By SGN | 18 July 2019
Zhihao was 12 when he lost his favourite uncle to cancer. To this day, his family still believes that his uncle would have lived had his cancer been diagnosed earlier. It was the death of his uncle that drove Zhihao to search for better ways for humans to make decisions.
It was this desire to improve medical diagnosis that led Zhihao to become a huge advocate of machine learning. Before embarking on his MSx program or becoming a Sloan Fellow at Stanford, he was head of Asia for Teralytics, a AI company that used cutting-edge data science and proprietary machine-learning algorithms.
Machine learning is the field of study where computer systems can “learn” using patterns based on existing data in order to perform tasks without needing to be explicitly programmed. It is part of the wider field of artificial intelligence (AI).
Examples of machine learning are varied. There are small, subtle tasks such as determining what emails are considered spam and what aren’t, to curating and displaying specific shows on Netflix that you’ll probably want to watch. More complex types of machine learning include self-driving vehicles and automated medical diagnosis.
How AI and machine learning can help build an inclusive society
Zhihao is excited to see how quickly machine learning is being adopted, especially by the technology industry.
“Most, if not all technology companies around the world are using data, machine learning and AI at some level as part of their arsenal to build a competitive advantage.”
However, while many companies are open to developing AI technology, Zhihao feels that there is also a significant gap in awareness – companies don’t know how to start incorporating machine learning into their business.
“Companies focus a lot on the tech and people, but don’t think enough about the data,” he believes. “A sound data strategy is something companies must think about right from the start of their AI transformation. With data, you can attract the people to build the technology which will lead to better products or more users. With a comprehensive data strategy, you are thinking about how to acquire more data, whether by organic growth or external acquisition, that will enable more and more machine learning use cases.
Many companies also rush into implementing AI and machine learning, without considering other options. Zhihao recommends that companies should build their machine learning capability only when it gives them the right competitive advantage. If there’s no immediate need to build an internal AI infrastructure, companies should instead consider relying on the experience of external vendors to provide suitable, adequate solutions. This would save companies time and money.
But perhaps the biggest concern about machine learning and AI is the inevitable job displacements due to automation. Zhihao empathises but also sees technology as an important enabler to be embraced, not something to be feared.
“Machine learning and AI is important to Singapore because of our aging population,” says Zhihao. “I think they allow us to increase the productivity of our workforce.”
He believes that, if we are properly prepared for it, we can use these technologies to build a more inclusive society. “By providing the right opportunities to all regardless of their financial background, we can leverage on machine learning and AI to liberate us from mundane, mind-numbing jobs and allow us to focus on careers that use our creativity, warmth and humanity. Machine learning and AI can uplift society as a whole and make it more inclusive. Access to these technologies should not just be for those who have the means.”
Connecting with loved ones back in Singapore
Zhihao’s idealism stems from his large extended family back in Singapore. While he doesn’t miss Singaporean food as much thanks to the presence of Shiok! Singapore Kitchen in the Bay Area, he does miss the loud, chaotic but warm family gatherings. Several years ago, while based in Switzerland, twenty of his extended family members flew from Singapore to visit him!
Building an AI and machine learning business in Asia
Zhihao also believes Singapore is also a great place to do business easily, especially when you’re a tech startup. He notes that, in Singapore, the basics of starting a local entity (as he did with Teralytics) could be settled in a few hours, and that the small size of the island meant it was convenient to commute and organise several external meetings with partners in a single day.
He also foresees excellent opportunities for the country to develop as a tech hub for the region, especially for the financial services and healthcare industries, as the region’s middle class becomes more affluent.
Yet Zhihao is also cautious of misrepresenting the Southeast Asian region as a homogenous market. He recognises that while many companies can build a single product and simply localise it to each major economy in the region, some companies will need to build and market a different product for each country.
Having completed the year-long program at Stanford, Zhihao will return home later this year to join a tech company and build their machine learning and AI business in Southeast Asia. He is looking forward to building businesses powered by AI that is inclusive, impactful and meaningful.
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