As a PhD student in cancer biology, Simon Chu took a huge leap of faith when he dived into artificial intelligence (AI) without any formal background in computer science or mathematics.
The odds seemed stacked against him. “I knew that it will be difficult to get into an AI role with my background,” he said.
To get a foothold in the field, he signed up for the AI for Industry (AI4I) programme – a self-paced, self-directed learning programme which gave him a year of access to DataCamp, an online learning resource for data science and analytics.
“I studied religiously on DataCamp, completing one to two courses per day. I completed the coursework requirement for AI4I fairly quickly, and I went beyond that to further enhance my knowledge with other courses on Data Camp,” he recalled. As part of the old requirement for AI4I, he had to attend 2 face-to-face workshops. The first workshop was actually a talk on AI for Everyone (AI4E). The session was not too technical and he learnt about the product development cycle from the talk.
And although he did not make it through his first attempt at the AI Apprenticeship Programme (AIAP), the knowledge he accumulated gave him the confidence to apply once again.
A different lens
Today, Simon is well on the way to completing the AIAP, and the experience has been an eye opener for him. He finds that AI presents a different lens for understanding data and ways in which the world works. Instead of the hypothesis-driven approach which was central to biology experiments, his experience in AIAP challenges him to let the data tell the story, instead of finding the data to support a story.
His passion for AI has also grown as he developed a firmer grasp of ways to develop his own AI models. He is currently working on a Singlish language model in the field of Natural Language Processing.
Despite the progress he has made, family and friends still ask him why he chose to go into AI after spending almost a decade in the field of biology. His answer is that biology and AI are not mutually exclusive options, and he firmly believes that his years in biology have not gone to waste.
Bilingual in Biology and AI
As he picks up skills in AI, he understands that AI is a way of dealing with data, and that it needs to be applied within the context of domain knowledge. In this regard, a biology background enables him to “speak both languages” and there will be opportunities for him to return to it and apply his AI skills, he said.
He also emphasises the importance of staying “teachable”. In an industry that is evolving very quickly, where research papers written three to five years ago could already be outdated, passion in AI has to be accompanied by a willingness to keep learning, he said.
Sharing his experiences with others who are planning to come on board to re-apply for AIAP, he said, “When you have a sense of what the technical assessment/interview is like, you know exactly what you are lacking in terms of skills and knowledge. So work on improving those areas.”
Besides working on technical skills such as coding and machine learning concepts, it is also important to understand the product development cycle. “Attend talks and workshop organised by AI Singapore and other parties, they might be helpful,” he advised. “Don’t give up! If a biologist like me can do it (eventually), so can you.”
If you are keen to prepare for the AIAP, click here (Becoming an AI Apprentice – Field Guide)