AI News, Rethinking Design Tools in the Age of Machine Learning

Rethinking Design Tools in the Age of Machine Learning

As a result, design tools have tended towards two opposite extremes: On one end of the spectrum, we have the “one-size-fits-all” approach, which can generally be found in consumer-level design tools — tools that simplify design processes by forcing users into one of a handful of pre-ordained templates.

On the other end of the spectrum, we have the “kitchen sink” approach, utilized by professional design tools — tools that provide an overwhelming number of low-level features that come with steep learning curves and often do not coincide with the user’s way of thinking.

At first, it would appear that machine learning offers a slightly more sophisticated version of the “one-size-fits-all” approach — a way of simplifying design processes by shifting some of the decision-making responsibilities away from the designer.

But, perhaps even more exciting is that these mechanisms will enable designers to give their entire focus to the design work itself rather than on learning how to map their ideas to the ways in which a particular tool has been organized.

When we first encounter the problem, we may not have an internal sense of the constraint: “At what point does this property ratio cause the glass to tip over?” We gain expertise by experimenting within the search space, learning the relation of properties to one another and to an initially unknown set of constraints imposed by the physical world.

If we want to maintain creative freedom, we seemingly need to either stick to low-level operations or generate a very large number of high-level features that would cover a wider range of possible use cases but would also sacrifice the succinctness of the tool’s vocabulary.

Rather than constructing design tools around an immutable set of pre-built high-level features, a recurrent neural network can be employed to discover commonly-used sequences of low-level features and dynamically synthesize purpose-built features related to the designer’s current activity.

Somewhat like recommendation systems that suggest music or movies based on the similarities of users’ tastes, the discovery of patterns across numerous designers can be employed to suggest relevant features to an individual based on the workflows he or she tends to utilize.

If we were to stop a random sample of people on the street, give them a blank piece of paper and ask them to design their ideal living room, many people would not know where to start.

For example, let’s say we’ve created the above drawing of an oak leaf using Bezier paths and later decide that we want something that looks more like a maple leaf.

variation control surface allows designers to be guided by their design intuitions rather than being limited by the proclivities of a particular tool’s way of abstracting the path to a given destination.

This allows us to navigate the design space verbally with commands like: “take me to a maple leaf.” Once there, we could say something like: “take me a bit closer to an oak leaf.” This is really powerful on its own.

But, it turns out that we can take this idea even further… In 2013, Tomas Mikolov and others released a series of papers describing a set of techniques for producing low-dimensional maps that represent the conceptual relationships between words.

As the Photoshop “filter bubble” of the 1990s proved, the novelty presentation of this functionality quickly turns to kitsch and does little to re-conceptualize or extend design processes in a meaningful way.

But as individual components of a larger and more wholistic design framework, these techniques provide a powerful mechanism for operating on media without leaving their native vocabulary, without mapping them onto an abstraction.

To help designers build this kind of expertise, let’s explore two more concepts… Simple expressions that communicate an individual command or point of information are more easily understood by machine learning systems than complex, multifaceted statements.

Tools could help the designer to deliver concise statements by creating interfaces and workflows that lead the user through a series of simple exercises or decision points that each address a single facet of a much larger and more complex task.

Like the original road trip game, 20Q asks the user to think of an object or famous person and then poses a series of multiple choice questions in order to discover what the user has in mind.

This system uses a kind of machine learning algorithm called a Learning Decision Tree to determine the sequence of questions that will lead to the correct answer in the smallest number of steps possible.

Using the data generated by previous users’ interactions with the system, the algorithm learns the relative value of each question in removing as many incorrect options as possible so that it can present the most important questions to the user first.

For example, if it were already known that the user had a famous person in mind, it would likely be more valuable for the next question to be whether the person is living than whether the person has written a book because only a small portion of all historic figures are alive today but many famous people have authored a book of one kind or another.

Though none of these questions individually encapsulates the entirety of what the user has in mind, a relatively small number of well-chosen questions can uncover the correct answer with surprising speed.

In addition to aiding the system’s comprehension of the user’s expressions, this process can benefit the user directly in his or her ability to communicate ideas more clearly and purposefully.

Each question and answer interaction serves as a translation vector through concept-space, moving the user a bit closer to his or her intended output while also probing the user to think about and articulate each facet of the idea.

Drawing on natural modes of interaction, these questions could be answered either verbally… …Or gesturally, preventing the user from needing to learn a complex menu system in order to access the tool’s capabilities: Building on recent advances in machine learning, it is increasingly possible for the machine to answer the user’s complex, contextual questions about the properties of a design: For example, the user could pose factual questions that would help him or her to evaluate the design’s suitability for some intended use: This dialogue would imitate the form of human conversation, but would benefit from the machine’s omniscient knowledge of the design’s properties.

This could also be tied to the machine’s ability to model real-world constraints such as material, physical or chemical ones: By embedding this capability within a realtime interaction, an architect, for example, could save a great deal of time by being able to quickly eliminate nascent ideas that are unlikely to yield fruitful results.

Aside from “real world constraints,” the user’s meaning in a given interaction may not always be clear — either because of the machine’s knowledge limits or because of a lack of clarity in the user’s statement: Rather than going with a “best guess,” the machine could offer clarifying questions and alternatives: This conversational approach would therefore help to clarify the user’s intent as well as build the machine’s knowledge base.

conversational approach also presents a natural mechanism for preserving the user’s iterative process in a manner that is far more accessible for review and reflection than an “Action History.” By unrolling the interface into a linear, traversable “news-feed,” the user is able to inspect each stage in his or her thinking and easily return to earlier iterations, branching off in a new direction while still preserving each other version of the design.

I’ve created a combined programming language and design tool called Foil which aims to bring many of the concepts we’ve discussed to life and is intended for users along the full spectrum of design experience, from novice to expert.

Depending on the user, Foil can be a consumer design tool, a professional design tool, and a platform for the creation of emergent interface elements and design widgets which users will ultimately be able to share with each other.

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