How AI And Machine Learning Are Transforming Front-End Development?
Building an intuitive and engaging website is the primary goal of developers. They need to go through several processes, including drawing concept sketches, building prototypes and testing the website before pushing it live.
These processes don’t happen overnight. In fact, developers spend weeks and months to build a beautiful and responsive website. But the advancement in technologies is making things easier for them.
The modern technologies like artificial intelligence (AI) and machine learning are accelerating front-end development and making coding and testing of website layout easier, faster and more efficient.
Especially, the deep learning, a part of machine learning, is playing a crucial role in front-end development.
• Deep Learning in Front-end Development
Deep learning is a sub-category of machine learning that involves training and inference. There are artificial neural networks that need to be trained. Users will need to feed datasets to an untrained neural network along with the right answers. What this means is that training will include a large amount of input and respective output.
Following the training, the neural network will become capable to analyze and understand the input and then return the output accordingly. Primarily, the deep learning models are used for several use cases of AI, like computer vision and natural language processing.
Front-end developers can use similar models for designing UI elements by training neural networks with relevant data.
• Training Neural Networks
There are two types of data that front-end developers can use to train a neural network. The first one is a graphical user interface (GUI) screenshots and the second one is whiteboard sketches. The screenshots and sketches would hold their respective code.
This training will make the neural network able to generate code from a GUI screenshot, which is called pix2code. Whereas, the conversion of sketches to code is called sketch2code. After analyzing the screenshots and sketches, the neural network will find out the ways to code these images in HTML or CSS. For images with text, the networks will also go through text recognition steps.
Following these essential steps, the neural network comes into action and leverages its knowledge of objects, their positions and design layouts for generating code for design elements. Eventually, this neural network becomes more capable to understand visual design elements and the intention of coding easily.
Although the power consumption of deep learning is higher, the use cases of this technology remove the roadblocks in web designing and make the entire process seamless.
Since there are millions of websites out there, developers don’t need to worry about the data needed as input for training deep learning models. Thousands of websites are pushed live every single day, which means that the training can be done with up-to-date design elements. Additionally, this approach can also be used for designing iOS and Android mobile apps.
• Microsoft AI Sketch2Work Tool
Tech giant, Microsoft is deeply involved in artificial intelligence to transform the world. With several products and services, it is also making the lives of developers easier.
For instance, the Sketch2Code tool by Microsoft AI Labs uses deep learning to create a system that can understand the things drawn by the designer on a whiteboard. This system then converts the understanding into HTML code.
Below is a sample of how the service streamlines the process of UI design.
Microsoft has trained its Sketch2Code system with millions of images. It can detect objects for a broad range of objects. The process is simple — upload your hand-written design, Sketch2Work will be at work, review your HTML and finally download it.
• Applications of AI-based Front-end Development
Enterprises of all sizes have started to use this technology to power their UX design.
Airbnb, the largest community-driven hospitality company in the world, is applying AI to create its own prototype. Benjamin Wilkins, the design technology leader at Airbnb says that the time required to test an idea should be zero. With this goal, the company uses the prototype to generate a workable design code from sketches.
Initially, Airbnb had trained the prototype with only a dozen hand-drawn sketches, machine learning algorithms, as well as some intermediary code for rendering elements from the design system into a browser.
On the other hand, Uizard, a startup in Copenhagen, is using the concept of converting images into HTML code for its entire business. The startup has built a machine learning algorithm that can read images and return the customizable and production-ready code related to the platform.
Uizard is applying this concept to the development of mobile and web applications.
• Primary Advantages of Machine Learning in Front-end Development
When machine learning is used to power front-end development, the designers and developers get more time to focus on creative tasks. They spend several hours and days interpreting sketches or finding out relevant graphic design.
A streamlined process of front-end development will enhance the way enterprises in this arena work. If the senior executives want to try out new ideas sooner or later, the developers wouldn’t need to spend the same time again.
Furthermore, web developers and companies will be able to predict the time required for the production of a project. Eventually, the optimization of time, resources and design budgets can help companies to reduce costs and increase return on investment (ROI).
Within the next couple of years, experts predict that AI and machine learning will completely change the way developers work on the front-end of a project. It will speed up the prototyping and break the barriers of software development.
Application of machine learning in front-end development can revolutionize the way developers, designers and companies operate in this space. What developers need to do is to become familiar with modern technologies so that they don’t get a deadlock stage.
AI and machine learning wouldn’t replace developers, rather, these technologies will assist them in becoming more creative and allow them to focus on fundamental tasks.
Also Read: Software Development Trends in 2019