when I was able to write a program on an old Apple machine in 1993. The ability to create “a small world” by writing a few lines of code is fascinating. My passion around technology has not stopped since then.

What was your education? Do you hold any certifications? What are they? I received my bachelor and master’s degrees at Zhejiang University.

Explain your career path. Did you take any detours? If so, discuss. I started my career as a developer with IBM, building large systems for banks, telecom operators and securities exchange companies. I was excited by the power of software - you are building your own world when you write software and you can do almost anything.

I moved to eBay and experienced the great turnaround of the company. eBay was in trouble in mid-2009. The share price was at a historic low, well off its near-$60 historical high. It was cutting costs, growth was negative, market share shrinking, and the technology team wasn’t empowered to innovate. By bringing in new executives, eBay started to make the engineering team an ideas powerhouse and built it into an equal partner, alongside the rest of the business. The company started the journey to use technology, data and AI to drive business.

I was lucky to join and build the search science team, which was the first team to leverage machine learning to rank items for buyers. We had a huge amount of data and could easily A/B test new models to learn how they worked. Every time we optimised the search model, user conversion would increase, which translated to millions or tens of millions in increased revenue.

Our team showed the whole company how powerful machine learning and data can be. Data science opened up an entirely different world than the engineering process I was used to. The new problems I was working on were interesting, but also very different from the engineering challenges I still enjoyed. I hesitated to switch my career to what seemed like an entirely new field. My mentor, a great tech leader who founded Bing’s image and video search team and was leading the big turnaround for eBay, convinced me to go for the new challenge.

For the next two years, I spent all my free time building my machine learning knowledge and picking up statistics. It was an intense period, but I learned the power of machine learning and how it can help change a business. Meanwhile, I continued to lead teams using machine learning and data to improve overall eBay search and marketing experience.

I then joined eBay’s data service and solution team and played a horizontal role to build data solutions for the whole company. We enabled product managers to optimise product experience by data, inventory managers to optimise inventory and price by data, marketing to optimise campaigns through data, CRM teams to engage buyers and sellers through data, and optimise experiment platform to support all the A/B testing. I had the opportunity to enable data driven decisions for every team in the company. I also built a retail science team and data labs to detect trends and seasonality of inventory, help sellers decide price for their products and help buyer to find interesting products.

After 11.5 years with eBay, I joined trip.com group as their chief data officer. My team leveraged data and machine learning to optimise the travel experience. We made significant revenue increases through search, recommendation, and CRM; We also saved costs from using AI in operation and customer service, improved internal efficiency in a big way as well as set up the data foundation for the whole company.

The more I worked in the machine learning and AI field, the more I realised the importance of data. Developers used to be the leaders of software, while training data was now becoming the leader of AI applications and data determines the logic and performance of AI. Appen’s mission is to create large volume of high-quality training data faster and I strongly believe that mission will help the whole world adopt AI faster in a better way. So, I joined Appen to make AI work in the real world!

What type of CTO are you? While I’m responsible for defining the technology vision and strategy for the company, I also focus on attracting and nurturing great talent and building a great culture for execution and innovation. Because of my upbringing, I bring a combination of high confidence and compassion to my leadership style. By making emotional connections with my employees, while always maintaining a “we can get it done” spirit, I have been able to consistently motivate my teams to achieve great results. I also really care about other people’s feelings and how they can grow and thrive professionally. Making an emotional connection has turned out to be an important way I motivate my teams to succeed and deliver results.

Which emerging technology are you most excited about the prospect of? AI clearly has the most potential to change the world, and I’m particularly excited about AI in the manufacturing industry, where we will see AI and IoT come together to transform the industry, saving tons of human labor while boosting productivity, efficiency and profitability. A related technology I’m excited about is NLP – natural language processing. Advances in NLP can help AI not only better understand text and sentiment, rank search results better, chat like a human, but also provide answers to questions, writing articles and even programing with human instructions.

Are there any technologies which you think are overhyped? Why? Generic Artificial Intelligence is overhyped in my opinion. Although AI has made huge progress in computer vision, voice recognition and natural language understanding, which makes it look like AI can behave like humans, most of those AI advancements are based on training data. AI is learning all those capabilities through training data. AI is far away from reasoning and understanding.

What is one unique initiative that you’ve employed over the last 12 months that you’re really proud of? Among those industry innovations our team delivered in the last 12 months, Fair Pay initiative is the one I am most proud of.

Appen delivers high quality training data at large scale to enable our customers to train their AI application. To do so, we also leverage a diverse crowd from all over the world to collect and label data. Appen is committed to treating our crowd members fairly and to care for their wellbeing, therefore we want to pay crowd workers above minimum wage for their location. I am very proud that we are enabling this through machine learning and great product design.

Are you leading a digital transformation? If so, does it emphasise customer experience and revenue growth or operational efficiency? If both, how do you balance the two? Technology is enabling Appen’s business growth. It helps to improve customer experience and revenue growth as well as enhance operational efficiency.

I am a huge believer in ownership and empowerment. You find the best talents, give them big challenges and autonomy, and then try to unblock them whenever they bump into obstacles. You can often be surprised on what people can do.

In terms of balance, we have divided up those initiatives among different leaders and teams and have seen great results from both sides!

What is the biggest issue that you’re helping customers with at the moment? For AI projects to succeed, companies must have access to huge amounts of well-trained, unbiased, up-to-date training data. Without this, their projects will fail to deliver on their goals. Data is always changing in response to a changing world – think of how many ways the pandemic has changed almost everything. AI projects that are not being constantly updated with new data can’t respond correctly to changing environments. Further, if the ML models are trained using limited or biased data, the results will never be applicable to everyone.

When it comes to AI, data will always be the biggest success factor. At Appen, we work with over 1 million contractors to collect and label images, text, speech, audio, video, and other data. Because these contractors live in over 130 countries and speak 180 languages and dialects, we can ensure companies have access to high-quality unbiased data for their AI projects – at scale.

How do you align your technology use to meet business goals? Appen’s executive team defined our business goals and strategies together. We discuss what role technology will play in the overall company strategy, just like what we did for sales and delivery team.

With the clearly defined business goals and company strategy, I work with my tech leadership to break them down and translate them into our tech strategies. Meanwhile, we collect other team’s technology needs, and other strategies proposed by the tech teams to make sure our technology is ahead of the industry curve. We then consolidate and prioritise them into the final technology strategy with clear success definition. There is a final review with other business leaders to make sure it is aligned with business goals.

Do you have any trouble matching product/service strategy with tech strategy? With the approach above, we successfully align our tech strategy with our sales and service strategy.

One area every tech leader should pay special attention to is tech debt. There is a famous saying in the Silicon Valley startup community: “Don’t scale until you have to”. It encourages people to use the quickest way to test the market first without building a solid tech foundation, which is absolutely a right strategy for early stage companies or early stage products in big companies. However, it does introduce a lot of tech debt. Paying off those tech debts is important to support future scalability, even if the business impact might not be transparent to business leaders. Tech leaders need to include that in the tech strategy and let people understand the importance of it.

What makes an effective tech strategy? An effective tech strategy has three major components:

Support the business need and accelerate business growth: There are a lot of business growth areas that require technology enablement and the tech strategy must support them. There are also many areas technology can drive business growth that business leaders might not have considered. The tech team should provide those insights and make it a part of the tech strategy.

Pay attention to tech debt and build a scalable foundation: Those areas might not be able to bring in direct business growth or operational efficiency improvement, but it can be made transparent to business leaders.

Invest in the future: Technology evolves very fast and there will be disruptions. A tech strategy has to include effort to invest in innovations and prepare the company for the future.

What predictions do you have for the role of the CTO in the future? All CTOs need to have deeper understanding of AI in the future. It is clear that AI has been transforming our world industry by industry and it is becoming a critical piece of the company’s overall strategy especially the tech strategy. CTOs need to make sure they see that trend, embrace the trend and drive AI evolvement.

What has been your greatest career achievement? There are two areas I felt very proud of in my career so far.

First, I have been able to work with great people to drive hundreds of millions of revenue growth in both eBay and CTrip through leveraging Machine Learning and Data.

Second, I have been lucky to be part of many people’s career growth who are now leading technology innovations in many industries and companies.

Looking back with 20:20 hindsight, what would you have done differently? I resumed regular sync ups with tech leaders from different industries about two months ago. I should have done that earlier, even from the beginning of the pandemic.