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What Designers Must Know About Machine Learning

Designing is a creative pursuit. It is a work of art where hues and patterns come together to create a visual that will speak volumes without mentioning a single word of text. 

What does a creative pursuit like that have to do with Machine Learning? Should designers be aware of machine learning and its infinite possibilities?

Turns out, yes. Machine learning is making sweeping changes to every business function as we know it. Art, of which design is a subset, is no alien to it. We already have ML tools amidst us that can turn rough sketches into well-rounded illustrations. This makes it necessary for developers to understand machine learning and its intricacies. It will make them better designers who can create visuals that will not only enthrall users but will also make the visuals easy to understand for machine learning systems.

Let’s begin with understanding what machine learning is, and how it works. 

What is Machine Learning?

In 1959, Arthur Samuel defined machine learning (ML) as the “Field of study that gives computers the ability to learn without being explicitly programmed”. We can safely assume that the pursuit of making computers think on their own has been around since the 1950s. 

However, it is only recently with the data explosion that ML has become viable and possible. ML needs vast amounts of data to perform computations. Without data, an ML system cannot analyze and come to worthy conclusions and forecasts. It uses a variety of computational systems that can make ordinary computer programs perform extraordinary feats, like creating a piece of art. 

Machine learning is not a single technology, in fact, it is a subset of Artificial Intelligence, the bigger cognitive computing technology. Also, ML performs in several ways based on the learning it is programmed for. Let’s take a closer look at the types of machine learning types.

Types of ML

Broadly, there are three types of machine learning:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Supervised learning

Broadly, there are three types of machine learning:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Supervised learning

A learning system where the ML algorithm is programmed to perform specific activities like classification or regression on a labeled data set. This is a rule-based approach to machine learning which makes the algorithm entirely dependent on labeled data and also the rules it is programmed for. It cannot do tasks that go beyond the rules or have unlabeled data. In other words, the system is literally fed with data and trained to perform computations. Hence, the term supervised learning. 

Unsupervised learning

In unsupervised learning, the ML algorithm is programmed and given capabilities to form its own clusters and associations within data. The data could be labeled and unlabeled. Unsupervised learning can predict outcomes like if a person does task #1, then the chances of the same person performing task #2 are 90%.

Reinforcement learning

This is perhaps the truest form of Machine Learning as the system becomes capable of learning patterns on its own and to make decisions based on the ground rules that have been fed into it. The algorithm continuously learns from the previous patterns and continues to improve the accuracy with which it predicts patterns. 

What does Machine Learning do?

Now that you have a basic understanding of Machine Learning and how it works, let’s further the various activities that a Machine Learning system can be entrusted with.

Although the possibilities are endless, from a designer’s point of view, there are four major use cases that Machine Learning can help with:

  1. Image recognition
  2. Pattern recognition
  3. Anomaly detection

Image recognition

Machine learning gives computers what is referred to as computer vision. Until recently, computers and bots had the ability only to read text that is written on a website or a document. With machine learning, they can now ‘see’ and understand pictures on any digital surface. For example, an Instagram image can be seen and understood by Machine Learning as containing two people at a beach with a doc. This ability is extremely useful for surveillance and security monitoring. 

For designers, this means creating visuals where the elements in the design are visually understandable by the ML system. Although understanding abstract designs might be too far for ML systems, the days are not too far when they can understand the hidden meaning of abstract images as well. 

Pattern recognition

One of the key capabilities of Machine learning is to form clusters out of any data, including text and images. Imagine the time and effort that ML could save designers if it can scan through hundreds and thousands of images and cluster them based on common factors like shapes, colors, people, and similar associations? That’s what pattern detection is all about. 

Anomaly detection

Until machine learning became capable, data analysts and scientists use to pour over massive amounts of data to handpick insights. The process was manual and hence took a long time. If the data was related to visuals, it was even slower and the insights were not always accurate. For example, it is almost impossible to determine how many images out of a bank of visuals have the same color code, pattern, or shape used repetitively. With machine learning, it is possible to pick out anomalies that are not adhering to the specified rules. 

Why should designers understand ML?

All the hustle and bustle around Machine Learning as an emerging technology might make one wonder why designers even bother understanding it. From what we have discussed until now, it is evident that machine learning has a lot to offer for designers. 

Make better palette choices

Choosing the right palette of colors that appeals to users is one of the hardest tasks that every designer must take a crack at. With ML’s assistance, they can quickly deduce which colors and patterns appeal to users most. This is done based on the user behavior and response to colors, visuals, and images on the website. 

Create machine-understandable imagery

Design and pattern recognition do not always go hand-in-hand. But, if designers know how to design imagery that systems can understand it will lead to huge breakthroughs in data analysis. With a basic understanding of machine learning, designers can create machine-understandable imagery. This will make pattern recognition and anomaly detection easier. 

Improve interaction design

Good design is not about looks, but how it helps users solve their problems. But, how can a designer know whether the design they create or have created in the past solves user problems? There is no practice of sharing data with designers that show how their designs help in elevating user experience, website conversions, mobile app interactions, and so on. However, with ML it is possible to deduce which designs and cues make customers act in a way that the programmer wants them to.

In a nutshell

Machine learning is fast becoming a mainstream technology. Designers, who are usually working on the creative side also need to understand how the technology works and its numerous possibilities. It will help them develop machine-friendly designs that will drive results for the organization.

Perfomatix | Product Engineering Services Company