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When AI learned to design

Article by sarika in Design

When AI learned to design

Have you ever wondered ”Can we really easily complete the design part of a product or marketing collaterals without including the designers?”AI (artificial intelligence) has become an over-hyped buzzword across many industries. Artificial intelligence is something everyone has heard of. Software and apps that can learn and improve on their own continue to increase investment. It's no wonder AI is involved in every area of our life. The field of design is no exception. Machine learning brings design to its new development phase. Even the most common applications begin to use AI to improve performance.In this blog, we will be discussing in detail on how design and AI can go together in this era of advancement and how they both can benefit from each other.

Capabilities

Graphic design used to require physical work. To compose letterheads, business cards, brochures, magazines, books, and posters, you hunched over a desk or a light table. In keeping in mind the recent advancements, experts see the field moving on an upscale path. It is expected that in the coming years, users will step back from hands-on labor and let the software generate ideas and plans.

AI Capabilities in design

  1. Creating visual styles like image filters
  2. Tools like Prisma identify the exact content of the image, whether a human face or a lemon slice and use intelligent filters based on image recognition technology to create the finest visual effects. There's an entire cohort of such apps that can fabricate generative and effective visual styles to augment the designer's capabilities. Applications like Prisma is a good example of this case.
  3. The personalization of user experience
  4. Websites are becoming more sharp-witted by considering a multitude of user data aspects to capacitate enhanced customized experiences for the visitors like time of the day, the day of the week, a source of users, device types used by the users and an ever-increasing series of data points and indicators which even users are not aware of. Techniques like A/B testing can be employed to different customers/clients and design and products can be personalized using their preferred choices. Coalescing all these facets can offer you abundant creative insights about exact user requirements and intent when they visit your website.
  5. AI for UX
  6. Smart user experiences will soon become the new norm, one of the most promising but least explored angles. Facebook applies AI to visually impaired users when they read the photo content.
  7. AI for image generation
  8. Not only AI agents can reduce the redundant tasks in design generation, it also can handle end to end design generation pipeline. Such AI agents can take in user requirements as input and generate required graphics and images for the tasks at hand.
  9. Using ads to refine the design
  10. Ads are used to keep track of user preferences and browsing habits. Using this data AI agents are able to customize a webpage for the incoming traffic.

Bucketing capabilities into two groups

We can differentiate the above-mentioned capabilities of AI agents in design mainly under two aspects. One aspect can be effort reduction wherein AI agents can reduce the pain of designers by taking in the redundant task of designers and automating them. Another aspect can be applications where AI agents are actually able to follow complete design pipeline and generate designs and patterns on their own.

Examples

  1. Adobe Sensei
  2. Adobe Sensei uses artificial intelligence (AI) and machine learning to help you to discover hidden opportunities, make tedious processes fast and offer relevant experiences to every customer. It can perform tasks like cropping graphics, finding stock images and techniques like face-liquify.
  3. Google Auto Draw
  4. AutoDraw is a collaboration between machine learning and the artist community. It can currently guess hundreds of designs. Below are some drawings created by different designers, illustrators, and artists, for public use.
  1. Grid.io
  2. Grid.io an AI agent which can take web content as input from the user and build websites with elegant designs.
  3. Prisma
  4. Prisma uses artificial intelligence and deep learning algorithms to process the images. All the image processing happens on their servers instead of locally on your app. This is the reason it takes a while before you can see the result. These servers have a few layers of neural networks each doing its own work.
  5. Firedrop
  6. Deliver your creative vision at scale with machine learning. Their technology learns your design approach and style rules and can generate variants of your design on demand at large scale.

Some other examples can include Dovetale, Let's Enhance.io, Deep art, 3D coloring Alive live.

Bucketing different AI tools into categories

Also read: Design marketing driven and developer friendly website on HubSpot COS

So are designers then needed at all?

Many of the products now available will disappoint users expecting miraculous results from AI genies. That's a letdown, for sure, but it also gives us some time to think about what kind of design work we want machines to do for us, and what roles we should be reserved for human beings.Many people think that design is an addition to the world. They do not believe it is necessary; rather that design is a superfluous beautification you apply when the product is finished.Without art and design and culture, technology is bound to either fall flat or not really capture the wholeness of humanity. In other words, it's not only about visualizing, but it's rather understanding what the impact of the technology can be, what the side effects can be, and how to counter them.

Goods v/s Not so goods

In this section, we will have a look at some cases in which AI agents can really perform well in the field of graphic design. Also, there is no denying fact that AI is a solution for every problem hence we will be looking at few cases where AI agents have not been able to succeed well in the design field.

Few Good Things

  1. Redundant Task Automation
  2. Image Enhancements
  3. Color Recommenders
  4. Drawing Assistants

Not so good

  1. Website Generators
  2. Design/Pattern Generators
  3. Requires Supervision
  4. Cost and Maintenance

Conclusion

I see AI as a tool. When designers master that tool, they can expand their ability.

Engineers, although they might technically understand machine learning systems, can lack a sense of AI's impact on society. They might see AI as just coding, but they need to realize that AI is not just a technology. Rather, it is part of the world.I believe that design is the enzyme for any kind of innovation. Designers must be embedded in engineering and coding teams to keep the AI and machine learning efforts real and keep them part of the world.

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