Generative Graphic Design: Will Algorithm-Driven Design Change our Approach in Designing?
It has always been of interest, machine learning, a subset of the subject artificial intelligence (AI), which, in its intriguing nature, sparks the questions of ethics and morality especially now that machines have arrived and are learning at an accelerating rate. In this modern-day and age, machines have been implemented to do tasks that vary from high-quantity production at a lower cost to data outsourcing on social media platforms, simple tasks which the majority of the process are repetitive and can be done without human input other than the initiation and control aspect of these tasks. Generative design itself has been used by architecture and civil engineering for quite some time (for context, it would be Autodesk that offers such tool). But the idea of a powerful tool to help generate as many iterations as possible seems to be a wonderful tool for a creative field like graphic design. As a job that is not a utilitarian task in its nature, graphic design will still require major human input to solve a problem, same goes for how it is used for architecture and civil engineering which process will be talked about below. For those who aren’t familiar with the process of solving a (graphic) design problem, even though it varies from one designer/design team to another, the variables are usually the same.
The Human-Dependent Design Process
The starting point is inarguably at the same point: defining the problem and initiating research, then undergo the strategic route to plan out their approach in solving the problem in terms of timing, budget, requests, and other micro-variables that layer on top of said-project. Some will go straight to experimentation to see the best approach. Fellow designers might be familiar with back-and-forth amends to fulfill the client’s needs and wants pre-production and/or post-production, and decision-making is inevitable from both parties. The process is progressive but it involves reflection for advancement. There is no ‘one size fits all’ process as the famous saying goes “everyone is different” (everyone in our case being the designers). The design process itself involves countless decision-making input that requires creative conscience, and such decision-making events on each variable cannot be fully given to a machine because machines are not capable of obtaining creativity yet. Unless we live in a time where highly intelligent and capable androids are mass-produced, but until then I’m only getting ahead of myself.
Involving a computer program and a set of constraints, the highlight of generative design is its iterative process that creates countless permutations to choose from of the end product and visually, the product will look like it’s in constant flux as if it keeps evolving. This graphic manipulation process can be divided into two types (which names I put together myself for the purpose of instant identification): Author-based input and Audience-based input.
Based on my observation, Author-based input design relies heavily on the author (designer) from the algorithmic collaboration to the set of constraints which produces output that, when done, is fixed and unchangeable. An example of Author-based input design is the collaboration by E Roon Kang, Richard The, and Studio TheGreenEyl for the MIT Media Lab Boston identity, which algorithmic design results in 45,000 permutations.
On the other hand, Audience-based input design involves the audience for completion by inserting their set of constraints, which adds more experiential value. An example of this is ‘Phase’, a generative type tool created by type designer Elias Hanzer, developed by Florian Zia that reacts to a manual slider that manipulates its form in real-time or sounds through voice input.
The experimental nature that is in generative design, in general, allows us creators to go in deeper when it comes to experimenting when possibilities are now endless. It might be liberating to some, now that this generative tool, not only does it generate a boundless amount of iterations to our design, but it also buys us time. To some extent, it probably does sound intimidating now that this kind of technology available today can create more iterations than we ever could, realizing limitless potentials that we could ever imagine. So what does this mean for us?
Modern Problem Requires Modern Solution
The question “will AI take my job?” sounds quite outdated for designers now that it has been answered with a “No” since creative endeavor is something that can’t be hard-wired on a human-level experience (at least for now), and computational creativity is still a growing area of research. But, there’s a catch.
With the ever-growing workplace demands and what feels like an intensified competition for some, the need for deeper and wider experimentation to seek for a difference that will stand out from trends and similarities also intensifies as mediocrity becomes a foul net to stay away from. But this doesn’t have to be scary. The tool’s potential to, for example, nail a dynamic brand identity or to create a real-time data visualization is immense and impactful that it could possibly be a game-changer in the future, which comes with its pitfalls too. The reality is that while its ability to flawlessly generate possibilities may replace designers of today, it certainly will not replace designers of tomorrow. Therefore, with this powerful tool in our hands today, how do we survive and thrive for tomorrow?
Associate professor of computer science at Georgetown University Cal Newport explains in his book ‘Deep Work’ that the two groups that he claims are poised to thrive in the new economy (a result of unprecedented growth and impact of technology that are massively restructuring our current economy) are those who can work creatively with intelligent machines and those who are stars in their field.
The power that we have as designers is actually immeasurable: to make and break rules at the same time. The decision-makers that are constantly reimagining the definition of beauty in what we use, what we see, and what we experience every day. Our future creative endeavor in design could perhaps be an open collaboration between man & highly-intelligent machines as much it is today with our devices and software. The idea may sound ludicrous now but it may become the new norm in the future.
Cal Newport also generously points out the two core abilities to thrive in the new economy in ‘Deep Work’: The ability to quickly master hard things and the ability to produce at an elite level, in terms of both quality and speed. The ability to quickly master hard things allows you to adapt to changes in the new economy easier. The accelerating rate of the ever-evolving technology might frighten those who are unable to keep up and have bigger chances to be left out as demands themselves will adapt and evolve to what’s available in the market. The spare time to learn new a skill or software you haven’t had the chance to delve into might be worth investing now for the long run. Excelling at what you do so differently that no one does it like you do sounds like a solid investment too. Technology does not and will not stop anytime soon, and for the millions of years that we’ve evolved as a species, this time around is no different. Here, I’m not nor will I ever intend to go against traditional, hands-on medium such as prints. The main objective is to fully utilize and adapt to technological advancement.
To loosely quote from a design talk I attended this year, “We upgrade our devices every day, so why not ourselves & our workflow?”
