AI is an increasingly compelling tool for powering creativity and innovation, and it’s so embedded into our digital lives that we use it and benefit from it constantly without a second thought. Here at Interwoven we take advantage of a wide range of AI tools every day. In this Insight article we discuss how we can use generative AI tools to enhance design ideation and fuel the creative process. We explore the advantages and challenges of integrating AI into design practices and discuss how AI is transforming the creative process. To set the scene, we first outline the major types of AI. We then define what generative AI is and where it sits in the AI hierarchy. Buckle up!
The Main Types of AI
While we’ve talked about AI before, we realize it would be useful to break down the main types of AI that are available so we can talk about it in a more specific and nuanced way. AI is a big category that is growing fast, and simply saying “AI” leaves room for interpretation given how many types of tools are available, how many we wish were available (but aren’t yet), and how different they are from one another.
Artificial intelligence is the intelligence of machines (including software) as well as the field of study that develops and investigates those intelligent machines. In the field, AI is sorted in two ways: by function and by capability. These are essentially two different ways of thinking about how we can categorize the same tools. In each of these categories, there are subcategories of types of AI. What is notable about these categories is that most of them have yet to be realized, remaining on a presumably distant horizon. What is also notable is that, despite the prevalence of information about AI bombarding us non-stop right now, we don’t often see these categories being used. We hear ‘deep learning’, ‘algorithm’, ‘generative’, and everything just gets called AI. Here’s some terminology that will help us be more specific about what we mean when we say AI.
AI Types by Function
- Reactive AI: Reactive AI is the most basic form of artificial intelligence. Reactive AI operates by providing predefined outputs based solely on the inputs it receives. It lacks the ability to learn or adapt beyond its initial programming, and it consistently responds to identical situations in the same predetermined manner. Examples of reactive AI include IBM’s Deep Blue, known for defeating chess grandmaster Garry Kasparov, as well as email spam filters. While reactive AI is extremely effective within its sphere of ability, its performance is limited.
- Limited Memory AI: Building upon reactive AI, limited memory AI enhances its capabilities by learning from past experiences. It leverages historical and observational data to make predictions and engage in complex tasks. A good example of limited memory AI is autonomous vehicles, which use data to analyze the behavior of other vehicles, enhancing their ability to navigate safely. These deep learning algorithms can improve over time as they process more data. While increasingly sophisticated, limited memory AI is unable to store information in long-term memory, limiting its potential for cumulative knowledge building.
- Theory of Mind AI: Theory of Mind AI refers to machines with human-like decision-making abilities and emotional intelligence. Machines with this capability will be able to attribute mental states to other entities, including beliefs, emotions, and goals, and adjust their behavior based on emotional cues. While progress is being made, theory of mind AI is still unrealized.
- Self-aware AI: The most advanced type of AI is self-aware AI. These are (imagined) machines with consciousness, emotions, desires, and self-awareness akin to human beings. This is Terminator territory. These machines would not only recognize their own internal emotions and mental states but also make deductions based on their emotional experiences. This type of AI requires advanced algorithms and hardware that are currently beyond our reach.
AI Types by Capability
- Narrow AI (Weak AI): This is the most prevalent type of AI today, designed for specific tasks and outperforming humans in those tasks. Examples include chatbots, autonomous vehicles, and virtual assistants. Siri and Google Translate are examples of narrow AI. Both reactive AI and limited memory AI tools can fall into this category.
- Artificial General Intelligence (AGI): Sometimes referred to as strong AI, AGI is a hypothetical form of AI with human-like learning, understanding, and adaptability. It can perform tasks as efficiently as humans across a wide range of domains. Researchers are working on it.
- Artificial Superintelligence: This hypothetical AI surpasses human cognitive abilities, excelling in thinking, reasoning, learning, and decision-making. It would outperform humans in all tasks with the combination of these cognitive abilities and access to massive memory and data processing capabilities.
When we talk about AI as a tool for divergent thinking and design ideation, we are talking about generative AI. Wait! That wasn’t on either list! Why isn’t generative AI in one of the major subcategories? Well, only the AI academic complex can answer that one. It’s really only been on the cultural radar since 2022, so we’ll give it a minute. It’s not really a functionality or a capability, anyway, it’s more a strategy.
The tools that are heavily discussed in the media at the moment, and the ones that are exciting for us as designers (Dall-E 2, Midjourney, Chat GPT, OpenAI Playground), are generative AI tools. Generative AI refers to AI that uses deep-learning models to ‘generate’ high-quality text, images, and more using the data they were trained on. They are algorithmic tools that can create new content. Depending on the nature of the machine, they are likely to fall under narrow AI, limited memory AI, or the cutting edge of theory of mind AI. Generative AI is exciting because it can create text, images, and even music that have never been seen before.
The Pros and Cons of Generative AI
Generative AI offers several advantages in the creative design process. It empowers artists and designers to create works that push the boundaries of traditional art forms. It creates personalized and unique outputs by discerning patterns within large datasets. This capability has the potential to enhance business processes, foster personalized customer experiences, and stimulate innovation. AI aids creative professionals by automating repetitive tasks, allowing them to focus on the more intricate aspects of their work. It can generate ideas and solutions to creative challenges, enhancing the quality of content. As a bonus, it’s fun to see what creators are coming up with.
That said, there are plenty of limitations and challenges associated with generative AI. The quality of generated outputs varies wildly. There is a steep learning curve to using these tools effectively. Controlling specific characteristics of the outputs can range from challenging to impossible. Copyright concerns can arise when AI generates works that infringes (or may infringe) on existing copyrights.
Generative AI and the Creative Design Process
The creative design process involves several stages, from defining the problem and conducting research to ideation, design creation, and refinement. Generative AI can have a transformative impact on each of these stages, enhancing efficiency and creativity.
- Defining the Problem and Creating a Brief
AI writing tools can assist designers in defining the problem and creating design briefs. By analyzing data, AI can assist in formulating a clear project scope, objectives, and constraints. AI-driven tools help designers work more efficiently, although the ultimate creative vision relies on the designer’s expertise.
- Researching the Problem and Gathering Information
AI simplifies the research process by analyzing vast datasets, identifying trends, and suggesting innovative ideas. It enhances data-driven decision-making, enabling designers to create content that resonates with their target audience. AI can also suggest improvements in areas like color schemes and layout.
- Ideating Solutions
Generative AI empowers designers by generating creative solutions based on verbal prompts. It has the potential to accelerate ideation, offering fresh perspectives and innovative concepts. AI comprehends the principles of successful designs, aiding in ideation and development. This AI ideation card deck prompts designers to consider all the ways AI could contribute to a design and looks like a fun generative tool.
- Creating Designs
AI enhances design creation by facilitating the rapid generation of concept visuals. It can simulate user interactions and offer tools to predict and visualize user experience. It identifies accessibility issues, ensuring inclusivity for all users. It can automate tasks and facilitate creative problem-solving. Design students who use generative AI strategically create delightfully unexpected work. This is the step dominating the visuals we see coming out of image generators at the moment, and this is only the beginning of the kind of results (which range from wacky to elegant) we are going to see going forward.
- Gathering Feedback from Stakeholders
AI streamlines the feedback process by analyzing user feedback and generating or revising concepts based on that feedback. AI-generated prototypes and visuals assist in effective stakeholder communication.
- Refining the Design
AI aids in rapid design iteration, ensuring designs align with user needs and project goals. It promotes data-driven improvements by analyzing user behavior and content performance. Designers can harness AI’s potential to create exceptional designs that cater to individual preferences and resonate with diverse audiences.
A Smart Collaborator
While generative AI are powerful and controversial tools, in the best case scenarios they complement rather than replace human creativity. The collaboration between human designers and AI tools promises to redefine the creative landscape, driving meaningful and impactful design breakthroughs. Here’s a good list of free generative AI tools you can play around with today.
The integration of generative AI into the creative design process marks a new era of innovation and efficiency. Who has time to be threatened by AI when it has so much to offer us? It empowers designers to streamline tasks, focus on creativity, and explore new horizons. It fosters inclusivity, enhances accessibility, and transforms the creative process for the better at every stage. Or, at least, it can, when used with intelligence and care.
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