AsianScientist (Nov. 28, 2017) – Fifteen years ago, Anne-Marie Droste might have seemed out of place in Singapore. Her mission—to take promising scientific discoveries and turn them into start-ups—would probably have been met with skepticism, much less embraced.
Back in the early 2000s, one sector in particular was abuzz in Singapore: the life sciences. The nation’s vision was to become a leader in drug development, a pharmaceuticals and biotechnology hub with a thriving ecosystem of research.
“At one point, there was life sciences and pharma,” says Droste, who moved to the city-state from London in 2016 to co-lead Entrepreneur First (EF) Singapore, a pre-seed investment program. “But now I feel the government’s focus has shifted from attracting corporate research labs towards building companies and start-ups.”
Droste’s observation recognizes what appears to be a strategic realignment in priorities that has quietly taken place here over the years: from an emphasis on big conglomerates and life sciences to home-grown companies and deep technology (Droste’s speciality).
In particular, artificial intelligence (AI), machine learning and big data have generated much interest and excitement, not to mention plentiful investments. Just in 2017 alone, the government launched a new program called AI Singapore with US$110 million at its disposal to boost the nation’s AI capabilities; committed US$73 million in a venture capital fund via SPRING Singapore to fund deep tech start-ups; and formed a Data Science Consortium to drive research in the field.
Most recently, Chinese e-commerce giant Alibaba announced plans to establish its first international research hub in the city-state. The hub, slated to open in the central business district in 2018, is part of a US$15 billion initiative to drive Alibaba’s future growth.
These developments seem to suggest that datasets and robots have taken the front seat in Singapore, but do they signal the demise of drug discovery and basic research?
Technology and the economy of the future
“Clearly there’s a growing emphasis and excitement over AI and big data,” says Howard Califano, director of the Singapore-MIT Alliance for Research and Technology (SMART) Innovation Centre. “But these are actually platform technologies that are really enabling… they cut across a lot of application areas. And clearly one of those areas is life sciences.”
Seeding new technologies into existing practices has birthed many changes, especially in the field of healthcare. Singapore’s healthcare technology agency Integrated Health Information Systems (IHiS) has developed robots that can assist in physiotherapy exercises, as well as a telemedicine system where patients need only a video camera and an internet connection to ‘see’ a doctor. In the country’s public hospitals, robots deliver drugs, medical specimens and documents. Wounds can be diagnosed from a picture snapped on a smartphone and epileptic fits predicted using EEG data—technologies enabled by machine learning.
AI has the potential to give Singapore’s economy a massive boost. According to analysis by research firms Accenture Research and Frontiers Economics, adopting AI could almost double Singapore’s annual economic growth rates from 3.2 percent to 5.4 percent by 2035. With AI, the country’s economy would double in size in 13 years, as compared to 22 years without the technology, according to the firms’ report published in July 2017.
“The economy of the future lies in our ability to embrace technology,” says Chan Cheow Hoe, deputy chief executive at the Government Technology Agency of Singapore (GovTech) and Singapore’s chief information officer. “But being a Smart Nation isn’t just about technology, it’s about how we use technology to make people’s lives better.”
The pie gets bigger
But the burgeoning AI and big data sector isn’t something the more mature life sciences sector should worry about, says Califano. Providing a fillip to the latter is British pharma giant GlaxoSmithKline’s recent launch of its Asian headquarters in Singapore, which Economic Development Board chairman Mr. Beh Swan Gin said is “testament to Singapore’s attractiveness as a biomedical hub in this region.”
Califano has a front-row seat to Singapore’s R&D scene: the Innovation Centre he heads runs grant programs to help researchers commercialize their technologies. Of the nearly 200 applications the Centre receives every year, 60 percent are medical- and healthcare-related—a figure that has remained consistent over time.
“I see about the same number of [these applications] as I did five years ago, so I don’t think it’s money moving away from one area into another,” Califano says. “The pie is definitely getting bigger.”
So if interest and funding are not zero-sum games, perhaps describing the trend towards deep tech as a shift in research priorities isn’t quite apt. An expansion in focus—one that’s a necessary adaptation to the changing climate—might be a more accurate description.
“I think you have to go into these new areas to stay competitive,” says Califano, who was formerly CEO of Johns Hopkins Singapore and The Johns Hopkins-NUH International Medical Centre in Singapore.
“There are a lot of interesting studies using genomic data, big data and large patient population groups going on right now, and it’s very important to dig down. If you don’t know these areas, you aren’t going to be competitive in the life sciences.”
A quicker, smarter investment?
SMART, Califano’s institute, is one of the major players helping to foster a burgeoning prototype-to-product environment in Singapore. Also providing funding for innovation are government-run agencies such as SPRING Singapore and the National Health Innovation Centre Singapore (NHIC); on the deep tech side, national agency SGInnovate is leading the charge to help get start-ups off the ground.
Institutional investors aside, funding also comes from a variety of private corporations, angel investors and venture capitalists. Notable among them are Rakuten Ventures (with an interest in AI), Jungle Ventures (healthcare) and the $50 million fund DreamLabs (cleantech, healthcare, energy and fintech).
Another factor driving the push towards deep tech, big data and the like could be profitability and the bottom line.
“The returns are faster for an AI-type company, there’s a shorter cycle,” says EF’s Droste. “If you have to do clinical trials, that can take up to ten years.”
From papers to patents
The diverse pool of talents in Singapore is clearly reflected in the inaugural batch of start-ups Entrepreneur First helped shape earlier this year. Instead of the majority of the new companies being AI- or machine learning-focused, which tends to be the case in London where the company originated, Singapore’s budding deep tech entrepreneurs have interests ranging from nanosatellites to robot navigation and kinetic sensors, says Droste.
“The problem with Singapore is not that the people aren’t smart enough, but that the right people were stuck in academia and weren’t starting companies,” she adds.
GovTech’s Chan agrees. “The proof of success is how to take a technology and commercialize it. There’s no point in it being a proof of concept forever.”
But a deep dive into the AI, big data and machine learning frontier doesn’t come without hurdles. As technologies evolve, other supporting areas have to develop too. For Chan, who is spearheading Singapore’s push towards becoming a technology-enabled Smart Nation, building supporting infrastructure is the biggest challenge.
“To transform digitally requires a lot of hard infrastructure, like sensors, cameras and environmental structures,” he says, adding that without better hardware, there are limitations to how much software can improve.
Another problem is how to handle the vast amounts of data being collected. “As data gets more and more pervasive, safeguarding that data—dealing with privacy concerns and information security—poses a big challenge,” Chan says. To that end, the Cyber Security Agency of Singapore is in the process of implementing a cybersecurity bill, which would be a first for the nation.
Still, tackling such obstacles is all part of the journey towards embracing new technologies and innovations.
“Technology can change your life if implemented properly,” says Chan. “We believe this is the way forward, it’s only going to get better. We’re very optimistic.”
So perhaps researchers shouldn’t be too alarmed about the growing focus on AI, machine learning and other emerging technologies.
Says Droste, “Even if there is a shift away from life sciences to deep tech, it’s an evolution of something rather than a change. And I think the time is extremely ripe for it.”
Copyright: Asian Scientist Magazine; Photo: Shutterstock.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.
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