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So you want to become an AI Apprentice?

Updated 3 Feb 2020

The AI Singapore’s AI Apprenticeship Programme (AIAP)™ is one of AI Singapore’s popular programme, with on average 120-160 applicants every time we open for an intake. It is also rumoured to be very hard to get into, according to friendly sources from the ground.

This article will share what it takes to get into the AIAP™ and map out a training roadmap that someone who is keen to join the AIAP™ should pursue. As mentioned in my earlier article Excuse me, are you a Singaporean AI Engineer?, the AIAP™ is a programme where you get to deep-skill and work on a real-world AI project over 9 months. It is not a re-skilling programme where you come in to train and learn Python.

Do note that our expectations of what an Artificial Intelligence (AI) Engineer or Machine Learning (ML) Engineer will do go beyond just ML modeling. The AI Engineer is expected to ingest data, do feature engineering, build, train and test the model, and lastly deploy it at scale with ability of the model to be retrained and refined whenever required.

What we look for in an AI Apprentice?

One of the most valued traits in our AI Apprentices, is that all of them are self-starters. We are looking for individuals who are self-directed learners and keen to learn data science (DS), AI, and ML  from everyone and everywhere, and anyhow. 

They are curious, they search, hunt and dig to learn. They do not ask “tell me how?” or “where can I go to learn?”.

Oh, and if you are one of those that need to pay money to attend a classroom to learn about Python programming, then you are also likely not someone we will be keen on.

The AI Apprenticeship Field Guide

To help you to better prepare for AIAP entrance test, we have developed the AI Apprenticeship Field Guide. It is a 12-18-months intensive roadmap to help you in your journey to prepare for the AI Apprentice entrance test if you are keen to join the programme, otherwise, you can also use this for your own AI/ML learning journey and the AI Singapore’s AI Associate Engineer Certification.

Earlier version of this article had the contents of the Field Guide repeated here, but I have now consolidated the contents into the Field Guide and removed the old contents from this article. So please head over to the AI Apprenticeship Field Guide for the details.


Most of the courses and materials listed in the AI Apprenticeship Field Guide above are FREE.  The recommended books is an investment in your education. 

Of course the above is not the only way to learn.  Some of you prefer learning only from books (like me), that is perfectly fine. Some like to watch online tutorials or University lectures on YouTube or attend MOOC and no books – please go ahead.

Some of you may be surprised, there is a lot you need to know even before you become an AI Apprentice!

As mentioned right at the beginning, the AIAP™ is a deep-skilling programme and provides the apprentice with an opportunity to work on a real-world AI/ML problem statement with all the common issues faced in the real world, from incomplete and dirty datasets to difficult requests from project sponsors, and yet at the same time, constant pressure to deliver at every Sprint and produce a working end to end model and solution within 9-months.

We cannot train you to be an AI Engineer in 9-months if you do not already have the required  foundation. We need you to acquire that foundation on your own, and we help to polish you up in 9-months so that you can land a role as an AI/ML Engineer or Scientist when you graduate from the programme.

Happy learning!

The AIAP™ is the first TechSkills Accelerator Company-Led Training (TeSA-CLT) initiative in AI. This is a collaboration between AI Singapore and IMDA to develop a pipeline of AI professionals for the industry.

Excuse me, are you a Singaporean AI Engineer?

Last Friday (16th August 2019) we had AI Singapore’s (AISG) AI Apprenticeship Programme (AIAP)™ batch #2 graduate after 9 months of training, which consisted of 2-months of intense self-directed learning and ten 3-weeks sprints which they delivered a minimum viable AI model to our 100E* project sponsors. With this batch, AI Singapore Industry Innovation team have solved our Singaporean AI talent crunch issue and have managed to hire a strong core team of Singaporean AI engineers to accelerate our AISG mission.

This article will share the AIAP™ journey and how we built a strong Singaporean core of AI engineering talents.

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Figure 1. AI Apprenticeship Programme batch #2 graduates. Photos includes our special invited guests that evening: Mr Aurelien Geron and Mr Jared Lander.

The AI Talent Crunch

Artificial Intelligence(AI), Machine Learning (ML) and Deep Learning (DL) are the hype these days, and nearly every company and start-up is either planning or has embarked on an AI project, and/or changing their business plans to include AI and ML.

Since we started the AISG programme back in June of 2017, the number one comment we get from companies here is “I cannot find Singaporean AI Engineers!” 

This is a valid comment as we experienced it ourselves when AISG wanted to hire AI Engineers to work on the 100E AI/ML projects.  We received nearly a hundred applications when we advertised for the AI Engineer position back in August 2017, and only a handful of Singaporeans applied, however most did not have the right experience or required skills. This was not a sustainable position for AISG and we felt it would severely impact our 100E deliverables, and as the Singapore’s national AI programme office, we need to have a strong Singaporean core of AI Engineers.

The AI Apprenticeship Programme

With an initial AI engineering team that included both Singaporeans and experienced SPR and foreigners with expertise in AI and ML engineering and decades of grooming young engineers, we developed the AI Apprenticeship Programme, a 9-months hands-on apprenticeship programme where candidates get to spend time to deepen their AI/ML skills in 2-months with a carefully designed curriculum which provided the necessary knowledge to take on an AI/ML engineering project followed by ten 3-weeks sprint to deliver a minimum viable AI model.

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Figure 2: The AI Apprenticeship Programme

During the initial 2-months, we do not teach in a formal classroom settings (those days are over), but instead, our AI mentors guide the apprentices to learn and do the following:

  • Agile methodology
  • Software engineering
  • Containers and dockers and their deployment
  • Big data with Hadoop/Spark/HPC
  • Supervised ML
  • Unsupervised ML
  • Deep Learning

Basically, each week, the AI mentor will say: “This week we are focusing on unsupervised learning, here are the materials and resources, and please show us your project on Friday!”. The apprentices are expected to use what they have learnt to develop an AI model in an agile manner with dockers and deploy them including all the ingestion pipelines and data processing required. 

We are able to do this because the candidates accepted into the apprenticeship programme have proven themselves to be self-directed learners. This is one of the traits we look for when we bring them into the programme, and it allows us to run at a quick pace. There is no spoon feeding here, no classrooms where you attend tutorials or lectures by the AI mentors. Instead, often, it is the AI Apprentices themselves who self-organize and conduct their own workshops to share the latest AI papers and teach each other.

From week 6 onwards, the AI mentors will have a sense of some of the strengths and particular challenges of the apprentices, and it is also the time we start to share and discuss the 100E projects that needs to be executed in the next 7-months. The 100E projects are listed on a board, and the apprentices can indicate the projects they are interested in. We try our best to assign the 100E projects based on their selection, but often we need to allocate the projects based on the apprentices strengths, background and future aspirations. 

The AI apprentice in the course of the 9-months programme would have built a Minimum Viable Model (MVM) in ten 3-weeks sprints, and deployed a Minimum Viable Product (MVP) into production in collaboration with the project sponsor’s engineering team. Our 100E outcome is a deployed AI model, and to date we have successfully deployed all of our 9-months 100E projects AI models. 

One of our best practices in contrast to a typical internship model, is that we require all apprentices to be based in the AISG premise for the whole 9-months including the period they are working on the 100E project with project sponsors. This allows them to easily access our AI mentors and other apprentices within the cohort. This leads to accelerated and shared learning between the apprentices, but more importantly, they build very strong bonds and friendships that will last them beyond the 9-months programme.

Getting into the AIAP™

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Figure 3: The AIAP™ application process

The AIAP™ accepts only SINGAPOREANS who pass both our technical assessments and interviews. It used to be that we require candidates to have a university degree. That have been relaxed to a minimum diploma requirement starting from AIAP™ batch #4 onwards starting in September 2019.  AISG currently takes in 3 batches per year, and we typically get 120 – 160 applicants per batch, and only around 10%-15% gets admitted into the programme.  

In the AIAP™, we are looking for candidates with the right skills, expertise and attitude. They typically come with intermediate to advanced programming skills in Python, and have done self-directed learning from books, and various online AI/ML MOOCs. What they lack is experience working on a real-world AI problem. This is what the AIAP™ provides.

Who are our Apprentices?

In selecting the apprentices, we do not consider which discipline they studied in or what their academic scores were when they were in school. There is no correlation between what they studied in school and how good they will be as an AI/ML engineer or scientist. Some of our best AI engineers studied economics, business administration, industrial/civil engineering and biology. 

From the 4 batches we have admitted,  we typically have the following in terms of academic backgrounds:

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Our apprentices are not students. They are either fresh graduates or working Professionals , Managers, Executives and Technicians (PMETs). The 4 in-takes we have so far have the following typical proportion:

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There is also a consistent ratio of around 12%-15% female apprentices to males per AIAP™ batch.

In less than 15 months since we started the AIAP™, AISG have solved our problem of “lack of Singaporean AI Engineers”. We retained 2 out of 13 apprentices from batch #1, and 9 out of 26 in batch #2, and released 28 AI Engineers into the industry. We have another 18 undergoing training in batch #3, and 18 new apprentices in batch #4 joining us in mid September 2019.  The AIAP™ will continue until 2024, and we hope to train 400-500 AI engineers by then.

We have found a sustainable model to build up our Singaporean core of AI engineers for ourselves and importantly also for the industry. We are not a big team in absolute numbers of AI engineers, but nowhere in Singapore will you find 40-50 Singaporean AI engineers in a single room solving real-world AI problems for companies!

So when you visit AISG, and step into our engineering hub and ask one of our engineers “Excuse me, are you a Singaporean AI Engineer?”, the answer will likely be a YES!


* 100E – To accelerate and help Singapore based companies and startups in this AI revolution, AI Singapore (AISG) developed the 100 Experiment (100E) programme. Under the 100E program, companies (Project Sponsors) comes to us with their AI problem statement, and if suitable, AI Singapore will co-create the solution on a 50-50 funding model. AI Singapore will invest up to $250,000 and companies will invest similarly and all the cash and resources will be directed to an AI engineering team assembled by AI Singapore with the local Universities and research institutions. Note that no cash funding is provided to the company.

Learn and Contribute

It was with great pleasure that AI Singapore invited Aurélien Géron and Jared Lander as guest speakers to grace the graduation ceremony of the second batch of apprentices from the AI Apprenticeship Programme (AIAP)™.

Aurélien is the author of Hands-on Machine Learning with Scikit-Learn and TensorFlow, while Jared is the author of R for Everyone. An interesting occasion indeed to have writers of the two dominant programming languages in data science in the house at the same time!

What was even more interesting, of course, were the nuggets of wisdom shared by these two gentlemen from the paths they have walked. There were two points which stood out and, perhaps unsurprisingly, shared by both the great minds – learn and contribute.

Aurélien spoke about the importance of making strong connections during the apprenticeship as well as reaching out to people one does not know. Constantly ask yourself if you are learning and contributing.

Jared praised the data science community in Singapore and shared how some of his best friends are from the data science community in New York. Join a community, listen, learn, speak and share! A believer in open source software, upon which his whole career is built, he exhorted the audience to contribute to it.

It was indeed a privilege to have had Aurélien and Jared at the graduation ceremony. I am sure all the graduates and other members of the audience were greatly inspired!

Introduction to Machine Learning with Python

With this book, you will learn: 

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestion for improving your machine learning and data science skills


Four public AI for Kids (AI4K™) workshops were recently conducted during the June school holidays. With support from Facebook and the National Library Board (NLB), 120 enthusiastic participants had a good and enriching time learning and creating basic AI solutions. And to promote inter-generational learning, 18 Singaporean PMETs were selected as the pilot batch of AI4K™ workshop facilitators. Read on to find out more…

The Hundred-Page Machine Learning Book

This is an excellent book for those that studied AI 10, 20 or 30 years ago, and need a quick weekend refresher course. In 100-pages, the author takes the reader through the basics of machine learning with sufficient maths of the core algorithms and neural networks.

I would not recommend this book if you have not done a university level maths or statistics course. If you just want to learn how to program machine learning in Python, Aurelien Geron’s Hands-on Machine Learning with Scikit-Learn and Tensorflow is a better start.

However, if you are comfortable with scientific notations, then this book is a treasure, and it covers the basics of what a practicing machine learning engineer needs to know!

This book is also a recommended read for my team.

Hands-On Machine Learning with Scikit-Learn & Tensorflow

An excellent introductory-intermediate book for any software engineer familiar with Python to quickly pick up machine learning. It will take the beginner to coding relatively proficiently with Python+Scikit-Learn and Tensorflow in a few weeks if you spend an hour or two a day.

The book provides a very nice end to end ML example right at the beginning (chapter 2) which helps to set the big picture, and the author then spend a chapter each on specific ML techniques such as Linear Regression, Logistic Regression, SVM, Decision Trees etc.

Appendix B – Machine Learning Project Checklist is particularly useful for those just starting out in Machine Learning and helps you to keep track of key tasks that you should be doing in the project.

Do note that the maths and statistics part is given a lighter treatment for the target audience in this book, so if you want deeper explanations, you have to refer to other texts. A newer second edition is due to be released soon based on Tensorflow 2.0.

This is a popular book within our team members and apprentices and hence I have decided to make this text as one the required reading for AI Singapore’s AI Apprenticeship Programme.

Building up young AI talents for tomorrow

AI for Kids (AI4K), which targets upper primary school children, is one of the initiatives AI Singapore has launched to develop the AI talent pipeline in Singapore.

Through blended learning and a hands-on workshop, AI4K fosters interest in machine learning and artificial intelligence (AI) and teach participants on the use of popular machine learning tools such as Scratch and Microsoft Azure’s Cognitive Services.

Four public workshops were recently conducted during the June school holidays.  With support from Facebook and the National Library Board (NLB), 120 enthusiastic participants had a good and enriching time learning and creating basic AI solutions.

Session 1: June 9, 2019 (Tampines Regional Library)

Session 2: June 15, 2019 (library@harbourfront)

Session 3: June 22, 2019 (Jurong Regional Library)

Session 4: June 23, 2019 (library@harbourfront)

Additional session on July 13, 2019 (Wellington Primary School)

Highlights from the workshops

Quiz to test/revise with the children on what they have learnt online

Children learning how to create image datasets to train a machine learning model
for “Rock, Scissors, Paper” game

Facilitators guiding the children in the “Edge Detection” exercise

8 (2)
9 (2)

Another Quiz conducted at the end of the workshop to recap key learnings

Announcing our top 3 winners

Promote Intergenerational Learning

As a national programme to anchor and develop AI capabilities for Singapore, 
AI Singapore seeks to provide an equal opportunity for all Singaporeans to have access to AI or learn about AI.

To coincide with the AI4K June workshops, we launched a “call for facilitators” where mid-career professionals and retirees were invited to apply and be trained as workshop facilitators. They will need to volunteer their time to assist the AI4K Lead Trainer conduct lessons to the children. They, in turn, will also get to acquire knowledge and understanding of AI.  

The response from the public was overwhelming during the recruitment call and we finally selected 18 individuals as the pilot batch of AI4K workshop facilitators. This group was trained over 4 days on the AI4K curriculum, machine learning environment used and steps on how to conduct the workshops.  


The pioneer batch of AI4K facilitators looking sharp and smart after surviving the 4 days training programme

“I realised the importance and relevance of AI and the need to get the younger generation be prepared for it. I am also reminded that I need to stay abreast with new technologies and to never stop learning.
Learning together with other facilitators of different professions exposed me to different views and ways of doing things.
One of my most memorable encounters during the workshops was seeing a mild autistic child coding and that impressed upon me that we cannot leave anyone behind in this AI journey.” 
Candy FOO
“The response from the kids were encouraging. My most memorable experience was having the opportunity to present in one of the workshops and based on the survey, my topics were voted most popular by the kids.
Overall, I enjoyed myself at these workshops. Everyone put in their best effort to make it a success.”

TAN Boon Yick
“It was an enriching experience for me as a facilitator at the AI4K workshops. I am now more aware about how AI is evolving and I hope that the kids who attended the boot camp are inspired to take the AI development to a new level.”
Pauline WONG
“AI4K has given me the opportunity to give back to the community. Guiding the kids to know more about AI is important as it will spark interest in them to continue it in the near future!”

Muhammad Shafie Roslan
“I am happy that the kids assigned to me seemed to enjoy the learning experience, in particular the interactive & hands-on activities."
“Overall, the sessions were good and it was a positive experience for me. I hope I have played a small part in igniting the kids’ interests and built a small but right AI foundation in them.”
Andrew YEO
“Good learning and rewarding training experience for myself. And some networking too. Good fun seeing how these kids learnt and enjoyed the boot camps!”

Karen NG
“The programme also allowed facilitators to be involved in enhancing the materials, validating teaching approaches and adopting areas for improvement in order to enable an efficient delivery of the AI curriculum to the kids.
It was indeed a very memorable and honourable experience for me to be in the first batch of facilitators trained under AI Singapore. Apart from acquiring new knowledge about AI, we were also given an opportunity to experience what it’s like being a coach. We also get to expand our circle of friends while building up the AI ecosystem. “
“I have benefitted so much from the training and facilitation sessions – more than what I had thought. In just a few weeks, I learnt about classroom management, AI tools and concepts and also had the opportunity to visit the sponsor's office at Facebook.
The pioneer group of facilitators were amazing. Despite coming from diverse backgrounds, everyone had the heart to contribute and share his/her knowledge… “
Coral PECK
“I picked up basic knowledge about machine learning but most importantly, the kids had fun while learning during the boot camp. I hope this programme can continue to be rolled out to all primary schools so that more kids can benefit from it.”

Kenneth THAY
“It was a privilege to be part of the pioneer batch of AI4K trainers. The training for trainers was structured and comprehensive and course mates were great company and the admin and logistics thoughtfully organised. The learning about AI, machine learning and deep learning was grounded in the fundamentals and well-paced. All these prepped us to deliver the AI4K programme for upper primary students, who rewarded us with their enthusiasm. A meaningful and satisfying volunteer project!”
Gladys NG

I am heartened to see the intergenerational learning and engagement between our facilitators and children through the AI4K programme. It is also very encouraging to see our facilitators, many of whom had no prior experience or knowledge about AI being able to learn AI in such a short time-frame, impart their skills to the young ones and partnering with us in this endeavour to build up young AI talents for tomorrow. Indeed we seek to get everyone on board to join us on this AI journey…this is just the beginning. said Laurence Liew, Director of AI Industry Innovation, AI Singapore

SGP 4.0: Singapore in the AI Era

The article below first appeared in COMMENTARY VOLUME 27, 2018 SGP 4.0: AN AGENDA.

Full PDF volume can be found here:

Singapore 4.0 – the AI (Artificial Intelligence) Lap – will bring about exciting new opportunities but also challenges. Opportunities, which if seized at the right time will lead to great rewards and prosperity for Singapore. Challenges, which if not addressed may break our society and country. The new fourth generation or 4G leaders need to be not only politically savvy, but they also need to be business and technology-savvy. More importantly, they need to take intelligent and calculated risks to grasp the opportunities and challenges of “The Second Machine Age”, which is a term coined by experts on the AI era, Erik Brynjolfsson and Andrew McAfee.

State and Society’s Response to Technological Change

The steam engine brought about the Industrial Revolution, and the Big Data age of the Information Revolution over the past two decades introduced us to companies such as Google, Facebook and Alibaba. But it will be AI that revolutionises the compact between government and its people; between industries and its workers. Singapore has seen this kind of transition herself as factories, which used to hire thousands of workers have shifted their operations to lower-cost countries in the last decade. Globalisation and automation streamlined operations and made them easily portable across countries, often independent of the education level of the workers.

However, in the past, this displacement happened mainly to factory or other blue- collar workers. Not so in the Age of AI where the disruption will affect what we still think of as sophisticated and white-collar jobs. For example, AI systems can read X-rays and MRIs (magnetic resonance images) faster and as accurately as a human radiologist; search, compile and produce legal case summaries in seconds compared to lawyers who may take days; sieve through thousands of documents and transactions to detect fraudulent transactions in audits; compute a client’s risk profile to price an insurance premium in near real-time; advise a bank client on what shares or stocks to trade and/or invest in; and handle voice support calls that respond to customers’ simple, routine, FAQ-type questions.

On the other hand, for each of these challenges, there are new opportunities. For example, the radiologist can now focus on complex cases and reduce error rates; lawyers can now focus on analysing cases and being creative in planning how to win them; auditors can focus on complex human-to-human investigations supported by insights from the AI system; insurance companies can lower their risks, create more interesting products and offer more cost-effective policies based on individual risk profile instead of a generic profile; bank advisors can provide a more personal approach and focus on difficult or premium customers; and call operators will be relieved of responding to routine questions and can focus on difficult questions or those that require human empathy.

Most of the jobs mentioned above entail completing multiple tasks unlike those of production-line workers. In other words, AI will remove the routine, mundane and predictable tasks and allow us to focus on higher-order and higher-value tasks. A good example would be your humble SPAM engine, which is AI-driven and has helped save millions of hours from VIAGRA4U and DEAREST ONE.

We cannot stop the advance of AI, nor should we avoid it. We need to learn how to harness and even excel at AI to maximise the advantage Singapore can have in embracing it. At the same time, we have to try to minimise the negative impact of the displacement of jobs and workers. Some studies have shown that more jobs will be created by AI than jobs lost to it. Some of those jobs will require the worker to have AI skills and hence a higher level of education, but most will not.

The blue, white and grey-collar workers who do jobs that are in-between the first two categories, will need to learn how to race with machines and not against them. They will need to learn how to leverage AI tools to increase their own productivity and value; to position themselves as AI- enabled and AI-ready workers, engineers, executives and managers.

Some examples of companies that use data and AI to power their businesses include Netflix and Youtube. Closer to home, AI Singapore has within the last one year engaged with companies that are keen to undertake an AI project, including building up their own AI talent. Some of these companies are Surbana Jurong, Defence Science and Technology Agency (DSTA), Singtel, Daimler South East Asia and Johnson & Johnson. We expect to support them with up to 200 AI engineers via our AI Apprenticeship Programme initially. If Singaporeans are open enough to embrace technology and ride the wave, there will always be new job and career opportunities.

In that vein, the start-up ecosystem we have in Singapore must be one of those engines for the creation of new industries, businesses and jobs. So, it is imperative that the 4G leadership finds ways to strengthen the start-up ecosystem and encourage the creation of startup companies whether in deep tech or otherwise.

What are some other issues that will emerge with the development of AI as we try to ensure positive outcomes for jobs, wages and people?

Universal Basic Income

There have been calls by various groups globally for governments to implement policies like Universal Basic Income (UBI), which guarantees any adult an income regardless of whether they are employed. Another group of policies are on managing robot or AI taxes.

While it is unlikely that Singapore will adopt UBI anytime soon given the strong anti-welfarist orientation of the Government, we already have policies and government programmes which, if you peel away the acronyms like CITREP, TESA and SkillsFuture, represent a limited form of UBI.

For example, today, in AI Singapore which I am part of, we have the AI Apprenticeship Programme mentioned earlier which is a nine-month programme of self-directed learning and hands-on training in skills to build and operate AI systems. The programme is funded by The Info-communications Media Development Authority’s (IMDA) TESA programme and AI Singapore. The apprentices have their tuition and course fees waived, and are paid a stipend of between S$3,500 and S$5,500 per month. The stipend allows them to focus on learning a new skill and not worry about meeting daily expenses.

This approach of funding training costs, which ranges from a 70 to 100 percent subsidy and the provision of a stipend for some specific programmes is Singapore’s version of UBI. The Singapore approach is measured; we do not freely provide this “UBI” – you get “UBI” only if you agree to upgrade or re-skill yourself.

This has proven to be a powerful policy and it has allowed our precious tax dollars to be spent on nudging as many as possible towards acquiring new skills and knowledge.

Software Intellectual Property

In taxpayer-funded research, we need to review research and development (R&D) policies specifically where the intellectual property (IP) generated is software, and question policies where universities and research institutes hold on to taxpayer- funded research while making Singapore companies pay royalties for the licence to use them. It feels like these companies are being taxed twice.

Our IP policies, at least with respect to software R&D, are still based on the legacy model of closed source development where the source code is not released to anyone except under a fee-paying agreement, that is, through royalties or other commercial agreements.

However, the open source model of development and innovation have changed the economics of IP exploitation. In the open source model, the source code is shared freely with anyone under a friendly licence such as BSD, MIT or Apache 2.0. In particular, the Apache 2.0 licence is commercially friendly and has gained wide adoption.

The Apache 2.0 licence allows anyone to freely use, modify, distribute and sell a software licensed under the Apache Licence without worrying about the use of software, including patents. This is because the licence explicitly grants developers the copyright and patent of the derivative software. The rights given are perpetual, worldwide, irrevocable, but also non-exclusive.

Just look at examples such as Apache Spark, developed by UC Berkeley under a friendly open source licence, and subsequently commercialised by some of the students and original developers. Spark is now the de facto standard for big data processing and storage, and has created hundreds of thousands of jobs worldwide.

Or at Google’s Tensorflow, the most popular AI framework today which can be used for free. It has created numerous start-ups and the economic benefits that accrue are enjoyed not just by Google, but also by the whole ecosystem.

The advantage of the open source model is clear in the two examples above – people everywhere like free and good software. If the software is good, it will be adopted as the standard. Companies can then commercialise the adoption, and often the companies that succeed in commercialisation do include the original developers. After all, it is very difficult for an external party to come in and “own” the software if they are not part of the original development team or community. This community safeguard is stronger than any licence.

Back to Singapore – how much of our taxpayer-funded research has been exploited and commercialised to the likes of Spark and Tensorflow? Are our researchers in the best position to exploit the IP that has been created? Would it not be better if taxpayer-funded IP were in the hands of entrepreneurs via friendly open source licences? It will allow them to commercialise the IP in a faster and bolder fashion. It is not that our technology is not world-class. On the contrary, many are at the top across several fields. However, it is our legacy IP policies that have prevented researchers from being able to see the adoption of their IP by a critical mass of users. Instead, these languish in folders in the legal departments of some offices.

Be Agile, Be Bold

Singapore admittedly has lost its technological edge in a few areas like e- payments and digital courts. In contrast, China’s Supreme Courts already recognise blockchain-based evidence as being legally-binding. It is only recently that the Government here has embraced Agile IT development practices and open source technology. This, after the industry has been pushing for the adoption of Agile and open source technology since 2000. While it is understandable to want stable and proven systems, there are many areas, especially in information technology, where some risks need to be taken and where we innovate fast and fail faster.

We need the 4G leadership to allow agencies like GovTech and its partners to experiment, to fail and to learn from failure and iterate again; to allow universities to experiment with innovative ways of training the next generation of engineers, developers, management, thinkers and makers.

We need the 4G leadership to allow government agencies to be bold and experiment; to take risks with Singapore start-ups. The IMDA Accreditation scheme, which validates and accredits our local start-ups and SMEs, provides a green lane for them to access to government projects. This is the right move, but we can be bolder and allow agencies wider leeway to work with our Singapore local start-up companies.

What will the situation look like when that transformation is complete? Would we be able to view “SGP 4.0” as an upgrade, an improvement, or a paradigm shift altogether? Will it be achieving something that is long overdue?

A transformation is complete when the butterfly transforms from egg to larva to pupa and finally to adult butterfly. However, for a country like Singapore, there can never be a complete transformation. Just like we encourage Singaporeans to adopt a mindset of lifelong learning, the 4G leadership must adopt a mindset of lifelong evolution and transformation for Singapore. Our transformation will never be complete since technology never stops evolving. It will mean that we must continue to evolve, transform and adapt too.

About the author

Laurence LIEW is the Director for AI Industry Innovation at AI Singapore and is driving the adoption of artificial intelligence (AI) by the Singapore ecosystem through the 100 Experiments and AI Apprenticeship programmes. A visionary and serial technopreneur, he identified and introduced Singapore’s enterprises to Linux and open source in 1999 as the first RED HAT partner in Asia Pacific; High Performance Computing (HPC) Cluster and Grid computing from 2001 by deploying most of the initial HPC clusters in A*STAR, National University of Singapore (NUS), Nanyang Technological University and Singapore Management University, and architecting and operating Singapore’s first Grid platform – IDA’s National Grid Pilot Platform; and Open Source Analytics in 2011 with Revolution Analytics. Revolution Analytics was acquired by Microsoft in 2015.

He is Chairman, AI Standards Technical Committee, ITSC, Advisor to SGTECH AI+HPC Chapter; and member of the Technical Workgroup for IMDA National ICM Technology Roadmap – Track T4 (AI and Data, and Blockchain). Between 2013 and 2015, he was a Working Group Member of the National Infocomm Media Masterplan 2025.

He graduated from the National University of Singapore with First Class Honours in Engineering, and also holds a Masters in Knowledge Engineering from the same university.

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