Machine Learning Meets Design

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?
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

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

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