As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As the hype around machine intelligence intensifies, this will lead to technology-driven pressure to extensively utilize machine learning. This may happen with little understanding of its actual benefits and its impact on product desirability and customer experience.
As a designer, to have your say about any plans for machine intelligence and how it is best implemented on the human interface layer, you need to know what it can do and how digital services can utilize it. Design teams have a responsibility to understand the inner workings of the digital or connected products they create.
The following post helps demystify how ML and design compliment each other with FAQs, and examples of how machine learning is being applied to every design discipline.
ML+Design Frequently Asked Questions (FAQs)
Q: What is Artificial intelligence (AI)?
A: Ai is the science of making machines intelligent, so they can recognize patterns and get really good at helping people solve specific challenges or sets of challenges.
Q: What Is Machine Learning?
A: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning Glossary for developers
Q: What is an ML Model?
A: specialized, connected, mathematical functions. Together they represent the steps an intelligent machine will take to arrive at a decision.
Q: What is Classification?
A: Classification is a task in which a model must predict what specific known group(s) a new input might belong to.
Q: What is Regression?
A: Regression is a task in which a model must predict a numerical value for a specific scenario.
Q: What is a Confidence level or score?
A: A confidence score or level is a numerical expression of certainty in percentages.
Q: What is Generative Design?
A: Generative design is an iterative design process that involves a program that will generate a certain number of outputs that meet certain constraints, and a designer that will fine tune the feasible region by selecting specific output or changing input values, ranges and distribution. Throughout the design process the designer learns to refine the program with each iteration as their design goals become better defined over time.
The output could be images, sounds, architectural models, animation, and much more. It is therefore a fast method of exploring design possibilities that is used in various design fields such as art, architecture, communication design, and product design.
Machine Learning Meets Design
ML+Design: UI Construction
- FIREDROP: A conversational UI assistant. You provide the content, then it helps you to create a layout and choose a visual style.
- THE GRID V3.: Chooses templates & presentation styles, retouches and crops photos — all by itself. Moreover, the system runs A/B tests to choose the most suitable pattern. | https://thegrid.io/
- ADAPTIVE MODULAR SCALE: Experimental computational design platform that generates design system tokens. | https://components.ai/
ML+Design: Content Preparation
- AIRBNB: The team learned how to answer the question, “What will the booked price of a listing be on any given day in the future?” so that its hosts could set competitive prices. Visit >>
- SKETCH CONFETTI: Sketch Confetti. A plugin generates modern confetti patterns that fit into existing screen mockup. Visit >>
- PRISMA: Neural network-based app that stylizes photos to look like works of famous artists. Visit >>
- ASSISTED WRITING: Assisted Writing re-imagines word-processing & explores new forms of writing, that allow authors to shift their focus from creation to curation, and write more joyfully. Visit >>
- YANDEX LAUNCHER:An Android launcher uses an algorithm to automatically set up colors for app cards, based on app icons. Visit >>
- ADOBE FONTPHORIA: This Sensei experiment turns any letter image into a glyph, then creates a complete alphabet and font out of it. It can also apply the result to a physical object via augmented reality. Visit >>
ML+Design: Individualized User Experiences
- Airbnb: The team learned how to answer the question, “What will the booked price of a listing be on any given day in the future?” so that its hosts could set competitive prices.
- Mutative Design: A well-though-out model of adaptive interfaces that considers many variables to fit particular users by Liam Spradlin. Here’s another application of this idea by researchers from Aalto & Kochi Universities.
- Anticipatory Design: It takes a broader view of UX personalization and anticipation of user wishes. We already have these types of things on our phones: Google Now and Siri automatically propose a way home from work using location history data However, the key factor here is trust. To execute anticipatory experiences, people have to give large companies permission to gather personal usage data in the
- Giles Colborne on AI: Advice to designers about how to continue being useful in this new era and how to use various data sources to build and teach algorithms. The only element of classic UX design in Spotify’s Discover Weekly feature is the track list, whereas the distinctive work is done by a recommendation system that fills this design template with valuable music.
- Persado: An algorithm that deploys individualized phrases based on what kinds of emotional pleas work best on you. They also experiment with UI.
ML+Design: Graphic Design
- Logojoy: A product to replace freelancers for a simple logo design. You choose favorite styles, pick a color and voila, Logojoy generates endless ideas. You can refine a particular logo, see an example of a corporate style based on it, and order a branding package with business cards, envelopes, etc. It’s the perfect example of an algorithm-driven design tool in the real world! Dawson Whitfield, the founder, described machine learning principles behind it. Logoshuffle and My Brand New Logo are similar tools.
- Variable Fonts : Parametric typography based on the idea of interpolation from several key variables: weight, width, and optical size. In 2016 it became a part of OpenType format specification. Previously, it was only possible to use variable fonts on the web through hacks or via desktop tools like Robofont.
- Generative Branding: Oi: Wolff Olins presented a live identity for Brazilian telecom Oi, which reacts to sound. You just can’t create crazy stuff like this without some creative collaboration with algorithms.
- Adobe Sensei: A smart platform that uses Adobe’s deep expertise in AI and machine learning, and it will be the foundation for future algorithm-driven design features in Adobe’s consumer and enterprise products: semantic image segmentation, font recognition, and intelligent audience segmentation. Scott Prevost sees 3 ways to apply it to designers workflow.
- Photoshop Content-Aware Crop: 2016 release of Photoshop has a content-aware feature that intelligently fills in the gaps when you use the cropping tool to rotate an image or expand the canvas beyond the image’s original size.
- Photoshop Scene Stitch: Photoshop added another content-aware feature — it replaces a whole part of a photo with a relevant piece from Adobe Stock collection. No need for intense retouching anymore. Another experiment is Project Cloak which replaces objects on videos. See more MAX 2017 announces based on Adobe Sensei platform (Puppetron and PhysicsPak are the best).
- Fast Mask: Another Sensei experiment that selects and tracks an object in a video. I.e. you can put a text behind a dancer. See more MAX 2018 announces (Smooth Operator and Good Bones are the best).
- Microsoft Animation Autocomplete: An experimental tool for autocompleting illustrations and animations. Shadow Draw is a similar concept.
- Drawing Operations Unit: Generation 2: Sougwen Chung creates collaborative art with her robot. It learns from her style of drawing, turning her practice of art-making into a real-time duet.