How distant is the future when machine learning for designers gets real?
What key skills do you need to possess in the era of machine learning in design?
How do we implement machine learning in design today?
We hope that you may find this post useful in case you work as a (web) designer and want to learn about the changes that your profession might face anytime soon. It is better to keep up with the changes than to leave them unattended, isn’t it? So, what exactly is machine learning?
Machine Learning Is …
… a new branch of Artificial Intelligence. The main aim of machine learning is to train machines, like computers or apps, so that they can learn from big data. Having done that, machines are expected to adjust their performance accordingly. Quite simple, isn’t it? There are many machine learning examples that have already become an integral part of our daily lives.
For instance, let’s take Netflix recommendations. Based on the list of movies and shows you have watched, the system tries to guess what other video products you can find interesting.
Uber also belongs to successful machine learning examples. Yes, these are the machines that are responsible for things like trying to minimize the wait time and determining the pricing policy of the services provided. Lots of machine learning examples can be found on the Internet too.
Amazon is putting much effort into personalizing its front page with machine learning in order to increase its customers’ engagement. It may surprise you but even Gmail spam filters rely greatly on machine learning methods. As the quality and the number of spam is constantly on a rise, these filters need to learn intensively to keep your mailbox safe.
The Major Machine Learning Methods
Though the overall concept of machine learning is easy to follow, the machine learning methods may look a bit challenging (s. the chart below).
In short, there are three machine learning methods, i.e. supervised, unsupervised, and reinforced. The supervised machine learning method is used when there is already processed and labeled data that can be applied to new datasets. Examples of how this method is used include fraud detection and different diagnostics procedures.
As opposed to this method, the unsupervised machine learning method doesn’t require any prior labelling of data examples. It is applied to look into targeted marketing, recommendations etc. As for the reinforcement machine learning method, its main idea is that a machine can have an interaction potential and an ability to assess the data. For instance, this method is used with machines that can tell the difference between mistakes and rewards to make real-time decisions.
Machine Learning for Designers – A Distant Future or Now?
Yes, we all still believe that machines cannot replace humans when it comes to creative professions, like designers. At the same time, the statistics data gathered by the World Economic Forum prove that machine learning models belong to top trends that will have changed the job market by 2020 (s. the chart below).
As the executive summary of World Economic Forum “Future of Jobs” delicately puts it, instead of replacing humans, machine learning methods will lead to changing to core skills needed for most occupations in the nearest future.
Machine Learning for Designers: The Primary Skill to Learn
The question remains, however, what changed skills will be on-demand for designers in the nearest future. To answer this question, we need to understand that it makes sense to apply machine learning for designers only in two cases. The first case is the optimization of the labour cost. In other words, machine learning in design is not the smartest investment if you are not having enough orders to be paid for. The second case when you need machine learning for designers is to minimize the time span spent on a design project. But again, if we talk about minutes, why bother with machine learning in design?
To cut the long story short, it seems that only designers can decide whether machine learning in design can be of any use for a given project. In other words, designers will always have the final say in a debate about the practical value of machine learning. As a result, this is decision-making that you have to work on to stay afloat in the turbulent design world.
Machine Learning in Design
Now that you are probably relieved to know that you (and not machines) will stay in charge of design for years to come, it is high time to learn what we can teach machines to do today. For starters, a program can be trained to suggest layouts and colour palettes based on your previous works or words used in your designs. Another example of how you can use machine learning in design is to teach a program to recognize the repetitive operations that come in sequences to speed up the creative process. When gathered from several designers who work on the same project, machine learning in design can work miracles in terms of time optimization.
Also, machine learning in design has an inclination to be used to create intuitive interfaces based on the “I know it when I see it” principle. Allowing users to experiment with designs without losing much of their time, machine learning expands the explorative potential. Another great way that machines can help us with is the style transfer. Using data from other images, the Style Transfer is capable of distinguishing the major style features and applying them to your current design project. As demonstrated in the article by Walid Ahmad, you can create a styled image of a cat easily using Style Transfer!
As you can see, machine learning for designers is not that scary as it sounds. Now that you know about the major methods of machine learning and ways to use machine learning in design, it is time for you to assess your decision-making skills. From what we have learned so far about machine learning models, decision-making is what all successful designers of the future will have in common. So, start making decisions today and make your magic designs matter tomorrow! Further learning: If you want to expand your knowledge, you can find a range of machine learning courses on Udemy.
Allison is a professional content marketer and an inspired author. Marketing manager by day and a writer by night, she is creating many articles on business, marketing, design and web development. She loves working with website builders and CMS, and sharing her experience with the readers. Follow her on LinkedIn and Facebook.
Thank you for subscribing to MotoCMS blog!
This email is already in use.
Something went wrong. We are fixing this. Try a bit later.