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Managing AI Projects in AISG

In AI Singapore, we execute 100 Experiments (100E) projects which employ AI to solve problem statements and the pain points of our customers (also known as project sponsors) in various industries. We also execute in-house Bricks projects which focus on product development around AI.

I have been managing AI Projects in AI Singapore for more than a year. In a previous post, we shared about Agile AI engineering practices. We have also curated a project delivery framework that has a lightweight process and adopts the best of all Agile methodologies for managing our projects. In this article, I am going to share this delivery framework.

The diagram below describes the AI Singapore’s project delivery framework.

Image Source:

The framework/methodology follows the phases below to deliver a minimum viable product for the project.

  • Initiation
  • Planning
  • Execution
  • Closure

We will go through each phase in more detail.


In this phase, we work with the project sponsor to understand the problem statement and to assess the incremental value expected from an AI solution and the technical feasibility to deliver the solution within the stipulated budget and timeframe. An overview of the methodology and potential solutions for the problem will be shared with the project sponsor. The project proposal will be reviewed by the key stakeholders within AI Singapore and we move to the next phase after approval.


The project team within AI Singapore is formed in this phase, comprising of the project manager, principal investigator, mentor and apprentices (Please refer to the terms of reference at the end of the article). At the same time, the high-level timelines of the project are prepared. The project has a fixed timeframe of 7 months and is managed with 10 sprints of 3 weeks (or 14 sprints of 2 weeks if the project sponsor requests for shorter sprints) to deliver the minimum viable product at the end of the project.

The project kick-off meeting is scheduled with the project sponsor in this phase to introduce the project team, discuss the project timelines and finalize the scope of the project. Following the kick-off meeting, a user story workshop is conducted where the members from the project sponsor team and the project team from AI Singapore jointly prepare the product backlog which is a list of prioritized features planned for the project.

Software tools: Google Sheets is used to capture the product backlog.


With the overall project plan and the user stories produced from the planning phase, the project is executed in this phase, sprint by sprint, to produce the minimum viable product. Typical activities performed by the project team in each sprint include, but are not limited to, the following:

  • Literature Review
  • Exploratory Data Analysis
  • Data Pipelining
  • Feature Engineering
  • Model Development, training and evaluation
  • Model serving APIs
  • Model deployment in the test environment of the project sponsor

Each sprint consists of a series of meetings where the project progress is monitored and controlled.


Sprint Planning
Sprint planning is conducted at the start of every sprint. The project team defines the sprint goals and prepares the sprint backlog containing a list of user stories prioritized by the project sponsor to work on in the sprint. The user stories are decomposed into manageable tasks and the developers estimate the effort for each task and add it to the issue board (more details in the section “Monitoring and Control”).

Daily Stand-up
The project team meets for the daily stand-up, timeboxed to 15 minutes, where the developers provide status update on the work done, inform next task to do and highlight any blocker issues which need to be addressed by the mentor and the project manager. The issue board is updated to reflect the latest status of the tasks

Sprint Review
Sprint review is conducted with the project sponsor at the end of every sprint. The project team provides progress updates and presents the accomplishments in the sprint. The team also discusses and agrees with the project sponsor on the user stories to plan for the next sprint. Any feedback or changes from the project sponsor on the user stories are incorporated and updated in the product backlog.

Sprint Retrospective
Sprint retrospective is an internal event within AI Singapore where the project team brainstorms ways for improvement. It is conducted after every sprint or every alternate sprint. The outcome is documented along with agreed actionable improvements for future sprints.

Monitoring and Control

Project progress is monitored regularly to identify any deviation from the plan. GitLab Issue Board, illustrated in the screenshot below, is used to track the progress of all the tasks in each sprint. Each task goes through the lifecycle of To Do, In Progress, Review and Closed. The board will be updated by the project team daily before the stand-up.

Project issues are tracked closely throughout the project. Issues are logged in the project issue register along with their description, owner, due date and status for proper tracking till their closure.

Project risks are identified upfront in the planning phase and they are logged in the project risk register. Each risk entry contains information about its description, probability, severity, mitigation plan, owner and status, and it is reviewed every sprint to pre-empt it turning into an issue.

Software tools: Google Sheets is used to capture the project issue register and the project risk register.


In this phase, the project team prepares detailed code and project documentation and arranges for code walk-through sessions to facilitate the handing over of the minimum viable product to the project sponsor. With this, the project is closed and the project sponsor signs off the project.


In addition to the above processes of the project delivery framework, I feel that timely and effective communication within the project team and with the project sponsor team during every phase of the project is crucial to the project’s success. In this aspect, the project team uses the Google Chat for internal communication and the Basecamp Campfire for external communication.

I would be happy to know more about your experience in managing AI projects in the Agile way. Please feel free to share your knowledge and the processes you adopt for your projects, so that we can learn from one another.

Terms of Reference

Principal InvestigatorProvides consultancy on project directions
MentorTechnical lead who provides guidance to the apprentices
ApprenticesDevelopers in the project team
Project SponsorCustomers of 100E project

Achieving Super-Resolution

One application of this research breakthrough is to overcome physical camera lens limitations in mobile phones

Can you take high-resolution, high-quality images on your mobile phone without being limited by the physical lens? Are you able to remaster classic films and video games for today’s 4K world?

One answer to these questions lies in the ability to upscale a low-resolution image to super-resolution by restoring the missing high frequency components. And this is an area that Nanyang Associate Professor Loy Chen Change, from Nanyang Technological University’s School of Computer Science and Engineering, has been focusing on within the field of computer vision.

Unlike conventional interpolation techniques, image super-resolution aims to provide sharper edges and textures for a more pleasing and vivid viewing experience. But it is a very tough nut to crack. As Prof Loy explained, “This is mathematically difficult because there are far too many high-resolution possibilities for a low-solution pixel.”

Prof Loy’s team has been investigating novel deep learning-based algorithms to solve this problem and invented the first deep convolutional network for single image super-resolution in 2014 (Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014).

This seminal work has inspired a new wave of technologies that make use of AI upscaling in mobile phone photography. Such technologies, which can be seen in various models of Xiaomi and Vivo mobile phones, typically exploit redundant spatio-temporal information from several photos taken consecutively to generate a single sharp and vivid image. This enables users to capture high-resolution and high-quality images without being limited by the physical lens in their mobile phones.

Image super-resolution can also be applied to re-digitise and restore film stock as well as to remaster classic video games. “The widespread presence of 4K and more recently 8K televisions is driving demand for high-quality versions of existing videos, as existing videos are limited by the resolution they were originally acquired in,” said Prof Loy. “Image super-resolution technology is able to enhance the quality much better, compared with conventional methods such as bilinear or bicubic interpolation that often give rise to blurry edges and textures.”

The technology also helps users to save considerable costs in bandwidth when they send images over the network. This is achieved by transmitting low-resolution images and upscaling them at the receiving end-user device.

These developments, however, are “only the tip of the iceberg”. “Given enough time and resources, computer vision could be integrated into most things that the human eye can perceive,” said Prof Loy.

He pointed out that by observing objects and how they move, a human being is capable of grasping the structure and some information on the object, and then generalising the knowledge to unseen samples.

In contrast, modern deep learning systems rely heavily on massive amounts of annotated data for learning effective representations. This means hundreds of thousands of hours spent on manual labelling for each percentage gain in accuracy.

“Can deep models learn meaningful visual representation without labelled data?”

His team is trying to answer this by developing new learning approaches for deep learning so that it can learn from a massive number of images and videos in an unsupervised manner, namely without explicit annotations.

As he continues on his research journey, Prof Loy feels that he has been most fortunate to be able to do research in a field that he is truly passionate in and to work with the right people. “I have been very lucky to have met the right people at the right time and at the right place (my collaborators, my postdocs, and my students). I am still working closely with many of these people, and I look forward to an even more exciting research expedition ahead.”

Software engineer Lim Xing Yi and chemical engineering graduate Ng Jian Ming seemed destined for different career paths when artificial intelligence (AI) beckoned, and the AI Apprenticeship Programme (AIAP®) gave them the opportunity to pivot. 

Lim Xing Yi was working as a digital business technologist with a local telco when he received an email from his university about the launch of the AIAP. His interest was stoked, but he had an upcoming internship placement in Shanghai and had already secured a job with the telco.

“I was keen on acquiring skills in data science and in particular AI, but I had other commitments at that point of time, so I had to forgo the opportunity,” he recalled.

But the thought persisted and in 2019, he decided to act on it. “I guess it was the strong and constant digital marketing presence from AIAP that prompted me to just go ahead and made the move,” he said.  

For Ng Jian Ming, his interest in AI was piqued when he was exposed to non-core modules such as Python Programming and Business Analytics as an undergraduate and had the chance to work with analytics during his internship. After graduating with an honours degree in Chemical Engineering in 2018, he decided to pursue a different path and signed up for AIAP.

Taking a deep dive into AI
Launched by AI Singapore (AISG) in August 2018, AIAP is a full-time structured training programme that aims to groom local AI talent and enhance their career opportunities in AI-related roles.

The nine-month programme equipped Jian Ming and Xing Yi with the technical and soft skills that they needed to embark on a career in AI.

For Jian Ming, this meant taking a deep dive into software engineering and best practices, and honing his communications skills to be able to translate technical AI jargon into terms that made it easier for laymen to understand.

For Xing Yi, important takeaways from the programme included time management and organisational skills which are crucial in a data science project.

“Building the machine learning pipeline requires logical flows with careful planning, otherwise more time and effort will be needed to rebuild the pipeline,” he said. “You are dealing not only with model training but also with data acquisition, model artefacts, the code repository, and documentations. These need to be well-organised for efficient development work and iterative training. These skills helped to save me time searching around for the things I needed.

Weekly sprints under the AIAP also gave him a sense of the typical AI project timeline and stakeholder management, and equipped him with knowledge to handle an AI project.

Working on the 100E project
As part of the 7-month on the job training on a real world AI problem, Xing Yi and Jian Ming worked on a 100 Experiments (100E) project with a medical technology start-up called EM2AI (part of Q&M Dental Group). 100E is the flagship programme by AISG to help companies develop solutions to AI problems where no commercial off-the-shelf solution exists.

The project undertaken by Xing Yi and Jian Ming was focused on helping dentists understand the dental health of the patient in a more efficient manner by detecting tooth pathologies such as decay and gum disease through the use of deep learning on dental X-ray images.

Over the course of the project, the apprentices encountered challenges such as pathologies that were less obvious and difficult to spot with the human eye, or those that were very similar in nature. With the increasing number of pathologies to detect, the speed of AI inferencing was also an issue that had to be addressed.

Our role is to ensure that the trained AI object detection models are comparable to less-experienced dentists in detecting the most common dental pathologies, and possibly many other complex and uncommon ones in the near future,” said Xing Yi.

After their apprenticeship, EM2AI went on to offer Xing Yi and Jian Ming full-time roles as AI engineers and they joined the company in June 2020 upon graduation to continue to build out the solution they were developing during the AIAP. This is indeed one of the “ideal marriage” outcomes of AIAP where well-trained Singaporean AI Engineers join their Singapore-based project sponsors to build out their AI capabilities.

A challenging and rewarding career
Five months into the “real AI world”, the work continues to be both challenging and rewarding for both graduates. “As new start-up, there was a lot of ground work to be done to set up the data science workflow, software engineering practices and agile methodologies,” said Xing Yi.

Both are working on the “upgraded version” of the 100E project as well as other AI initiatives in the company. “Given the full picture of the company’s system and future goals, the challenge now is not only to improve our AI system’s scalability but also to ensure an end-to-end AI system that is aligned with other systems in the clinic,” said Jian Ming.

Despite the new challenges, he finds the work rewarding and enjoyable. Citing the 100E project as an example, he said, “It is not only an innovative solution from a market perspective, but is also meaningful in that it has the potential to improve the quality of life for both patients and dentists.”

Both are grateful to AIAP for opening up these opportunities for them. “The AIAP is a good stepping stone to AI because it provides the chance to work on real-world problems through the 100E project,” said Xing Yi. “The opportunity is very hard to come by for someone with little experience in AI, and the experience helps convince people that we can implement and deploy an AI project.”

Echoing his view, Jian Ming said, “It would have been very difficult to enter this field without the apprenticeship programme. It was the key to help me make the transition and begin my career in the tech industry.”

For more details on the AIAP, please visit

If you are keen to know how to get into the AIAP, do check out this AIAP Field Guide


Generating Data for AI

Deep generative models help alleviate the difficulties of gaining access to costly or sensitive data

Artificial intelligence (AI) relies on data. AI algorithms are trained on large amounts of data in order to identify patterns, analyse them and develop predictive capabilities for automated output or responses.

But what if data collection is expensive and difficult, or the data is sensitive and therefore not accessible? One answer to this lies in applying deep generative models which will enable computers to synthesise new data, ultimately in an unsupervised setting without the need for data labelling. This is the research area that Associate Professor Ngai-Man (Man) Cheung from the Singapore University of Technology and Design (SUTD), is focusing on.

Prof Cheung notes that a lot of progress has been made in generating data of a single category, for example, facial images in frontal view. However, there is still much work to be done on generative models that can synthesise many diverse categories of high-quality images and data.

This is especially challenging in an unsupervised setting where no data label is used, because it involves modelling the underlying probability distributions of high-dimensional data with very many degrees of freedom.

To address this, Prof Cheung’s team took the approach of self-supervised learning which exploits the data itself for supervision.

Good results were achieved. “In some settings, our model which was trained without using labelled data was as competitive as other models which relied on labelled data,” said Prof Cheung. “We have also developed some ideas to train deep generative models with limited data.”

The ability to synthesise new image data is useful for many computer vision applications, especially in domains such as healthcare and cybersecurity where data may be difficult or expensive to collect. For healthcare and clinical applications, the data may also be sensitive.

Deep generative models can help to address these issues by using machine learning to synthesise new data for healthcare data analytics.

For example, Prof Cheung has worked with AI Singapore and its apprentices on a 100 Experiments (100E) project with medical AI company KroniKare, where the deep generative models were used to synthesise data samples for the training of classifiers. KroniKare provides an AI diagnostic tool that automatically assesses and manages chronic wounds with quick scans and accurate detection for better decision-making.  

Collecting samples for this and similar use cases can be expensive for many healthcare applications as it requires clinicians to carry out data annotation.

In cyber security, there are similar issues with data collection.  For example, some attack samples are difficult to identify and have to be analysed by security specialists, which increases the cost of access to data.

Going forward, Prof Cheung and his team will also delve into generative models which can be applied to various computer vision problems, such as few-shot image classification where models are trained to do image classification with very few examples for each category.

“I am excited about the problems our team is working on. They are challenging but the potential impact is significant,” he said.

Developing Human-like 3D Perception in Robots

The technology will enable machines to navigate safely in an environment with humans and other objects

Much work has been done in the area of robotics, from control and path planning to collision avoidance and mechanical design. But one thing remains elusive – giving robots the ability to perceive the 3D world the way humans do. And this is the challenge that Assistant Professor Lee Gim Hee from the Department of Computer Science, National University of Singapore (NUS), has set out to solve with his research into 3D computer vision.

Prof Lee’s research journey began during his undergraduate and Master’s studies at NUS, when he was working on mobile robots as part of his final year project and Master’s thesis. He noticed that the challenge of getting a robot to function autonomously in any given environment was far from being solved. And this was because the most important competency in this respect – 3D computer vision/perception – was still lacking.

Deciding to devote his time to solving this problem, he went to ETH Zurich to do his PhD in 3D computer vision.

“The main objective of 3D computer vision research is to recover geometric and semantic information about the 3D world that we live in, using sensory inputs such as RGB camera images and/or 3D point clouds from range scanners,” explained Prof Lee. This involves reconstructing 3D models and combining it with an object-level and/or scene-level understanding of the 3D world.

There are many exciting application possibilities for this. Prof Lee cites the example of a self-driving car or an industrial/domestic robot, which can use the technology to “see” its 3D surroundings and “know” its own position and orientation within that environment. This will enable it to move, navigate and interact safely in the environment with humans and other objects.

Armed with accurate 3D perception capabilities, a self-driving car can help to reduce traffic accidents, and a fully autonomous industrial/domestic robot can help lighten the workload for humans.

3D computer vision also has many applications in navigation and metrology. Prof Lee gave the example of a student who is lost on campus. The student can take a photo of his current location and send it to a cloud server which has a 3D model of the campus. 3D computer vision algorithms can then register his photo with the 3D model to identify the student’s location and suggest a possible to route to his destination.

The same 3D model and 3D computer vision algorithms can be used in construction metrology to monitor and analyse structural integrity and aid architectural design to prevent catastrophic failure in buildings. They can also be used to help archaeologists and historians preserve a digital copy of an ancient artefact, or law enforcers to preserve a crime scene for further investigation.

However, despite the progress that has been made, Prof Lee finds that there is still a gap in the research. “Studies of geometry and semantics are still largely decoupled,” he explained. “Algebra and physics are used to solve the geometry problems, while machine learning techniques that learn from big data are used to solve the semantic problems.

He is intent on finding the missing link between the two approaches, so that geometry and semantic information can complement and compensate for each other’s strengths and deficiencies. The ability to learn from big data can be used to mitigate difficulties in handcrafting sophisticated geometric models to model complex scenes; and prior knowledge of geometry can help reduce the reliance on large amounts of training data without sacrificing accuracy in semantic understanding. “This will take 3D computer vision into a whole new level, paving the way for a more holistic understanding of our 3D world by machines,” he said.

Forging a Strong Collaboration to Anchor Deep AI Capabilities and Develop Local AI Talent for Singapore’s Engineering Cluster

In an effort to anchor deep AI capabilities and develop the local AI talent for Singapore’s engineering cluster, AI Singapore (AISG) recently signed two Memorandum of Understanding (MoUs) with The Institution of Engineers, Singapore (IES) and SMRT Corporation accordingly.

The signing ceremony was held in conjunction with the annual National Engineers Day (NED), an engineering festival for students, in collaboration with their partners. Miss Grace Fu, Minister for Sustainability and the Environment, who was the event’s Guest of Honour, witnessed both signings.


The MoU with IES will drive cluster-wide adoption of AI solutions by its member companies in the engineering sector in Singapore. The collaboration will also seek to grow certified local AI engineering talent through AISG’s talent programmes such as AI for Industry, AI Apprenticeship Programme and the AI Certified Engineer certification programme. 

“AISG is honoured to partner with The Institution of Engineers, Singapore (IES) and the AI Professionals Association (AIP)* to identify up to 20 AI projects, which will be supported under AISG’s 100 Experiments (100E) programme in the engineering sector in Singapore, as well as train up to 50 Singaporeans as Certified AI Engineers through AISG’s AI Apprenticeship Programme (AIAP®) over the next two years,” said Laurence Liew, Director of AI Innovation, AI Singapore. “In addition, AISG will partner with Google Cloud on this initiative, providing participants in the programme advanced Cloud AI technology and expertise to help them on their journey.”

In the second MoU signed with SMRT, they will be the first organisation in Singapore to collaborate with AISG on multiple AI projects under the AI Engineering Hub (AIEH) programme. The AIEH programme consolidates between 3 to 10 100E projects under a single organisation via a Master Research Collaboration Agreement to streamline the approval process.


The day culminated with another related exciting announcement. ST Engineering Electronics, one of IES’s member companies, received a Letter of Award from AISG for their 100E project on AI-based Object Recognition for unmanned surface vehicles. This will include providing an AI engineering team that comprises a Principal Investigator, project management, mentors, engineers and AI Apprentices.

*Founded in Singapore, AIP is the first grassroot – driven association for engineers and professionals working in AI-related roles. At the same event, an MoU was signed between IES and AIP to develop and promote joint learning and development programmes for the engineering communities in Singapore

Blazing The Trail in AI Talent Development

Artificial Intelligence is one of the most important areas of technology today. It mimics human capabilities such as understanding, reasoning, communication and perception and can be applied to a multitude of use cases across almost all spheres of life – from healthcare and transportation to retail, marketing and financial trading.

In Singapore, AI has been identified as one of the core technologies that is essential to the country’s push to become “digitally ready”. As the national programme office for AI, AI Singapore’s (AISG) mission is to develop deep national capabilities in this field, in particular to “grow our own timber” by developing the next generation of local talent.

To achieve this objective, we have adopted a transformative approach that starts with early acquisition of basic AI knowledge and awareness in primary school, and progresses through various stages of skills and capability development to ensure that new AI talents will be able to hit the road running when they enter the industry.

An adaptive curriculum
For a fast-moving field like AI, a traditional curriculum based on a relatively stable body of knowledge may not be the most effective approach to teaching and learning. We decided instead to borrow a concept from the software development domain and adopt agile methodology to deliver AI literacy to primary school students.

AI for Kids (AI4K)® is an adaptive programme tailored to pupils aged 10-12 years old. Just like agile development, the curriculum is not static but evolves in response to the latest developments, trends and issues in the AI world by incorporating topics of growing importance.

AI4K® takes an interactive approach using hands-on exercises to reinforce learning concepts as pupils progress through the lesson plan.

It takes a village

In developing an AI programme for children, we identify the key role of Parents and School volunteers early on that makes the programme outreach successful and sustainable. AI4K programme interfaces closely with this ‘AI Educator’ partners to enable them to have active roles to encourage and support the learning process of the children.

AI4K® was launched in June 2019 and by end March 2020, before the suspension of all school extra-curriculum activities due to the COVID-19 pandemic, 22 primary schools already had AI4K-certified instructor teams in place, delivering the programme to 384 students.

“It was a wonderful experience teaching the kids about AI. We could see from their faces that they really enjoyed the hands-on activities through the AI4K Bootcamp. It is indeed a great platform to provide a kick start to young kids to build their basic foundation in AI.” said Drishti, parent support group.

International Thought Leadership
AISG was invited to the Global Education Task Force for K-12 (EDUAI-20) this year, organised in conjunction with International Conference on Artificial Intelligence in Education (AIED-20).     The task force brings together national-level organizations and key opinion leaders to pool our collective knowledge and expertise in AI education for students all over the world. 

Our AI4K® programme was selected as a case study for the 10 July 2020 workshop, which our deputy director, Koo Sengmeng, shared to an audience of over a hundred international audience.

In recent years, AI has attracted a lot of attention from the public, and become a major topic of economic and societal discussion. AI already has a significant influence on various areas of life and across different sectors and fields. The speed and force with which AI is impacting our work and everyday life poses a tremendous challenge for our society and educational system. Teaching fundamental AI concepts and techniques has traditionally been done at the university level. However, in recent years several initiatives and projects pursuing the mission of K-12 AI education have emerged.

Improving data fluency
While AI4K® focuses on building basic AI literacy in younger children, the AI for Students (AI4S)™ programme takes secondary and tertiary students to the next stage of data fluency by equipping them with programming and data skills.

To support their learning journey, educators from the Ministry of Education’s public schools can tap on AISG’s partnership with DataCamp to leverage the latter’s learning platform for teaching purposes. Students can also embark on independent learning in the lessons under AI4S which include learning Python programming and Git, which are necessary skills to get involved in working with AI.

Since AI4S® was launched in November 2018, it has reached out to more than 19,000 students across the Singapore public schools. 

For my past two years of experience with AI4S, it has been a remarkable programme for both students and educators alike, because in addition to offering free access to the Datacamp modules, it is constantly developing more free AI educational content in the AI Makerspace, where one can delve deeper with learning, doing and sharing of content and applications related to AI. Indeed, I have personally benefited very much from AI4S, and certainly hope this programme will eventually become an integral part of the educational journeys of students and educators."

Dr Chen Weiqiang, Lecturer in charge of Robotics and Computing Club,
Eunoia Junior College

"In the Robotics and Computing club, my CCA members and I feel that the AI4S programme has been effective in building our learning foundations in machine learning, whilst honing our Python programming skills through the rich content offered by the Datacamp platform, and the AI Makerspace."

Ho Jiawen Jane, President of Robotics and Computing Club,
Eunoia Junior College


Creating AI-savvy consumers
Another prong in AISG’s talent development efforts is to train students to be savvy consumers of AI products and services, so that they are able to identify opportunities for AI applications in their daily lives.

In line with this, an AI Foundation Course has been introduced for all polytechnic and Institute of Technical Education students as part of their digital awareness training. Available since April 2020, the course enables students to learn about modern AI technologies and applications, how to identify potential use cases and to build a simple AI model with online tools. All ITE students also have to take a module on the basics of AI in their first year of study. This exposes them to the different fields within AI and how the technology can be deployed in services and products.

“ITE is preparing all its students to be ready for a future where AI is pervasive, as well as training them to take on the emerging jobs. In the coming years, new AI-enabled products, services and businesses will be made available. New job roles and employees with AI skills will be in demand. We want to enable ITE graduates to be ready to seize the employment opportunities in the new digital economy, and contribute to Singapore’s progress and transformation.” said ITE chief executive Low Khah Gek1.

Aligning AI learning with industry experiences
To further enrich AI learning and ensure closer alignment with industry experiences and requirements, a new AI Makerspace has been launched at the Singapore Polytechnic (SP) to provide students with internship opportunities and the chance to be mentored by AISG engineers and staff from SP’s Data Science and Analytics Centre.

A satellite node of AISG’s existing Makerspace, the SP AI Makerspace allows students, including those from the Diploma in Applied AI & Analytics programme, to leverage tools such as AISG’s AI Bricks to build AI solutions.

The latest AI Makerspace is timely as the Services and Digital Economy Technology Roadmap cites AI as one of the key technology areas that will change the world and take Singapore’s economy forward in the coming years. As Singapore embarks on its Smart Nation journey, SP is glad to partner AISG to equip our youths and companies with much needed technological skills that will help our industries transform.” said Dr Edna Chan, Centre Director, Data Science and Analytics Centre.

AISG is also working with universities to expose more students to AI. Under a collaboration with the National University of Singapore and Singapore University of Social Sciences, undergraduates will be able to use credits from AISG’s AI for Industry (AI4I)® programme to help fulfil their degree requirements.

Open to industry professionals such as technical executives, managers and developers as well as the undergraduates, AI4I introduces participants to AI concepts and use cases, and equips them with the programming skills to build data and AI applications. It is hosted on AISG’s online AI Makerspace platform and leverages DataCamp for the learning resources that participants need to complete the programme.

Developing industry-ready AI talent
The AI learning pathways do not end here. For students and industry professionals who complete the AI4I course, the next step would be to embark on the AI Apprenticeship Programme (AIAP®) which aims to groom local Singaporean AI talent and enhance their career opportunities in AI-related roles. The apprentices will work on real-world industry projects and deepen their skills not only in AI and machine learning but also in software engineering skills for model deployment into production. AISG also provides an opportunity for aspiring start-ups to pitch their AI idea to develop an AI Brick which could result in a possible spin-off.

The programme also prepares the learner to attempt the technical test for AI Certified Engineer Associate – the first of four professional certification levels under the AI Certification, a professional qualification programme to recognise and award credentials to working professionals in AI-related engineering roles.

To sum up, as we develop the next generation of AI talent, we are mindful of the need to seed AI literacy in the formative years of education and to develop data fluency across the general student population. It is also important to inculcate awareness of the potential use cases and opportunities for AI, in order to spark new ideas and catalyse innovation. 

At the same time, for those with the interest and acumen in AI, regardless of their professional background, we will provide opportunities for them to gain a foothold in the industry through programmes such as AI4I and AIAP. We will also provide opportunities for certification and up-skilling.

In short, we will cast our net wide, keep the door open to talent, and work to ensure clear pathways for progress.

1 Extracted from “All ITE students to learn artificial intelligence in first year to take on future jobs” The Straits Times, 28 September 2020

“...So the best way to create a nation is to start from the schools.”
S Rajaratnam (Deputy Prime Minister of Singapore from 1980–85)

AISG Helps Empower Infineon’s Next Generation of Employees

At a ceremony to mark their 50th year in Singapore, German microchip maker Infineon Technologies announced that they will make Singapore its first global AI innovation hub by 2023. The plan includes the upskilling of more than 1,000 of its 2,200 employees here and the deployment of about 25 unique AI projects covering the entire value chain of activities by 2023

And AI Singapore is excited to partner with Infineon on this endeavor to empower the next generation of employees. In this collaboration, AISG will help Infineon develop company-wide AI literacy, enable their staff to upskill themselves and learn data science and AI in Python and recognise their internal AI talents via the Chartered AI Engineer professional qualification.

“AISG is excited to play a part in contributing to Infineon’s digital transformation and training journey. As the national AI programme, our key thrusts are to anchor deep national capabilities in AI and to groom local AI talents. As AI becomes more pervasive and with Singapore moving into becoming a smart nation, it is important that our people are not only AI-aware, they will also be AI-enabled and ready to be producers and users of AI products and services.” – Laurence Liew, Director of AI Innovation, AI Singapore

Singapore’s Deputy Prime Minister, Coordinating Minister for Economic Policies and Minister for Finance Heng Swee Keat witnessed the event held at Infineon Technologies Asia Pacific Headquarters in Singapore.

(L-R): Laurence Liew, Director of AI Innovation, AISG, Deputy Prime Minister Heng Swee Keat, Chua Chee Seong, President and MD of Infineon AP and Pamela Leong, VP of Human Resources, Infineon AP


Catalysing AI Adoption to Boost Community Healthcare

Explainable AI as a Service aims to make AI understandable and easily configurable for use by healthcare providers, practitioners and patients

By 2030, chronic diseases such as hyperglycaemia (Type II diabetes), hyperlipidaemia and hypertension, collectively known as 3H, will be the top three diseases affecting 18-69 year olds. In particular, the number of people with Type II diabetes will hit an estimated 1 million if nothing is done to change the trajectory of this disease.

The third grant awardee in the AI in Health Grand Challenge – a team of 19 principal investigators from National University of Singapore (NUS) and National University Health System (NUHS) – is aiming to address this by catalysing the mass adoption of AI in healthcare.

About Explainable AI
The team is developing tools and techniques that will make AI understandable and easily configurable so that it can be used by non-data scientists such as clinicians. It is building a platform to enable the delivery of AI as a service, so that it can be applied more widely in areas such as precision medicine, preventive advice and automatic lifestyle coaching. Prototype AI devices are also being developed for deployment and testing in a community setting, to provide healthcare support for patients with chronic diseases.

12-month report card

Forming the backbone of the team’s work is PANDA, a platform which enables AI to be delivered as a service to solve the problems of 3H. Advanced AI techniques were developed to boost the training and inference capabilities of PANDA, which also has built-in automatic model selection and automatic hyperparameter tuning capabilities. This makes it easy to use, especially for healthcare practioners who do not have much background in AI and data science.

In the back end, GPU resource scheduling has been automated to optimise the use of processing power for deep learning training jobs.

The team has also developed a table-top telehealth device called MEDDi (medical digital intermediary) which incorporates vital signs sensing, biomarker sensing, video analytics, a chatbot and an AI-enabled intervention mechanism. MEDDi can be deployed in polyclinics and homes so that patients with chronic diseases can measure and monitor vital health parameters without having to go to the hospital.

For AI-enabled intervention, the device is equipped with CURATE.AI, a software module  which integrates information from various sensors to calibrate and modulate medication dosage, especially in a multi-drug scenario. This enables healthcare providers to make informed decisions about dosage adjustment and behavioural therapy.

The MEDDi chatbot supports users through natural communication channels such as text and voice. For voice communications, the speech recognition system also supports Singlish, which includes code switching between English and Mandarin within the same sentence.

For vital signs sensing, the team has developed and deployed a network of wireless sensors embedded in clothing, which use near-field communications to provide continuous measurement of human physiological signals. For example, self-powered socks equipped with sensing capabilities enable long-term monitoring of the user’s physiological status  including his/her gait in real-time. Another sensor attached to the skin measures pulse wave velocity, making it easy to monitor blood pressure.

Various AI techniques have also been adapted for use in different healthcare-related scenarios. For example, Foodlg is a dietary health app which facilitates food journaling with automatic food image recognition using a convolutional neural network. Pilot studies of Foodlg as an auxiliary healthcare solution are being carried out in collaboration with several hospitals and health establishments. 

Another AI technique, reinforcement learning, has been applied to train the system to improve the patient’s glycaemic control and avoid hypoglycaemia and other complications. 

In related work, the team has also recruited volunteer patients for a Familial Hypercholesterolemia study which focuses on high-risk hyperlipidemia case identification and genetic studies of familial cholesterol risk. A precision cost-benefit calculator has also been developed to support decisions on the use of expensive or branded medicines, and machine learning models are being used to identify genetic signatures of statin-induced muscle aches.

Benefits to Primary Care Teams

With MEDDi, the burden on hospitals can be alleviated by transferring some of the health care functions to polyclinics and eventually to the patient’s home.

One of the main reasons patients with chronic diseases need to go to hospitals is the lack of medical equipment at home to measure and monitor vital health parameters. It is estimated that MEDDi can reduce the number of hospital visits by addressing up to 80 percent of a patient’s healthcare needs at home.

For doctors, AI-as-a-Service delivered through PANDA enables them to use AI intuitively to enhance precision medicine, optimise drug combination therapy and give precise advice on 3H prevention and management.

Benefits to End-users

For patients, devices like MEDDi provide the convenience of monitoring vital health parameters from home, allowing them to make fewer trips to the hospital. At the same time, the integration of this data and application of the right AI models ensure they receive effective interventions and better quality care.

The general public also benefits from the use of AI techniques in apps such as FoodLg app for automatic analysis of food and nutrient intake and coaching.

What's Next

The team is continuing with extensive data collection efforts to support AI training, validation and testing. Preparations are also underway for the MEDDi prototype to be deployed for beta testing.

For more details, please visit

About the Team

Lead Principal Investigator: Professor Ooi Beng Chin (NUS)

Co-Principal Investigators:

  • Dr Ngiam Kee Yuan (NUHS)
  • Professor Dean Ho (NUS)
  • Professor Wong Lim Soon (NUS)
  • Associate Professor Xiao Xiaokui (NUS)
  • Associate Professor Ng Teck Khim (NUS)
  • Assistant Professor Bryan Low Kian Hsiang (NUS)
  • Assistant Professor Wang Wei (NUS)
  • Professor Lim Chwee Teck (NUS)
  • Associate Professor Qiu Anqi (NUS)
  • Professor Li Haizhou (NUS)
  • Associate Professor Mehul Motani (NUS)
  • Associate Professor Vincent Lee Cheng Kuo (NUS)
  • Assistant Professor John Ho SY (NUS)
  • Assistant Professor Edward Chow Kai-Hua (NUS)
  • Assistant Professor Benjamin Tee Chee Keong (NUS)
  • Assistant Professor Feng Mengling (NUS)

Host Institution: National University of Singapore (NUS)

Partner Institution(s): National University Health System (NUHS)

Other achievements:
The Explainable AI team has papers featured in some of the top conferences including Neural Information Processing Systems (NeurIPS 2019), the Association for Computing Machinery’s Special Interest Group on Management of Data (ACM SIGMOD 2020) and the International Conference on learning Representatins (ICLR 2020), as well as some of the top journals.

About the AI in Health Grand Challenge

The AI in Health Grand Challenge is a five-year, two-stage programme with a total funding quantum of $35 million. AI Singapore, together with an International Review Panel, selected three projects to be awarded Stage 1 funding of $5million per project for the first two years. The projects focused on applying AI technologies in innovative ways across the continuum of 3H (hyperlipidemia, hyperglycemia, hypertension) care.

Translating and Scaling AI Technology for Healthcare

Industry workshop provides AI in Health Grand Challenge teams with a platform to share their progress and future plans, and to reach out to potential partners

What’s next for the teams of the AI in Health Grand Challenge? This was the focus of an industry e-workshop on “Translating and Scaling AI Technology for Healthcare” which was held on 19 November 2020.

The AI in Health Grand Challenge is a $35m programme to fund AI research to support primary care teams in reducing complications arising from hyperlipidaemia, hyperglycaemia and hypertension (3H).

In the two years since its launch, the three awarded teams have made substantial progress towards the goal of using AI to help slow disease progression in 3H patients. One of them has developed an AI-assisted 3H Care (A3C) system to identify pre-3H persons, assess the status of 3H patients, and provide personal or group-based interventions through gamification. Another is rolling out an AI platform, JARVIS-DHL, which can be used to improve the care delivery process by facilitating evidence-based personalised care and shared-decision making. The third is aiming to catalyse mass adoption of AI in healthcare with Explainable AI as a Service.

The workshop provided a platform for the three teams to share their progress and future plans, and brought researchers and clinicians together with industry experts to discuss the translation and deployment of AI research and technology into scalable healthcare solutions.

In her welcome remarks, Prof Leong Tze Yun, Director of AI Technology, AI Singapore, highlighted the special features of the AI in Health Grand Challenge and its focus on measurable objectives and translation pathways. She also spoke about the technical challenges of effective translation and reiterated AI Singapore’s commitment to continue supporting the teams in their journey by helping them to source for additional data and funding opportunities.

Discussing AI Advances for 3H care

Representatives from the three teams took part in a panel discussion on “AI Advances for 3H Care” which was moderated by Dr Stefan Winkler from AI Singapore. A3C was represented by Dr Wang Di from the School of Computer Science and Engineering at Nanyang Technological University (NTU); JARVIS-DHL by Prof Ng See Kiong from the Computer Science Department, National University of Singapore (NUS); and Explainable AI as a Service by Prof Dean Ho from the Biomedical Engineering Department of NUS. Joining them was Mr Sutowo Wong, Director of Analytics & Information Management at the Ministry of Health (MOH), who shared MOH’s perspective on the use of AI for treating and managing chronic diseases.

The panellists discussed challenges related to medical data such as variability between patients or missing information, the issues involved in AI-based medical decision making; the explainability of machine learning models and the role of users (both patients and medical practitioners) in the process.

Delving into Integration Challenges

Another topic that was discussed at the industry workshop was the “Integration and Deployment of Healthcare Solutions”. This second panel session was moderated by Mr Chua Chee Yong from Integrated Health Information Systems. The speakers delved into the challenges of integrating healthcare data across public and private healthcare institutions, and the role of the National Electronic Health Records in consolidating, streamlining and standardising patients’ records to make the information shareable with health care providers.

They also highlighted the main hurdles for the deployment of new health technology solutions and how they may be overcome, and discussed the key technological, operational and design considerations that could help drive the adoption of AI healthcare solutions in Singapore.

The panellists included Prof Robert Morris from the MOH Office for Healthcare Transformation, Dr Sue-Anne Toh from Novi Health, Mr Gavin Teo from Altara Ventures, and Dr Frank Qiu from User Experience Researchers.

Progress and Future Plans
The workshop also created opportunities for the teams to link up with potential partners to do this. Breakout sessions were organised to allow representatives of the Grand Challenge teams to network with workshop participants and discuss more details about their work, the use cases of their research, and potential collaboration.

The virtual event attracted more than 170 participants, about half of whom were from hospitals and private sector organisations. Significantly, more than two thirds of the registrants were senior professionals including C-level executives, directors and professors.

As we move into the second stage of the AI in Health Grand Challenge next year, it will be important for the teams to collaborate with the right partners for deployment. They have made significant progress so far, demonstrating compelling prototype solutions for a wide range of applications, and we are heartened to see the high level of industry interest in their work.


AIAP Start-up Track: In Conversation

With the opening of the application window for Batch 8 of the AI Apprenticeship Programme, a notable addition to the ever-improving talent programme is the Start-up Track. This empowers those who have the entrepreneurial zeal to define their own projects within the apprenticeship. I had a chat with John, my colleague spearheading the track, to find out more.

Below is a transcript of the conversation [*].

Basil : Hi, John. How are you doing?

John : Doing great today. Thanks, Basil. Glad to be on this podcast with you.

Basil : Today, we are going to talk about start-ups and the first question I have for you is, why do a start-up?

John : I think there are many reasons why we see people launch start-ups. Usually you would hear as number one, I want to be my own boss, or number two, I want to be able to make a lot of money and number three, which I actually think is the most important reason and one that you must have no matter what start-up you are intending to launch, is that you must have the passion for the particular problem or a particular topic or industry that your start-up is in and the kind of value it is trying to bring to users and customers. So, those are the three main things. I started a start-up previously and when I did it I asked myself those three questions. So, I said, yes, I have a passion for the topic and I did want to be my own boss and I did want to make a lot of money. So, with those three checkboxes I ticked off, I decided at that time for myself to launch a start-up. I think that most founders should have at least two of these three items in order to really enjoy the process.

Basil : So you are actually speaking from your own personal experience. Could you share a little bit more about what your start-up was doing?

John : Yes, sure. I used to work in the finance field. I was there for a couple of years once I graduated school, but after a while I kind of got restless in a big corporate environment. An opportunity came along. One of my friends had done some deep research into a biotechnology that was able to replace chemicals in the industrial and agricultural process and it looked like a great product. It was cheaper, it was easier to use and it was environmentally-friendly. So, we licensed the product and began selling it both in Singapore and eventually branched out to the rest of ASEAN. Eventually, my partner and I looked at where the market was and he thought that China was the place to go and he wanted to go there, while I was a bit more comfortable in Singapore. I was not ready to move yet, so what I did was I sold out to him and he is in China now and doing really well.

Basil : Amazing work. Now, every start-up starts out with an idea. So, how does one go about developing such an idea?

John : The way I see it, there are two main things that make a start-up business successful. The first is that you have to have a good product and the second is that you have to be able to tell enough people in the market and convinced them to buy your product. Once these two things are present – you have a good product and you have people buying your product – then you have a good business. So, definitely one of the most important things is that the technicals behind your product must be good. It’s very difficult to sell a bad product into the market or a bad idea to customers. You might be able to convince a few people to join, but once they realize that your product really isn’t up to scratch, then you’ll probably see big churn or people stop purchasing your product. So, the technical backing and the quality of your product needs to be high.

At the same time, you also need to be able to drill down and validate the market for your product. Usually, most founders, and I myself in the beginning made the same mistake, I looked at how big the market was and thought, oh wow, the market is so big – I’ll go tackle all of it. And usually that ends up being a case where the start-up bites off more than it can chew. So, actually what you would want to look at and what most VCs and investors should look at is, for a start-up that is launching its first product that is fresh to the market, you want to have drilled down on a very specific market segment. For example, if your market was students, that would probably still be too broad. You would probably have to say, I’m looking at students in the secondary school level and maybe even one level deeper and say who has private tuition. So, you zoom in to that very specific market where all your customers have the same profile and check the size and viability, whether this particular customer would actually see value in your product. When you find a big enough pool of users or customers combined with a good product, then those are two very key things that everybody – yourself, investors, your employees – who want to see good growth prospects into the future.

I think that one of the other things that most people look at kind of in the wrong way as well is the subject of competition. Usually, when I hear a lot of pitches, people are afraid to bring up competition. They think that having a big competitor in the market would automatically invalidate their idea, or they think that they have to position their product in a way where they can say that, oh there is nobody that’s doing this in the market right now. Actually, both of these are wrong. Let me illustrate why. When you look at big competitors, say the Googles or the Microsofts of the world, they have to have one product that services a whole range of different people and they get to be big because their product is actually good enough to satisfy most user needs. But, that’s probably just at the average or the bare minimum level. When you drill down to people’s specific needs, specific experiences for each unique target market, more often than not, you will find ways in which the users are dissatisfied with the offerings of these big competitors. These big competitors can’t afford to pay that much attention to them, but as a small start-up, you can. You have that nimbleness, you have the ability to really make something that delights your users, so that means that you shouldn’t be afraid of the big boys being in your competitive space, unless you are planning to challenge them directly. On the other hand, some founders think that I have a blue ocean market and it will be great if I can prove that nobody is currently in the space. That’s also something that actually triggers some red flags for investors, because it is very rare that a real problem that people have is not being addressed by someone or something out there, even if it was a non-technical solution, even if most people are doing it themselves, or there must be some kind of solution to a problem that is present today and being able to list out the ways where people are offering solutions to these problems or solving the problem themselves in a very inefficient manner is also part of the competitive landscape. So, when we look at competition, we want to know that there are people out there solving these problems by themselves or with the help of others, so that we can establish what are the alternatives to our product out there and demonstrate actually that there is a problem in the market.

So, these three things : a good product, good market size, as well as the presence of competitors.

Basil : Well, certainly a lot of points to consider when developing an idea. Yes, of course, an idea is just half the story. It is certainly essential, but not sufficient. The other half probably I would say it’s the people. Just as in any organization, people are important. So what kinds of people typically do well in the start-up space? For example, at which point in life you are in – is it for young folks or is it for mature folks? What do you think?

John : I think this is a very interesting question. So, I have been going around the multiple incubators in Singapore as part of the collaborative nature of what we do over here at AISG, and I find a pretty even split between young folks and really mature folks – you are talking about guys who have actually collected their CPF – and I think on either side of the spectrum, both kinds of founders bring different kinds of energies and skill sets to the table and there are pros and cons to it. So, if I’m really young, for example, maybe I just graduated or I’m still a student, I have a lot of energy, I have a long runway and I am able to pivot and harness the youth, to let people give me a chance and try and fail, whereas for mature folks, we find that they come to the table with resources, with a deep understanding of the problems, because they’ve probably experienced it in their own corporate experience or their own lives, and they bring networks and the ability to understand how to deal with both people in and outside their company, how to structure things well simply by life experience. So, they will probably be a lot more efficient out of the gate in terms of doing things in a more structured way. But, of course, they probably don’t have the same amount of raw energy that the young folks have. So, both young folks and mature folks, I think, will fare equally well in the start-up space. The trick would be to play to your strength.

Basil : Well, talking about strengths. So, any particular thoughts on the skill sets, like hard skills or soft skills, that are especially relevant for start-ups?

John : I think that when it comes to skill sets, one of the things you can imagine – and it’s true – is that as founders or early employees of start-ups you need to have a very broad range of skills, you need to be adept at moving from technical skills like writing code or creating presentations all the way to soft skills like handling customers and on-boarding early employees, so on and so forth. So, I think that the best founders will be the ones that don’t let the perception that a particular skill is hard or not for them, and then they don’t engage with it at all. I think founders that jump in and try out being technical, try out the soft skills part, they will only get better with it over time, and developing that breadth is an important thing to do as a founder. At the same time, you are probably not gonna be good at everything, so that’s where we also think it’s important to find a good co-founder that complements your skill sets. You would probably let the guy with the better set of soft skills focus on leveraging it for the benefit of your start-up, and the guy with, say, the better set of technical skills be able to spend more time leveraging that to build a good product. So, if you’re a solo founder, you will have no choice, you have to work a bit harder. One of the benefits of finding a cofounder is you’re actually able to divide that work up and focus on the parts that you do best. At the same time, of course, it also doesn’t mean that everybody just does their own thing in a silo. You will still need to be able to know enough of what the other side is doing to be able to fuse everything at the end of the day so that the start-up can move forward.

Basil : Of course, related to skills would be personality. Any thoughts on that?

John : I think personality-wise, no matter whether you are introvert or extrovert, you need a never-say-die kind of attitude. You can’t let failure get you down, and definitely when it comes to start-ups, you will stumble, you will fall, you will get knocked down multiple times, and the most important personality trait is tenacity. To be able to pick yourself up and try and try and try again. So yeah, I think whether you are an introvert and prefer to work independently, or whether you are an extrovert and you sync well with a team, all of these configurations will work equally well in a start-up context. You just need to be able to find the right people to gel with, and once you establish a productive way of working with each other, it doesn’t really matter how you guys do it as long as you do it well.

Basil : Right, so talking about gelling within the team, of course, no start-up is an island. The start-up itself exists not in a vacuum, but within an ecosystem. What you think about the start-up ecosystem here in Singapore?

John : I actually think that compared to five years ago, maybe ten years ago, the start-up ecosystem is much more robust. There is a lot of institutional help, a lot of funding, especially from the government to encourage people to be brave and take that leap if they have good ideas and they really want to launch a start-up. Previously, it was a bit more the case of everything was on the shoulder of the entrepreneur and they were expected to soldier through adversity, but now wherever you look you will find players in the space encouraging people to start-up, whether it’s in the form of giving them funding, whether it’s in the form of incubating them as accelerator, or introducing them to the next stage of venture capital funding… the field is a lot more robust now, so I am actually quite happy with what we have here in terms of the start-up ecosystem in Singapore and if you asked me to start-up here, I would say ya for sure there’s nothing that is lacking in this particular ecosystem.

Basil : That’s certainly very good news. Now we come to the really interesting part which is the AI Apprenticeship Start-up Track which you are leading. Could you tell us about, say, the history and the process of this track?

John : Right. So, AI Singapore has a talent development programme called the AI Apprenticeship Programme (AIAP) and we are now in our seventh iteration – Batch 7 has just come in and we are about to bring in Batch 8 sometime in early 2021. The number of applicants and the quality of applicants for our talent development programme has steadily risen over the last few batches and we found that as we spoke to some of these apprentices, a lot of them had side projects, a lot of them had ideas that they were developing on the side, so we began to toy around with the idea of letting them actually launch those ideas officially and support them officially with AI Singapore resources if they could pitch it to us right and fulfill the three basic criteria of having a complementary team, having a good idea of the technical backing behind their product, and a good business case. So we launched that softly in Batch 6 and now we’re going official in Batch 8, because that has been quite a positive experience for everyone.

Basil : So, what would you say the track offers which other incubators don’t?

John : I think what we do differently here at AI Singapore is that when you come in to AIAP, you go through a nine-month process. Two months are earmarked for deep skilling, so that after you are done, you can hit the ground running for the project proper. And in seven months, you are actually tasked to build something that you can put up in the marketplace and bring in beta users. So, the challenge that we give to our guys is, if you are a B2B product, get three to five beta customers for your product by the end of the nine-month period. For B2C products, we say challenge yourself : go for five to ten thousand users by the end of the nine months and that actually would set them up very well to graduate into another accelerator programme or even raise a venture investment on their own. So, that’s what people go through when they come into the Start-up Track in AIAP. In terms of what we do special, I think because we were set up as a talent development programme specifically for technical AI skills, we have full-time engineers that we assign to each of these start-ups that get through our pitching process. And not just that. These engineers are supported by our commercial team, also on a full-time basis, as well as a whole range of very experienced what we call heads at our AISG programme that are familiar with natural language processing, familiar with computer vision, familiar with software engineering and the backend infrastructure. We make all that available to start-ups inside our programme on a full-time basis. Basically anytime you have a problem, our main job is to come and help. Other places usually have this on a part-time basis, so we make sure that the mentoring is so-to-speak intensive and always switched on. We found that that really helps our start-ups move fast and pivot very quickly when they realise that something might not be working. The other thing which our start-up incubator offers is a significant amount of funding. When you join the Start-up Track, you actually get a stipend of between $3,500 and $5,500 depending on where you are in your career. Not only do you get that cash stipend, you also get various kinds of in-kind funding such as the full-time staff that will be posted to your project to mentor you. You will get compute credits, a lot of compute credits if that is something that your start-up needs. So, that’s a lot of support, up to $180,000 of support available to a start-up that joins this track. I would say that this is one of the more generous programmes out there when you look at it as a whole.

Basil : So, there’s a lot of support and resources at the apprentices’ disposal. How are the current folks on the track coping?

John : I think they’ve been having a great time. Something interesting in our programme is that we actually have more technical people come in than business-oriented people, so they don’t really have a big issue with developing a technically sophisticated product. More of the help that they need is actually figuring out how to approach the market in a smart way. So we have actually been ahead of the curve on most of our tasks. Our demos are actually coming along quite nicely a few weeks ahead of schedule and right now for our inaugural batch of start-ups, they are actually about to launch their demo products into the market in January of next year.

Basil : Really looking forward to that. Just to wrap up, to those listeners out there, people who always wanted to do a start-up but are still looking for a good incubator, or those who have the idea of a start-up cross their minds before and just need to be pushed or encouraged little, or maybe even people who never knew that they wanted to do a start-up but after hearing what you have said have a certain passion lit in them, could you make your final pitch for the AI Apprenticeship Start-up Track?

John : Yeah, I think if you are considering developing yourself in the AI field at all, the AI Start-up Track at AI Singapore is something that will challenge you, whether or not you are looking at how to build the backend of your product all the way to the AI model development, all the way to actually talking to the users and making sure that what you deliver is something that is a pleasant experience for them to use. This will challenge all the skill sets that one wants to develop and it will come in handy whether or not you actually choose to continue with your start-up after the nine months or you decide that you might want to move on to do something else not in the start-up space. I think that the skills that you pick up are going to be very transferable, so if you are thinking of how to develop yourself more in the AI space, there’s actually no better project to do for nine months than to try to launch a start-up, because I guarantee you that you will learn far more than any other kind of project there is out there.

Basil : Now that the Batch 8 application is open, listeners, do consider this and it could be the best decision of your life. Thanks, John, for sharing so much with us today.

John : Thanks, Basil.

[*] This conversation was transcribed using Speech Lab. The transcript has been edited for length and clarity.

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