Scientific Learning

In my previous post, I wrote about Lean Startup. And I found that it is very interesting that I have come through these things.

  • S.M.A.R.T (specific, measurable, actionable, realistic, time-bound) (post)
  • Machine Learning – Deep Learning – Convolutional Neural Network (CNN)
  • Statistics
  • Kaizen (改善法) (post)
  • Agile development. Test development development (TDD)
  • Research study

When I read The Lean Startup (Ries, 2011), I learn about lean manufacturing. Then only I discovered that kaizen is also part of the lean manufacturing. Wow! I realise that, all of these things are related. And the core of the concept is, “scientific learning”.

In Lean Startup, in my opinion, the fundamental concept is Build-Measure-Learn loop. According to Ries (2011), the “Entrepreneurship is a management, learning, and involves experiments”. Throughout the whole entrepreneurship, it is about the learning. It is a scientific learning, because entrepreneurs need to form hypotheses and then test the hypotheses with experiments. Furthermore, the method can be replicated,  The results can be measured and validated. It is a validated learning. The main purpose of Lean Startup is to make the Build-Measure-Learn loop faster, then for each iteration, the product will be optimized and improved through the learning.

This is exactly how Machine Learning works. In Machine Learning, the core concept is the gradient descent. For each calculation of output (≈ build), the machine get the errors (difference of actual output and the desired output) (≈ measure). Then using the errors, machine will update the weight so that it will produce the actual output that is closer to the desired output (≈ learn). It involves small batches and many iterations throughout the training.

Agile development shares many characteristics with Lean Startup. It involves iterative development and daily stand-up meetings (scrum) which improves the communication. Besides that, TDD is also part of the agile development. Why is TDD important? I have wrote about it previously. But after reading The Lean Startup, I found that TDD is more than just making the product stable. As we use TDD, we can test the features or functions with command-line. It is extremely faster than testing the functionality by running the application from beginning to the end manually. Imagine that you want to test a webapp functionality, you have to open the web browser, login, click-and-click-and-click, refresh-refresh-and-refresh. If using TDD, as lean manufacturing, this can minimize the waste drastically. Similarly, you write the code (≈ build), you test it (≈ measure), and you make changes to optimize your code (≈ learn).

Unluckily, when I was doing PhD research study, I didn’t know about Build-Measure-Learn. Research study also shares a characteristic of entrepreneurship, i.e. uncertainty. However, getting supervisors’ feedback is not an easy job, as most of the time our supervisors are hardly to give instant response. As a result, unlike entrepreneurship, research study has difficulty to accelerate the Build-Measure-Learn loop. And most of the time, research study requires enormous prior knowledge of the research area.  Without fulfilling this requirement, supervisors’ feedback doesn’t help much at all.

When I was a lecturer, my institution adopts OBE (Outcome-Based Education). It shares the characteristics as Lean Startup and S.M.A.R.T, as the outcomes have to be measurable and observable. However, I personally don’t agree the ways of implementation of OBE, because it makes the teaching become very rigid, especially setting up the exam papers have to fulfil the criteria strictly. I agree that the outcome should be measurable, yet the ways of implementation is exhaustive and not really effective. It is muda and muri. But I believe that, if students are able to implement Build-Measure-Learn loop in learning, the learning will become effective. For example, “build” the knowledge through lecture class, group discussion, and other activities. “Measure” their gained knowledge through various tests. “Learn” from the results of the tests. And, repeat. Each iteration should be small. By each iteration, the students should able to learn through the failures they made and optimize their solution to get the desired result. (Nevertheless, I believe that this implementation is impractical, due to the cultural pressure and stereotype that the students results should be a bell-curve. W*F!)

As a conclusion, by using the scientific learning method like Lean Startup, kaizen, or even Machine Learning in our daily life (small batches, iterative, and measurable), surely this will make our life better.

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