Machine Learning for Business

Applying Machine Learning is like Cooking. Here’s Why.

The story of a tech noob often starts like this: if I’m not familiar with how XYZ is built, I shouldn’t be using it. 

This is often the case as far as machine learning is concerned. However, if we regress a little and apply the same pattern to the case of using a microwave oven, it doesn’t seem to make much sense —

If I’m not familiar with how a microwave oven is built, I shouldn’t be using it.

Many of us are not engineers by trade, yet most of us have used a microwave oven before, and this is exactly what was proposed by Cassie Kozyrkov, Google’s Chief Decision Scientist — a machine learning model is like a microwave oven. It doesn’t take an engineer to know how to use it.

Cassie Kozyrkov speaking at TNW Conference, 2019.

Build that Winning Recipe, Chef. Not the Microwave.

In her short presentation at the TNW Conference, Kozyrkov explained: “machine learning is a general-purpose tool that helps other people solve their business problems”, much like how a microwave oven is built to make food edible. What business executives should do is not to train staff on how to build a “microwave”, but to focus on forming a winning business strategy and consider what tools would help achieve that strategic vision. In other words, in order to make Michelin gourmet, it’s more important for the chef to create that jaw-dropping recipe than to understand how an industry-grade oven is built. We can leave that to a professional engineer.

The Machine Learning Attitude – Test, test, test.

Thanks to the engineers, you don’t need to know how to build the tools from scratch. However, there’re some important aspects to think about when applying machine learning to your businesses. 

In another quick machine learning talk hosted by Google, Kozyrkov demonstrated that not all machine learning models are created equal. Some models may perform better than the others for certain purposes. Let’s take an example from the world of cooking again – it’s like rice cooker versus saucepan for cooking rice. A saucepan can do the job but it’s not quite the same. So, how do you know which machine model is good for your business? 

Repeat after me — test, test, TEST

“Don’t be afraid to fail and make mistakes. Before attaining the state of perfection, a machine learning model would have to fail many times in order to know what to improve. Failure is a necessary step. Test as many times as you can.” Kozyrkov advised that this “machine-learning attitude” is something important to have when integrating the tool as part of your business strategy. If you’re too scared to make mistakes, it’s almost certain that you’ll never succeed. 

Feature Engineering – Convert Domain Knowledge into Good Solutions

Finally, it’s all about data. Data is like that quality ingredient you’re about to put into a Michelin dish. The skill of a chef is crucial, but no chef can make magic without good ingredients

In the terminology of machine learning, the process of picking the right data for your model is called Feature Engineering. Does it take an engineer to build the right features? No, but it does take a domain expert to decide what sort of data should be gathered and how to weigh their respective importance when looking for the right solution for a business problem. Again, that’s similar to building a recipe. This applies to every industry. From our very own experience as an educator, it takes not only data scientists to find out the right algorithm for providing the best learning experience for our programming students, but also the domain knowledge from education experts, researchers, and scholars.

In short, it’s not difficult to apply machine learning to your business as long as it fits into your business strategy. The key is to try until you find the right approach.


Alright cheffies, whenever you’re ready – it’s time to cook up a storm! 

A.I. Intell takes over jobs in financial world, Insider…

Even lawyers could be replaced by A.I.

Before we begin, here’s a question for you: what kind of company is J.P. Morgan? What industry does it operate in?

It seems like a no-brainer — J.P. Morgan is an iBank, also known as the largest bank in America. Except that it’s not just an iBank. We are with someone who has told us a scoop — the financial company is changing fast, and it’s reflected in its hiring strategy in 2019.

So, what kind of company J.P. Morgan really is?

Mr. Park Pu is the parent of one of our coding students, and a top executive in J.P. Morgan China. His daughter is just old enough to join the Scratch coding classes in Preface this year. However, back in the early days of Preface, the coding school was already on his radar.

“I noticed you guys back when you had a smaller office on Lau Li Street. It’s nice to see you grow into a bigger space. I think it also means that more parents are seeing the importance of coding education gradually.”

He brought Ashley, now 7 years old, to Preface for her Scratch classes around the same time he witnessed a rapid change in the hiring strategy of J.P. Morgan.

“J.P. Morgan spends more than US$10 billion in technology every year. At the moment we hire more programmers than Microsoft yearly. This will continue to be the hiring trend in the entire industry.”

Earlier this year, J.P. Morgan announced the launch of JPM Coin, their very own cryptocurrency. The digital currency is not money per se, yet it allows J.P. Morgan’s clients to settle transactions between their accounts with the digital currency. The product is currently in trial.

Launching the service means a big deal — the corporate finance world was notorious for its reluctance in adopting new tech too fast too soon. The fact that J.P. Morgan has swiftly joined the bandwagon of cryptocurrency means that the financial industry is not only ready to change the way it operates, but also how the entire world perceives new tech in finance.

Pu revealed that JPM Coin is not the first technological project J.P. Morgan piloted. “J.P. Morgan helped build the mobile payment systems that everyone now uses across the U.S.. Without the involvement of a financial institution of such scale, it wouldn’t be possible to make any digital payment system available at a country level. As much as it is a financial company, J.P. Morgan also makes an investment in its tech department bigger than any outsider can imagine.”

The exponential increase in the demand for technical personnel inevitably means the career of some might be jeopardised, even for those who are in the prestigious roles.

“J.P. Morgan has recently introduced an A.I. system to review all the standard legal documents produced internally. In a short length of time, the system is able to complete the amount of work that would take considerably longer by human. A huge part of the legal team is now replaced by automation. We expect that continual development in technology will form similar trends for other functionalities, especially the ones that involve a lot of repetitive manual work.”

J.P. Morgan is not the only one who is having this transformation. Goldman Sachs went through a similar change years ago — the CEO even made a bold statement that “Goldman Sach is a tech company” because the company hires more engineers than Facebook in absolute terms. It’s perhaps far-fetched to say Goldman Sachs is a 100% tech-maker, yet the boundary is getting blurry. The financial industry is indeed one of the biggest driving forces behind technological advancement.

It is crucial to keep learning new skills, and the most important skills to learn is how to reinvent oneself whenever needed.

From an industry research done by Goldman Sachs Research, it is suggested that as automation plays an increasingly prominent role in a workplace, there will be a bigger need for the workforce to reskill and upskill throughout the lifespan of one’s career. It is estimated that about one-third of the workforce will need to transfer to a new occupation in the next 5 years. It means that the marketers of today would be the programmers of tomorrow; the programmers of today may become the new data scientists in a few years. In other words, it is crucial to keep learning new skills, and the most important skills to learn is how to reinvent oneself whenever and wherever you are.