Previously, I've introduced three underlying general capacities of machine learning that are exploited in applications. However, they are not enough for designers to actually start building applications. This is why this particular post introduces four general design guidelines that can help on the way.
How can we and will we communicate machine intelligence to users, and what kinds of new interfaces will machine learning call for?
Machine learning under the hood entails both opportunities to do things in a new way, as well as requirements for new designs. To me, this means we will need a rise in importance of several design patterns or, rather, abstract design features, which will become important as services get smarter. They include:
- suggested features,
- shortcuts vs. granular controls,
- graceful failure.
Text and speech prediction has opened up new opportunities for interaction with smart devices. Conversational interfaces are the most prominent example of this development, but definitely not the only one. As we try to hide the interface and underlying complexity from users, we are balancing between what we hide and what we reveal. Suggested features help users to discover what the invisible UI is capable of.
Graphical user interfaces (GUIs) have made computing accessible for the better part of the human race that enjoys normal vision. GUIs provided a huge usability improvement in terms of feature discovery. Icon and menus were the first useful metaphors for direct manipulation of digital objects using a mouse and keyboard. With multi-touch screens, we have gained the new power of pinching, dragging and swiping to interact. Visual clues aren’t going anywhere, but they are not going to be enough when interaction modalities expand.
How does a user find out what your service can do?
Haptic interaction in the first consumer generation of wearables and in the foremost conversational interfaces presents a new challenge for feature discovery. Non-visual cues must be used that facilitate the interaction, particularly at the very onset of the interactive relationship. Feature suggestions — the machine exposing its features and informing the user what it is capable of — are one solution to this riddle.
In the case of a chatbot employed for car rentals, this could be, “Please ask me about available vehicles, upgrades, and your past reservations.”
Specific application areas come with specific, detailed patterns. For instance, Patrick Hebron’s recent ebook from O’Reilly contains a great discussion of the solutions for conversational interfaces.
Once a computer gets to know you and to predict your desires and preferences, it can start to serve you in new, more effective ways. This is personalization, the automated optimization of a service. Responsive website layouts are a crude way of doing this.
The utilization of machine learning features with interfaces could lead to highly personalized user experiences. Akin to giving everyone a unique desktop and home-screen, services and apps will start to adapt to people’s preferences as well. This new degree of personalization presents opportunities as well as forces designers to flex their thoughts on how to create truly adaptive interfaces that are largely controlled by the logic of machine learning. If you succeed in this, you will reward users with a superior experience and will impart a feeling of being understood.