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Do Great Things Together With Federated Learning

Last December, the Microsoft AI, ML Community hosted a ‘live’ online event where the Federated Learning team in AI Singapore walked attendees through the Synergos platform they had been building for federated learning. The high level architecture of the platform had already been outlined in a previous post. This event provided an opportunity for the team to explain to the community in greater detail what federated learning is in general and how Synergos works in particular. It was also a great chance to receive feedback and answer questions.

The event has been recorded and below is a list of the highlights. You can jump to the part which interests you by clicking on it.

  1. The Data Privacy Problem in Machine Learning
  2. How Federated Learning Solves the Data Privacy Problem
  3. Building Synergos
  4. Use Cases
  5. The Federation Component
  6. Demo
  7. The Roadmap

You can also view the full recording below. Many thanks to host Setu Chokshi for making this possible.

Get in conversation with us at Let’s do great things together! 🤝

1. The Data Privacy Problem in Machine Learning

Machine learning requires immense amounts of data. However, this might not be directly accessible in certain applications for various reasons, including personal data protection concerns and business considerations.
( ▶️ Jump to video segment)

2. How Federated Learning Solves the Data Privacy Problem

Federated learning is based on the principle of sharing how to adjust and improve a model, rather than the data upon which the model is trained.
( ▶️ Jump to video segment)

3. Building Synergos

The motivation behind Synergos is explained. Built upon PySyft, a tour of the different components is given.
( ▶️ Jump to video segment)

4. Use Cases

Go through some possible use cases for federated learning. Understand the steps involved and see the benefit of federated learning compared with the results from local training and centralized training in a pilot of Synergos in the healthcare domain to predict ICU in-hospital mortality.
( ▶️ Jump to video segment)

5. The Federation Component

The Federation component in Synergos is where the federated learning takes place, which happens in three phases consisting of nine steps.
( ▶️ Jump to video segment)

6. Demo

Using the heart disease public dataset from the UCI machine learning repository, the setup, model training and inference with 3 workers coordinated by a TTP (Trusted Third Party) is demonstrated. This dataset is used for demo here because: (1) it is a good use case for federated learning as the data is collected from multiple geographically distributed hospitals; and (2) the data is relatively small so that we can see a full cycle of federated learning within a short demo.
( ▶️ Jump to video segment)

7. The Roadmap

See what features for Synergos are planned as part of the journey towards its official launch and beyond.
( ▶️ Jump to video segment)

The Federated Learning Series

The Data Engineering Series

How AI Apprentices Can Successfully Tackle Virtual Interviews

Taking care of a few important points can make a big difference to your virtual interview

These are strange times indeed; many call this the new normal – people attending virtual interviews, receiving offers, being onboarded and hitting the ground running in their new roles, all not having ever met their hiring manager and their colleagues, nor having stepped into their new workplace once (many in AIAP Batch 5 can attest to this!). COVID has upended the process of hiring and how new employees join their companies, one of the most important aspects of an employee’s experience.

While employers explore new ways to engage and attract talent to join them, jobseekers too grapple with new ways in which to stand out from the marketplace and impress the interviewers, and ultimately get that role that they have applied for. As an apprentice planning your return to industry, you have followed well-meaning advice (please read my earlier post if you have not yet done so!): you networked, written an impressive resume and updated your LinkedIn profile, and you have been invited to…a virtual interview. What should you do to convey your know-how, your abilities, and most importantly, your interest in the role and company?

1. Preparing your equipment

I don’t know about you, but IT has a way of not working when you most need it/least expect it to (there is probably research on how Murphy’s Law is 80% more likely to apply when it comes to IT equipment!). Prepare your equipment in advance – make sure your laptop camera is working, you have a working headset with a microphone, and ensure that where you will take that virtual call is an area with a strong WiFi signal. If possible, prepare another laptop or a tablet as a backup. Do not use a smartphone for a virtual interview, unless you also use a tripod or stand – most people hold their phones too close to their face and it conveys an unprofessional “vibe” to the session.


I cannot emphasise how important preparing your equipment is – even if you somehow manage to get on the call when you were not able to initially, it would have affected your focus, your frame of mind, and most of all, the interviewer’s impression of you.

2. Decide where you would take the call

Apart from WiFi signal strength, another aspect you should consider when deciding where you will take the call is the background of where you would be seated. Choose a spot where it conveys your maturity or professionalism – a neat bookshelf filled with books, a wall with some plants behind or even a blank wall painted in a neutral color are good backgrounds. What about virtual backgrounds, you say? If you have to use one (because you live in a shared apartment, or your kids have taken to drawing portraits of you on most of your walls), choose one that is static, and that reflects maturity and professionalism (like a neat bookshelf, for example!).

3. Practice looking into the camera

With virtual interviews, many do not realise that looking at the interviewer’s eyes on the computer screen is not the same as when looking at the interviewer’s eyes in a face-to-face interview – what the interviewer sees is you looking into the computer screen, and not at them. Thus, to ensure you convey interest and to establish a connection with the interviewer, it is important to practice looking into the laptop camera. Should you need to refer to notes (this is an advantage of virtual interviews – the ability to look at notes), place the notes slightly above the laptop camera. That way, you can still maintain “eye-contact” while you refer to what you have prepared. Think of yourself as a newscaster!

4. Be professionally dressed

You may be at home for the interview, but that doesn’t mean being informally dressed (even if you are interviewing with a hip tech startup). At the very least, a plain, neutral colour-ed polo tee for the men, and a blouse for the women. And please, to the men out there, please put on some pants! For the women, putting on some light makeup conveys your professionalism and interest in the role.


Being properly dressed also gives you confidence for the session and puts you in the right frame of mind for success.

5. Research and prepare

It is important to remember why interviews conducted in the first place – most hiring managers want to assess your interest in the role and company, to validate what you said in your resume, and to determine your personality and fit for their team and company. Thus, approach interviews with the mindset of conveying those very information to the hiring manager. Research deeply into the company and find out what is its mission, its growth trajectory, its upcoming projects, and how your role, if offered fits into those aspects. Think about your past experiences, and be prepared to share deeply about how you brought value to your previous companies, and how you these same skills and knowledge you now have, can continue to bring value to their company. Lastly, show a little personality – hiring managers want to also know you as a person, not a well-trained chatbot/AI that gives the perfect answers to questions. Smile, engage in light banter, and share personal anecdotes (nothing salacious or too personal/political) that drive home your professional “story”.

Parting Advice

Virtual interviews have given either a false sense of comfort (“well, I’m behind a screen, what can possibly happen?”) to some, and a false sense of unease (“I’m behind a screen! What do I do?!”) to others, but it doesn’t have to be. As Abraham Lincoln once said “give me six hours to chop down a tree and I will spend the first four sharpening the axe” – the key is to be prepared. If you need help in preparing for such interviews, do remember that I am just a virtual call away!

An Invaluable Learning Experience with Real-world Opportunities

The machine learning (ML) bug bit Pang Hui En when she joined Grab for a six-month stint as a data science intern. “I was using ML for activity recognition and GPS (global positioning system) correction and I was intrigued by the possibility of other applications,” she said. 

But with a degree in Environmental Studies and her previous job as GIS research assistant at the National University of Singapore (NUS), she did not have the confidence to embark on a career in data science.

“I felt I lacked the necessary skillsets as I was not from a technical background like computer science or statistics,” she said. “I also did not have experience with model deployment, nor did I know how to build an end-to-end ML system as I was mostly doing model training in my Grab Internship.”

That would soon change. A senior at the National University of Singapore, a chemical engineer who had made a job switch to become a data scientist, introduced her to the AI Apprenticeship Programme (AIAP)®. “He encouraged me to try for this programme if I was serious about becoming a data scientist,” she said.

AIAP presented her with the opportunity to pick up software engineering and model deployment skills and to build an end-to-end machine learning system.

A diverse cohort

To prepare for the programme, she dug into the AI Apprenticeship Field Guide and worked on Kaggle challenges published by the online data science community.

Still, there was a niggling concern that she lacked the necessary technical competency or exposure to data science.

But she needn’t have worried.

On starting the AIAP, she found that most of the apprentices did not have a computer science degree either. She got to interact with biomedical engineers, economics and business majors, and a whole lot of other people from diverse backgrounds, and this only enriched the learning experience. “Having a diverse background is an advantage because you bring your own domain knowledge to the table and formulate a problem differently.”

The apprentices were also asked about their areas of interest and matched to projects that would enable them to pick up the skills that they wanted to acquire.

Safe environment to learn

Hui En, who is currently in AIAP Batch #6,  joined the AISG team working on Finepose, a project that uses computer vision models in real-world applications, for example, pose estimation models for social-distancing. 

When team members found out that she was keen to pick up software engineering skills, they gave her the opportunity to learn.

One of her first tasks was to refactor a portion of the codebase that previous batches of apprentices had worked on. In order to do this, she had to quickly understand the code, its structure and how it works.

“During my first refactoring, I made several mistakes but thankfully, AIAP is a safe environment to learn,” she said.  

Her mentor gave her useful tips and advice on writing cleaner and more efficient code, and this has helped her to become a more all-rounded data scientist.

An unbeatable experience

Hui En is in charge of model training and the training pipeline, and a challenge for the team is to build accurate and lightweight models for deployment. This is something that she has been looking forward to, and the experience has been an eye-opener for her.

“I used to think that model accuracy was the most important metric or indicator of how good the model was. However, working on Finepose, I realised that there is a ton of resource constraints when deploying a model,” she said. 

“There is always a trade-off between model accuracy and size/inference time. This is important for real-world applications and deployment, and I learnt how to build models that try to achieve a good balance between both.”

No doubt the nine-month AIAP is a rigorous programme, but having the right attitude makes the learning process easier, said Hui En. And the opportunity to apply ML skills on real-world applications is a really valuable experience that online courses cannot provide. Summing up the experience, she said, “It has been an extremely fulfilling journey thus far.”

AIAP® Assessment

AI Singapore’s AI Apprenticeship Programme (AIAP)® was launched in 2018 to groom local Singaporean AI talent and enhance their career with AI-related skills. In the first 2 months, apprentices undergo a self-directed deep-skilling phase that is supported by AISG’s AI engineers, data scientists and computational resources. This is followed by a 7-month on-the-job training where AI apprentices execute real-world projects with the 100 Experiments team or our in-house engineering teams (see below for the different tracks). Since its launch, there have been 10 batches of apprentices who have embarked on this award-winning programme. 

Before becoming an AI apprentice, he/she has to pass our recruitment process. The recruitment process consists of three stages and these stages provide a platform for applicants to showcase their technical competencies, knowledge and ability to solve machine learning challenges. Basic knowledge in statistics, data analytics, machine learning and software engineering are essential to excel in the programme.

There are 3 stages of assessment in the recruitment process, namely, Assessment 1, Assessment 2 and Assessment 3.

Assessment 1 is an online assessment. Applicants are given one attempt to complete 15 multiple choice questions, which assess applicants’ competency in Python, software engineering and machine learning. Applicants are given 40 minutes to complete the assessment and achieve a passing score of 80. We recommended studying the AIAP Field Guide before undertaking the assessment.

Assessment 2 requires applicants to perform exploratory data analysis (part 1) and build an end-to-end machine learning pipeline (part 2) on an unseen dataset. In part 1, applicants are expected to extract the dataset from a database before performing an exploratory data analysis of the dataset (EDA). In the EDA, applicants should develop a good understanding of the dataset by analyzing each feature individually as well as their interactions. Applicants should also form hypotheses about the dataset and verify them during the EDA. A good submission involves presenting these findings in a logical and well-thought-out process with insights.

In part 2, multiple machine learning models have to be developed and evaluated based on the given task. In the development of these models, applicants are required to document their design decisions in data pre-processing and reasoning to support their choice of models. In addition, models should be evaluated in a meaningful manner with an evaluation metric that is appropriate for the task at hand. When tackling a problem using different approaches, applicants may have to be creative to find a common evaluation metric to ensure consistency across comparisons.

As part of the submission process, applicants will be required to package their work for review. Submissions should include an iPython notebook (.ipynb file format), Python (.py file format) and bash (.sh file format) scripts. Other files required to recreate the development environment should also be included. Reproducibility is an extremely important tenet in machine learning and the ability to reproduce an applicant’s results is a key assessment point. Submissions should also demonstrate an understanding of software engineering fundamentals and all code submitted should incorporate proper coding conventions while being readable and easily understood.

We do expect applicants to demonstrate a reasonable understanding of key concepts in statistics, data analysis and machine learning. While heuristics, checklists and flow charts for standard practices may be useful, AIAP applicants are expected to understand the rationale behind these tools and be able to decide (and justify) how to apply these ideas in new situations.

As the development of machine learning models is an iterative process with a considerable amount of experimentation, it is recommended that applicants take a principled approach to experimentation and appropriately justify any design considerations.

Lastly, Assessment 3 consists of a technical interview and a group case study exercise. At the start of the technical interview, applicants are to present their submissions. While the content of the presentation is free-form, we recommend applicants make the best use of their time by highlighting areas of their work that are critical and distinctive.  

In the group case study exercise, applicants are allowed to work with other like-minded individuals on an undisclosed case study. The team is given time to formulate, document and present their solution. Besides reaffirming the applicants’ technical competencies, the third stage of the recruitment process is also designed to identify applicants who possessed the ability to problem-solve and demonstrate himself/herself as team players.

We are heartened to see the improvement in the quality of applicants in each batch’s application and we hope that the pointers above will be helpful to current and prospective applicants. We would like to wish all applicants the very best of luck in their assessment and we look forward to welcoming you as AI apprentices.

Here are some projects past apprentices have worked on:

PMET Transitions: Passion, Pain and Plan

As the year kicked off, I had a chat with my colleague Kevin. Both of us are PMETs (Professionals, Managers, Executives and Technicians) who have made transitions in our careers. Hopefully, what we share can help others walking similar journeys.

Below is a transcript of the conversation [*].

Basil : Hi, Kevin. Hope you’re off to a great start in the new year.

Kevin : Hello, Basil. How are you? I’m good, thank you.

Basil : Yeah, couldn’t be better. So we will not be talking about AI today. Well, at least that won’t be the central focus of our conversation. I think we can agree that 2020 was a year of transitions. So, we thought that it would be good to share with listeners our own experiences in transitioning to new roles as mature PMETs, which I think is an equally interesting topic. Now, Kevin you have appeared in the media before. Minister for Communications and Information Iswaran mentioned you as somebody who made a successful transition into a digital career. You also did an interview with Channel 8 pre-COVID. So, maybe listeners out there already know a little bit about you. Could you tell us more about the earlier part of your career?

Kevin : Yeah, sure, Basil. I spent over twenty five years in the high tech industry. I was trained as an electronic and computer engineer during my school days, but my career has been mostly non-technical in nature. My first job was with Motorola working as a product engineer in the factory. That was the only technical work I did, but very quickly I realised manufacturing didn’t suit me. So therefore, in less than a year, I decided to switch to sales and marketing. I started in product marketing and product management for the semiconductor and IT industry. Later I move into sales and general management. I was fortunate to be at the start of the high tech boom back in the early 1990s. I worked for Intel at that time. I witnessed how fast the industry grew as computing started to permeate every aspect of the society. I saw firsthand how the IT technologies came in to disrupt many other industries, how businesses were conducted and how we lived our lives back then fundamentally changed. In 2000s, I also witnessed the same phenomena in two other technology disruptions. The Internet commerce era and the mobile revolutions. These two disruptions again brought in big changes to our lives. But at the same time, I felt that they also created lots of opportunities that didn’t exist before. So now, I think I’m witnessing the fourth technological disruption in my career which is AI. And that’s why I’m so excited to be part of this industry now.

Basil : Very interesting journey. For me, my own journey is a little bit more mundane. I spent seventeen years just in one single industry which was semiconductor chip design. Although my role required me to constantly learn new things as an engineer, I still felt a sense of stagnation and even of being pigeonholed. After about fifteen years on my job, I just felt that the value add was incremental and it wasn’t really opening up new doors for me. So, I was ready to try something new. Then Google open sourced TensorFlow back in late 2015 and it piqued my interest. Now, anybody could do deep learning using neural networks. Those were stuff that I heard about but was a total mystery to me at that time. So, I read up more on it and sensing the direction the world was moving… I just felt that with ever greater computing power and storage, more and more data would be generated and then naturally algorithms would come into play to return value for those who know where to employ them. And just like you, I felt that we were on the cusp of something significant. And, of course, around that time (March 2016), AlphaGo captured the world’s imagination by beating the best human players [1] … at this point I would like to mention our colleague Azmi who recently also shared about his transition to become an AI engineer. Listeners please refer to the link which I will put up on the page [2]. He talks about the importance of being aware of trends beyond one’s immediate profession and AI is definitely a part of that trend.

Kevin : Yeah, that is interesting. I mean, for me, AI has always been an interesting technology since thirty, forty years ago. But it never occurred to me back then to enter this industry. I only realised just a few years ago, after a long career, I got bored with corporate life, so I ventured into doing trading and investment full time. It was at that time that I came across AI and I was curious about the possibility of using AI to help me out in my investment. So I decided to, kind of like, take a peek under the hood. At that time, I didn’t have any coding experience other than the programming courses that I took in university, about thirty years ago. So, I had to start by taking some free online courses in python programming from online sites like Coursera and even from YouTube. It was okay. It was not too difficult, I felt. It took me a couple of months to get to a level where I could do some useful codes. Then I realised to learn AI, math became a blocker. I remember I started with Andrew Ng’s machine learning course on Coursera, but by Lecture #3, I had to give up because all the math was just beyond me. And so I took a step back to learn math. I had a engineering degree so I thought I could start from university math. I did some online courses again, but soon I realised I couldn’t even do that anymore. I had totally forgotten things like calculus, linear algebra… stuff that I learned back in high school. So no choice, I had to go back all the way to high school math. I had to start from ground zero again and I did that on Khan Academy. After re-learning math, then I proceeded to take several machine learning nanodegree courses on Udacity. Just like you, I was mesmerised by AlphaGo at that time. I actually followed the competition ‘live’. I also took two courses specialising in deep reinforcement learning, just to try to understand how AlphaGo really worked.

The interesting thing is that some of my friends actually asked me why I was kind of like “torturing” myself. How could you learn AI at your age?… and my answer to them really is passion. When a person is passionate about something, doing that something is no longer a torture. It actually becomes an enjoyment. Yes, you will need hard work, lots of it, actually, because no success is possible without going through a long journey filled with difficulties and hard work. But hard work doesn’t really need to be hardship. It can be painful at times, but it need not be suffering. Just like top performing athletes, they have to endure long periods of hard work, but they manage to persevere, because they have the passion to get them out of their low periods. If not for passion, most people would just simply give up. So I think I experienced that first hand, when I had to go back to re-learn my high school math, that could have been something unthinkable. I could have easily given up if there wasn’t an end goal which I was totally passionate about. That’s why I think passion is really, really critical. Another example is that I also experienced that when I started my own company. A bit of history – besides my corporate career, I also founded two start-ups in China. I spent quite a few years running that operations up in China. So during those days, doing a start-up was really difficult. Without passion, that wouldn’t be possible. Therefore, through that experience I’m convinced that having strong passion for what you do is a critical success factor in life. Because with passion you will end up spending more time than others. You will be able to do that and endure the pain that comes along and, because of that, increase your chances of success, because you are able to stay on it longer. So I think that’s one of the biggest learnings in life for me. I have also had periods of my career when I was stuck in doing things I wasn’t passionate about. I guess many people also have the same experience as well doing jobs that are neither good nor bad. They are just so-so, just another job. I mean, with hindsight I would definitely have moved on to other things sooner. Life, I think, is too short to get stuck in things that we’re not passionate about.

Basil : Yes, definitely agree. Yeah, I really think we cannot over-emphasized the importance of passion, even if it might come across as a sort of a motherhood statement, because… like going to work, if it’s just a push factor, it won’t be sustainable in the long term. And what you mentioned, I think there’s an equation, I don’t remembered the source, which describes what you just mentioned. It goes something like Despair = Hardship – Meaning [3]. So, if there is a big dose of meaning, then whatever hardship there is, and I think going out of one’s comfort zone certainly is one of them, that despair might even turn into joy. But I suppose for many people, actually knowing what they are really passionate about is surprisingly difficult.

Kevin : Yeah, definitely. I think you’re right. It’s not realistic to always expect to be able to start our career with a job that we are passionate about. In reality, it needs planning. Actually, very long term planning and transition. Most people are probably like me. We don’t really know what we like early in our career, it will take time to explore. But make sure that we keep an open mind and also keep an eye on the goal of finding our passion. We just have to keep exploring. Because in most cases, people easily settle into a job that they neither like nor dislike. Over time, we will then habituate into it, and that will become a comfort zone. And once it becomes a comfort zone, we’ll be reluctant to get out of it. That’s human nature. We all tend to just complain about it, but we don’t do anything about it either. Therefore, I think in order not to get stuck that way, it is important that we continuously find out about ourselves. Over time, I spent a lot of time learning about myself. What I like and dislike. That requires a lot of intellectual honesty and willingness to explore. We can explore, for example, by volunteering, by engaging in different communities, by experiencing things outside our work. It will take time, lots of time. Usually, it will take years and not months to find out what we are truly passionate about, and that’s why it is important that we start early. But, it’s not too late at any point in your career to start exploring, although the risks tend to be higher at a later stage of your career. Once we find out what we like, then we need to plan, because most of the time we find out something that we like, we are not in the exact path to do that. Therefore, we need to plan to acquire the skills, experience the network so that we can transition to the desired path. For example, in the software related fields, that would usually mean taking classes to learn new skills, or volunteering to work on some projects to build up the experience to have a good GitHub to show to people and stuff like that. There’s a concept called Plan A, Plan B. That means you need to have both. Plan A is the plan to continuously build up the competence in your current job, whatever path you’re working on. You shouldn’t just dropped that and do something else. So you still need a Plan A. But your Plan B is something on the side that you need to build up to get yourself ready to pivot into a different path in the future, that will bring you through your passion. So, yes, back to the question – smooth transitioning is needed, but it takes time and it takes a lot of planning as well.

Basil : Yeah, I certainly felt what you described very eloquently. It took me, like about two to three years, personally, to figure out what I should do. I joined various communities, did some little side projects before I actually took the necessary action.

Kevin : Yeah, I’ve experienced it myself, as well as seen friends and colleagues my age. I think, one of the biggest barriers in my opinion is really our inertia to change. We tend to want to hide inside our comfort zone. Things inside our comfort zone feel familiar. They are what brought us to who we are today. So, venturing outside our comfort zone is pretty scary and instinctively we are reluctant to do that. We tend to give ourselves excuses of why we shouldn’t get out of our comfort zone, and then we procrastinate. We always think, maybe one day I’ll do something, but I’ll not do it. So, I don’t know about you, Basil, but I used to set annual resolutions like now in the beginning of the year. I said I want to do something for this year, right? Often, you feel passionate about it, set some goals, big goals, with some detailed targets and action plans. It could be like learning something, or just simply doing exercise. Usually, I would start by following my plan with a lot of enthusiasm. But, by the second month or so, the momentum would start to fizzle, and by the third or fourth month, I would give myself a convenient excuse why I couldn’t do that anymore.

That happened to me a lot in a past, until I stumbled upon a concept which was very, very useful – the concept of mini-habits. That really made a huge difference for me. The concept is really very simple. Instead of planning to do something that requires a strong commitment of time and energy which we know we won’t be able to follow through consistently, we start by committing something so tiny that it is ridiculous not to follow. Let me give you an example. If you want to learn to play, say the piano. If you set a goal to practise an hour a day, you might be able to follow that religiously in the first two weeks. After that, it becomes a pain. And then things cropped up, you start to give yourself excuses why it’s okay to skip a day of practice. That one day will sometimes become two to three days of excuse soon, and then you’ll be practising once a week instead of once a day. And then that once a week becomes once every two weeks and, finally, not at all. So, that kinda like fizzles out. Now, a mini-habit means that we don’t commit ourselves to something that we cannot follow through, but instead, let’s say we commit to practising only ten minutes a day. Ten minutes is such a short period that there isn’t really any good reason not to follow through and it’s not painful to practise for ten minutes. If it is, then you cut it down to five minutes. Even when you are really very busy that day, it is still no reason why you can’t just sit down and practise for ten minutes. On days that you’re not as busy, then you would practise longer. Sometimes you are in the mood, you may even practice one or two hours. So, the key point is consistency. No matter how busy, rain or shine, you would do that ten minutes of practice. And that becomes a habit over a time, practising no longer become as painful, as long as you are able to put in that consistency day after day after day after day. After that, you can increase the commitment from ten minutes to half an hour a day, and that’s how habits get developed. I tried that on many things and it really worked. Like for now, I want to continuously acquire new AI knowledge because the field is changing so fast. So, I need to do that by reading research papers, articles, practise coding, or take some other courses to improve my AI skills. I started with ten minutes a day of doing one of those activities and now I’m already up to an hour a day. And most of the days, to be honest, I’m actually doing much more than an hour, so consistently every day, even on weekends. That is no longer a pain to me, but rather an enjoyment. As the saying goes, the key to success is really continuous and never-ending improvements.

Basil : I think what you’ve just described is what somebody calls the one percent improvement rule where small, seemingly insignificant improvement stack up over time. For me, I like to learn languages. But one does not simply acquire a new language overnight. It takes constant exposure and practice to achieve competency a little bit at a time. So it’s like every day I would think of a random sentence and ask myself, how would I say this in Japanese? In German? If I am not able to do that, I go and find out, whether by asking in a forum [example] or I just search the Internet. It just takes about five, ten minutes every day. This is an approach applicable to everybody, no matter what field you are in or going to get into. This constant, continuous and never-ending improvement is an attitude that everybody should adopt, I think. I find it interesting that even as we come to grips with a world where machines can learn stuff, we humans also need to up our game in learning, perhaps in ways that machines cannot achieve in the foreseeable future. Now is not the time to be a Luddite. What do you think about AI and learning?

Kevin : Yeah, I think there’s been a lot of discussion about AI taking over the human’s job in the media. In my opinion, it is not really about AI taking over a human’s job per se. Instead, it is about humans who know how to leverage on the power of AI, who will take over those who don’t. So, AI is just a tool. It depends on whether you know how to make use of the tool or not. It is very much like thirty years ago, people who learned how to use computers replaced those who didn’t. In more recent history, I think companies that learned how to leverage on the power of e-commerce decimated the businesses that didn’t. That’s a trend and likewise, what we will see in the future is people and companies who learn how to make use of AI productively will replace those who don’t. I’m coming to an important point. In the past, we learned a professional skill in our twenties and we could safely expect that professional skill to serve us well until our retirement. But, that is no longer true. Today, no matter how good our skills we start with, we can expect them to be obsolete very soon, not once, but multiple times in our lifetime. So, if we cannot get out of our comfort zones and we cannot learn new skills, then we will run a risk that we will be replaced, we will be wiped out by others who do learn those new skills during our mid-careers. Lifelong learning to me is no longer just a cliché. It has actually become a survival skill. It is no longer a nice-to-have. To succeed, I think we will have to reset our careers, learn new skills and pivot to totally new paths, at least once, if not multiple times. When we are in our twenties, we truly have no fear. We absorb new knowledge, like a sponge. But, as we grow older and we become more successful in our careers, we somehow tend to stop doing that. Slowly, we habituate into our own comfort zones. We become reluctant to change, and we become reluctant to take risks. But, as I realize, over the past few years, age actually is just an excuse for hiding in our comfort zone. A lot of people say that when you grow older, it is much harder to learn new skills. Actually, I think it is not a matter of ability, but rather a matter of mindsets. In today’s world, everything is learnable. Actually, they’re figure-out-able, as long as we put our hearts to it. There are plenty of resources out there to help us succeed. So the question to me really is, are we willing to do so or are we being held hostage by our own mindset?

Basil : Talking about the teaming up of humans and machines, I think not only should we not fear AI, we should actually embrace it, to increase our own capabilities. Now, I’m on Facebook and YouTube a lot, and that’s where I get a lot of my new ideas and become aware of new trends and opportunities, of being connected to a larger whole. But yet not every part of that whole is relevant to me and that’s where AI-powered recommendations come in. I do find such recommendations very useful, and they have helped me on my journey so here’s one way where we can actually harness the power of AI to improve your own self.

Alright, Kevin, you are leading the AI Advisory Team in AI Singapore. What’s the mission of the team, could you tell us?

Kevin : Yeah, sure. Many companies are interested in adopting AI, but they don’t know how. So, the AI Advisory Team in AI Singapore was set up to help these companies along their AI adoption journey. Companies have different readiness. Some of them are interested in AI, but totally have no idea at all of how to get started. Some others have specific ideas, but they are not sure whether those ideas are real or mature, do they have the data, do they have the right work processes, or do they have the talents to execute those projects? Therefore, we develop different programmes to help them. For example, for the total beginners, we run AI Clinics, which are workshops that are aimed to show companies some successful AI use cases in their specific industries to enable them to start thinking about what they might want to do for their companies [4]. And then for those who already have some ideas and want our help to explore their specific use cases, we have the AI Discovery Programme through which we will provide consultancy services to help them explore and scope specific AI projects. So you can see that we kind of hand-hold people, nudge people along the way so that they will end up at a stage where they are ready to do their first AI projects for their companies.

Basil : So, helping companies transform their businesses with AI. That’s a story for another day, and I think we have plans to talk about it very soon in another podcast. Right, Kevin?

Kevin : Yeah, sure, Basil. I would love to do that.

Basil : So, listeners, for today, I hope that you have some useful takeaway points from our conversation. Everyone walks a different path, but I’m sure that there will be things that we can learn from listening to others. So, join me and Kevin next time as we explore how companies can transform their businesses with AI. Until then, stay safe and, bye bye.


[1] If you haven’t, do watch this great piece of storytelling of a historic moment in 2016: AlphaGo – The Movie | Full Documentary
[2] Azmi’s transition story – The AI Engineer: An Unexpected Journey
[3] I traced the equation Despair = Hardship – Meaning to Chip Conley
[4] Read more about AI Clinics here

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

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