Black Box thinking-learn hard things-
Suppose you want to know how a function, method, or logic work without diving deep into details, which means abstracting the small things that compose what you want to learn (generally this thing is hard to learn in a short time) and looking at the big picture.
Definition
In science studies, the social process of black boxing is based on the abstract notion of a black box. To cite Bruno Latour, black boxing is “the way scientific and technical work is made invisible by its own success. When a machine runs efficiently, when a matter of fact is settled, one needs to focus only on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become.
Blackboxing. (2022, October 10). In Wikipedia. https://en.wikipedia.org/wiki/Blackboxing
Let’s understand by an example-1
You are a beginner programmer looking for a method to sort list items in a specific order (ascending or descending) in python.
The famous method that comes to mind is the sort method, let’s discover what it does, using normal strategy vs black-boxing.
The normal strategy (understanding how the sorting algorithm works and then consuming the already implemented sort method or defining your own method/function)
pros:
- more comprehension
- increases your problem-solving skills
- understanding how things work deeply instead of just consuming
cons:
- time-consuming
- a little bit scary for beginners to learn complex things
- hard to explain (especially for non-programmers people)
The black-box strategy
In this strategy of thinking we will look at the big picture rather than explain how the algorithm/logic works in detail, we’ll focus on the input and output.
It’s easy to explain for beginners and even non-programmers and thus gaining time & effort.
# define your list of numbers
lst = [100,-7,0,-10,1000]
# sort list & output results
lst.sort()
print(lst)output
[-10, -7, 0, 100, 1000]explain!
Understood, right? (let me know in the comments section!)
pros:
- Time saver
- Explain complex concepts easily
- Make learning less scary.
- As a data scientist, Machine learning engineer, you can easily talk the same language as business persons hence explain your findings in a more simple and understood way
Let’s understand by an example-2
The Machine learning field is full of algorithms and complex visualizations, functions & statistics that need to be mastered in order for you to perform well in this domain but, how can we explain machine learning as a concept to a non-data background person?
The normal strategy (diving deep into the technical concepts of machine learning as well as trying to explain how each ML algorithm works in addition to statistics and visualizations, leading the other person to lost and making this scary!)
It’s the same thing as showing you the below image(suppose you came from a non-data background) & waiting for you to understand machine learning
The black-box strategy
In this strategy of thinking we will look at the big picture rather than explain how the ML algorithms work in detail, we’ll focus on the input and output.
It’s easy to explain for beginners and even non-programmers and thus gaining time & effort.
So here I’ll simply say that Machine learning is a subset of Artificial intelligence and it’s based upon data to learn patterns and discover tendencies in order to come up with a mathematical(or not) modelization of the given data after that we’ll feed him with new data to come up with new results.
The above image shows us how a machine-learning algorithm for fraud detection can help us decide whether a given user is a fraudster or not,
as you can notice, we feed the data of that suspect user to our ML algorithm, and with the help of its learning (based upon training data) he can help us decide whether it’s a fraudster or not.
Another case:
Suppose you want to communicate your findings to stakeholders during a data science project, it may sound difficult to talk the same language together so opting for black boxing will help you!
conclusion
Black boxing allows you to understand logic, concepts, and lessons without knowing how they were implemented or how to code them(case of algorithms).
It focuses on understanding the overall goal instead of getting lost in details.
That’s all for this article, I’ll let you discover more while working with this tool, and let me know if you have any remarks or addition.
Thanks for reading.
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