Problem-Solving And Design Thinking In Machine Learning And Artificial Intelligence

Problems are an integral part of any human activity and cannot be avoided because we work in a dynamic and unpredictable environment. Therefore, it is helpful to adopt a systematic approach when solving problems. The first step is to ask the right questions about existing problems and find practical ways to solve the problem before validating whether machine learning can solve that problem in a unique way that has not been discovered through other means.

In this article, we will discuss

1. Design thinking
2. Stages of design thinking
3. Machine learning
4. Stages of machine learning and how you can apply design thinking to solve machine learning problems

Design thinking is an intersection of what is desirable from a human point of view and what is logically feasible and economically viable. The design thinking process focuses on studying the user's needs and creating solutions to develop better products and services. This process encourages innovative thinking and creative problem solving, which leads to better impacts on users and organizations.

STAGES OF DESIGN THINKING

Design thinking is a five-stage process, which are:

• Empathize,
• Define,
• Ideate,
• Prototype,
• Test.

Let's take a look at these stages one after the other.

EMPATHY

This stage requires a deep understanding of the problems and realities of the users. It involves learning about difficulties people face and uncovering their latent needs and desires to explain their behaviors. We need to understand the people's environment and their roles and interactions with their environment to do so.

DEFINE

This stage requires a clear idea of the problem to solve, which is then turned into an actionable problem statement.

IDEATE

This stage is a crucial transitional stage from learning about the users and the problem to brainstorming to develop the solutions. This stage is where innovation thrives. It provides a solution that users have been missing.

PROTOTYPE

This stage involves the study of users' interactions with the product to see what works and where users are facing problems and identify what additional designs you can add to enhance the user experience.

TESTING

In this stage, you monitor the usage of the product or service to determine the effectiveness of the solution. This stage also provides a continuous product improvement loop where you can quickly use feedback to improve the user's experience. Lastly, observation during this stage will likely uncover needs those users had never before articulated.

We have learned what design thinking is including the different stages. Let us now understand machine learning and how design thinking can solve machine learning problems.

What is machine learning?

Machine learning is a technology that automates analytical model building. It is a branch of artificial intelligence based on the idea that computer algorithms can learn from data, identify patterns and make decisions with minimal human intervention.

Solving machine learning problems does not follow predictable rules and behavior, which results in the need to create human-centric solutions while solving such problems. Machine learning projects need good ethical design and reliable data sources. One advantage of design thinking in solving machine learning problems is that it reduces the risk of failure.

Implementing design thinking in a machine learning project makes visible and human-centric improvements to the design and implementation of the project (such as faster and better decision-making). In addition, it helps get a clear vision of the target group's problems and reduces the risk of spoiling the whole project.

STAGES OF MACHINE LEARNING PROJECTS WITH DESIGN THINKING:

Just as in design thinking, there are five-stage processes, so it is in its application to machine learning problems, and there are:

• Empathize/Analyse,
• Define/Synthesize,
• Ideate,
• Tuning,
• Validate.

Now let’s look at them one after another.

EMPATHIZE/ANALYSE

This stage captures the user's critical decision and finds variables and metrics that might be useful for making better predictions. It is good to inspect data to identify issues and gain insights into the data because the model's predictive power will only be as good as the data it’s fed.

DEFINE/SYNTHESIZE

This stage is where we draw insights from the data. First, we explore the data gathered and prepare the data to help define the problem using the insights drawn from available data, as data is fundamental to machine learning

IDEATE

Here explains the impact machine learning has on a business, users, and stakeholders— and the importance of searching for the best model that solves the problem without compromising any of the stakeholders. Including domain knowledge experts will make the building of the machine learning model more robust, reliable, and human-centric.

TUNING

At this stage, you want to make sure the machine learning performs optimally based on the performance measures and avoid overfitting the model.

VALIDATE In this stage, we use the model to make predictions, validate its performance, and make sure it is ready to be deployed to users. Additionally, we can also spot bugs with the model by testing its quality and the input data, providing an opportunity to improve the data quality and quantity. This step is also the start of the continuous learning and improvement process for the model for an optimal user experience.

CONCLUSION

Using design thinking for machine learning provides a framework and simplifies the process. The design thinking outlook helps integrate the human-centered way of solving problems in machine learning and highlighting a repetitive pattern of building algorithms.

Business leaders, Data Scientists, and Machine Learning Engineers need to understand human-centered design thinking as that is the best approach to solving complex problems with Machine Learning.