One way to get a clear and well-developed strategy is to personalize a product for a narrow audience segment or even specific users. We see it every day in Facebook newsfeeds, Google search results, Netflix and Spotify recommendations, and many other products. Besides the fact that it relieves the burden of filtering information from users, the users’ connection to the brand becomes more emotional when the product seems to care so much about them.
However, the key question here is about the role of designer in these solutions. We rarely have the skill to create algorithms like these — engineers and big data analysts are the ones to do it. Giles Colborne of CX Partners sees a great example in Spotify’s Discover Weekly feature: The only element of classic UX design here is the track list, whereas the distinctive work is done by a recommendation system that fills this design template with valuable music.
Spotify’s Discover Weekly feature (a slide from Giles Colborne’s presentation)
Colborne offers 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. It’s important to learn how to work with big data and to cluster it into actionable insights. For example, Airbnb 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 38 hosts could set competitive prices . There are also endless stories about Netflix’s recommendation engine. 39
“Interaction designers vs. algorithms” by Giles Colborne (
Source ) 40
A relatively new term, “
anticipatory design ” takes a broader view of UX personalization and anticipation of user wishes. We already have these types of things on our phones: Google Now automatically proposes a way home from work using location history data; Siri proposes similar ideas. 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 background. 41
I already mentioned some examples of automatic testing of design variations used by Netflix, Vox Media and The Grid. This is one more way to personalize UX that could be put onto the shoulders of algorithms. Liam Spradlin describes the interesting concept of
mutative design ; it’s a well-though-out model of adaptive interfaces that considers many variables to fit particular users. 42 An Exoskeleton For Designers
I’ve covered several examples of algorithm-driven design in practice. What tools do modern designers need for this? If we look back to the middle of the last century, computers were envisioned as a way to extend human capabilities. Roelof Pieters and Samim Winiger have analyzed computing history and the idea of
augmentation of human ability in detail. They see three levels of maturity for design tools: 43
First-generation systems mimic analogue tools with digital means.
The second generation is assisted creation systems, where humans and machines negotiate the creative process through tight action-feedback loops.
The third generation is assisted creation systems 3.0, which negotiate the creative process in fine-grained conversations, augment creative capabilities and accelerate the acquisition of skills from novice to expert.
Large preview ) 45
Algorithm-driven design should be something like an exoskeleton for product designers — increasing the number and depth of decisions we can get through. How might designers and computers collaborate?
working process of digital product designers could potentially look like this:
Explore a problem space, and pick the most valuable problem for the business and users to solve (analysis).
Explore a solution space, and pick the best solution to fix the problem (analysis).
Develop, launch and market a product that solves this problem (synthesis).
Evaluate how the product works for real users, and optimize it (analysis and synthesis).
Connect and unify the solution with other products and solutions of the company (synthesis).
These tasks are of two types: the analysis of implicitly expressed information and already working solutions, and the synthesis of requirements and solutions for them. Which tools and working methods do we need for each of them?
Analysis of implicitly expressed information about users that can be studied with qualitative research is hard to automate. However, exploring the usage patterns of users of existing products is a suitable task. We could extract behavioral patterns and audience segments, and then optimize the UX for them. It’s already happening in ad targeting, where algorithms can cluster a user using implicit and explicit behavior patterns (within either a particular product or an ad network).
To train algorithms to optimize interfaces and content for these user clusters, designers should look into
machine learning . 46 Jon Bruner gives a good example: A genetic algorithm starts with a fundamental description of the desired outcome — say, an airline’s timetable that is optimized for fuel savings and passenger convenience. It adds in the various constraints: the number of planes the airline owns, the airports it operates in, and the number of seats on each plane. It loads what you might think of as independent variables: details on thousands of flights from an existing timetable, or perhaps randomly generated dummy information. Over thousands, millions or billions of iterations, the timetable gradually improves to become more efficient and more convenient. The algorithm also gains an understanding of how each element of the timetable — the take-off time of Flight 37 from O’Hare, for instance — affects the dependent variables of fuel efficiency and passenger convenience. 47
In this scenario, humans curate an algorithm and can add or remove limitations and variables. The results can be tested and refined with experiments on real users. With a constant feedback loop, the algorithm improves the UX, too. Although the complexity of this work suggests that analysts will be doing it, designers should be aware of the basic principles of machine learning.
O’Reilly published a great mini-book on the topic recently. 48 Synthesis
Two years ago, a tool for industrial designers named
Autodesk Dreamcatcher made a lot of noise and prompted several publications from 49 UX gurus . It’s based on the idea of generative design, which has been used in performance, industrial design, fashion and architecture for many years now. Many of you know Zaha Hadid Architects; its office calls this approach “ 50 parametric design .” 51
Autodesk Dreamcatcher (
View large version ) 53
Logojoy is 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, 54 described machine learning principles behind it . 55
Large preview ) 57
However, it’s not yet established in digital product design, because it doesn’t help to solve utilitarian tasks. Of course, the work of architects and industrial designers has enough limitations and specificities of its own, but user interfaces aren’t static — their usage patterns, content and features change over time, often many times. However, if we consider the overall generative process — a designer defines rules, which are used by an algorithm to create the final object — there’s a lot of inspiration. The working process of digital product designers could potentially look like this:
An algorithm generates many variations of a design using predefined rules and patterns.
The results are filtered based on design quality and task requirements.
Designers and managers choose the most interesting and adequate variations, polishing them if needed.
A design system runs A/B tests for one or several variations, and then humans choose the most effective of them.
It’s yet unknown how can we filter a huge number of concepts in digital product design, in which usage scenarios are so varied. If algorithms could also help to filter generated objects, our job would be even more productive and creative. However, as product designers, we use generative design every day in brainstorming sessions where we propose dozens of ideas, or when we iterate on screen mockups and prototypes. Why can’t we offload a part of these activities to algorithms?
Experiment by Jon Gold
The experimental tool
Rene by Jon Gold, who worked at The Grid, is an example of this approach in action. Gold taught a computer to make 58 meaningful typographic decisions . Gold thinks that it’s not far from how human designers are taught, so he broke this learning process into several steps: 59
Analyze glyphs to understand similarities in typefaces.
Formulate basic guidelines for combining typefaces.
Identify the best examples of type combinations to understand trends.
Create algorithms to observe how great designers work.
His idea is similar to what Roelof and Samim say: Tools should be creative partners for designers, not just dumb executants.
Generative design by Jon Gold: Plausible combinations
Gold’s experimental tool Rene is
built on these principles . He also talks about imperative and declarative approaches to programming and says that modern design tools should choose the latter — focusing on what we want to calculate, not how. Jon uses vivid formulas to show how this applies to design and has already made a couple of low-level demos. You can 61 try out the tool for yourself. It’s a very early concept but enough to give you the idea. 62
VIDEO Rene — A Product Design Tool
While Jon jokingly calls this approach “brute-force design” and “multiplicative design,” he emphasizes the importance of a professional being in control. Notably, he left The Grid team earlier this year.
Unfortunately, there are no tools for product design for web and mobile that could help with analysis and synthesis on the same level as Autodesk Dreamcatcher does. However, The Grid and Wix could be considered more or less mass-level and straightforward solutions. Adobe is constantly adding features that could be considered intelligent: The latest 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. 63
VIDEO Introducing Content-Aware Crop
There is another experiment by Adobe and University of Toronto.
DesignScape automatically refines a design layout for you. It can also propose an entirely new composition. 64
VIDEO DesignScape: Design with Interactive Layout Suggestions
You should definitely follow Adobe in its developments, because the company announced a smart platform named
Sensei at the MAX 2016 conference. Sensei 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. In 65 its announcement , the company refers to things such as semantic image segmentation (showing each region in an image, labeled by type — for example, building or sky), font recognition (i.e. recognizing a font from a creative asset and recommending similar fonts, even from handwriting), and intelligent audience segmentation. 66
However, as John McCarthy, the late computer scientist who coined the term “artificial intelligence,” famously said, “As soon as it works, no one calls it AI anymore.” What was once cutting-edge AI is now considered standard behavior for computers. Here are a couple of
experimental ideas and tools that could become a part of the digital product designer’s day-to-day toolkit: 67
But these are rare and patchy glimpses of the future. Right now, it’s more about individual companies building custom solutions for their own tasks. One of the best approaches is to integrate these algorithms into a company’s design system. The goals are similar: to automate a significant number of tasks in support of the product line; to achieve and sustain a unified design; to simplify launches; and to support current products more easily.
Modern design systems started as front-end style guidelines, but that’s just a first step (integrating design into code used by developers). The developers are still creating pages by hand. The next step is half-automatic page creation and testing using predefined rules.
“Platform Thinking” by Yury Vetrov (
Source ) 70 Pros And Cons
Should your company follow this approach?
If we look in the near term, the value of this approach is more or less clear:
Remove the routine of preparing assets and content, which is more or less mechanical work.
Broaden creative exploration, where a computer makes combinations of variables, while the designer filters results to find the best variations.
Optimize a user interface for narrow audience segments or even specific users.
Quickly adapt a design to various platforms and devices, though in a primitive way.
Experiment with different parts of a user interface or particular patterns — ideally, automatically.
Altogether, this frees the designer from the routines of both development support and the creative process, but core decisions are still made by them. A neat side effect is that
we will better understand our work, because we will be analyzing it in an attempt to automate parts of it. It will make us more productive and will enable us to better explain the essence of our work to non-designers. As a result, the overall design culture within a company will grow.
However, all of these benefits are not so easy to implement or have limitations:
We can only talk about a company’s custom solutions in the context of the company’s own tasks. The work requires constant investment into development, support and enhancement.
As The Grid’s CMS shows, a tool alone can’t do miracles. Without a designer at the helm, its results will usually be mediocre. On the other hand, that’s true of most professional tools.
Breaking past existing styles and solutions becomes harder. Algorithm-driven design is based on existing patterns and rules.
Copying another designer’s work becomes easier if a generative design tool can dig through Dribbble.
There are also ethical questions: Is design produced by an algorithm valuable and distinct? Who is the author of the design? Wouldn’t generative results be limited by a local maximum?
Oliver Roeder says that “computer art” isn’t any more provocative than “paint art” or “piano art.” 71 The algorithmic software is written by humans, after all, using theories thought up by humans, using a computer built by humans, using specifications written by humans, using materials gathered by humans, in a company staffed by humans, using tools built by humans, and so on. Computer art is human art — a subset, rather than a distinction. The revolution is already happening, so why don’t we lead it? Conclusion
This is a story of a beautiful future, but we should remember the limits of algorithms — they’re built on rules defined by humans, even if the rules are being supercharged now with machine learning. The power of the designer is that they can make and break rules; so, in a year from now, we might define “beautiful” as something totally different. Our industry has both high- and low-skilled designers, and it will be easy for algorithms to replace the latter. However, those who can follow and break rules when necessary will find magical new tools and possibilities.
digital products are getting more and more complex: We need to support more platforms, tweak usage scenarios for more user segments, and hypothesize more. As Frog’s Harry West says, human-centered design has expanded from the design of objects (industrial design) to the design of experiences (encompassing interaction design, visual design and the design of spaces). The next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systems. Rather than hire more and more designers, offload routine tasks to a computer. Let it play with the fonts.
(vf, il, al)
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