by Pietro Casella - This is the original Memo later published in the DDVC newsletter.
When ChatGPT was released, I remember feeling mesmerized by the blinking cursor, the embodiment of one of the most remarkable inventions humans have ever made. For a moment, I suspended disbelief, wondering what was unfolding on the other side. I attempted to anticipate its responses, treating my subsequent questions like a strategic chess game, seeking its limitations. Yet, there was an elegance in this pulsating square. Like a pause in a conversation, it represented the AI pondering its response. This simple choice was as effective and theatric as Kubrick's use of a red lamp to depict HAL's physical existence.
As the landscape evolved, I reflected on the new shapes for the AI that software creators chose to intuitively convey its power. This involved the search for metaphors, mental models, and representations, as well as concealing its flaws and embracing the exploratory nature of it all. Creators making choices about their desired version of the future, and investors reassessing their beliefs.
As with every software advancement, the prevailing interaction models are yet to be defined. However, it's crucial to understand the impact of these choices, their significance, viability and evolution. It's equally important to observe the evolving scaffolds and foundations around them, to comprehend the path of least resistance - the path that technology will likely follow.
Designing the user interaction experience with AI in your application is similar to User Experience design, but with an additional cognitive dimension. Interacting with AI is often like negotiating with a human; you express ideas and anticipate responses. Your internal dialogue processes this interaction and plans, while your emotional response reacts and guides you. However, it can also be like interacting with a sophisticated machine, mechanical and predictable but doing things previously only expected from humans. You press a button, and it generates a paragraph or performs a task for you.
Designing in this spectrum, the AI interaction experience involves a series of choices. It starts with an innovative concept that encapsulates all your experiences and preferences, or the decision to apply a metaphor you've observed to see if it "fits the bill". Early AI applications focused on conversational UX, an effective metaphor which now seems basic as new approaches are emerging.
I've noticed that while some choices are logical, others are like trying to fit a round peg into a square hole. They lack the appropriate AI-problem fit. Even though they might still be intriguing and exciting, crafting the AI-experience will require more.
A common issue with AI-experience design arises from the gap between the application designer and the user. The designer comprehends the AI's mechanics, while the user might be encountering these features for the first time. The necessary affordances haven't been created yet. Consequently, users, unaware of the backstory, perceive AI as a new device, influenced only minimally by its stated purpose. They strive to comprehend it and learn how to use it effectively, which requires adjusting their thinking to master its operation.
The classic user experience essentially involves interacting with visual artifacts that encourage engagement. Through this interaction, users try to create a mental model, often drawing comparisons to their previous experiences. The goal is to complete tasks efficiently; the faster the task is completed, the better the user experience.
Mental models represent a user's understanding of how the software functions, its degrees of freedom, and its responses to inputs. When new technology is introduced, it often feels like a complex, puzzle box waiting to be explored. There's a tension between the developer's desire to explore innovative features and the user's need to understand how it works.
What are then examples of emerging mental models in AI applications? which problems do they solve and what do they imply for the future? I will dive into that in the following sections.
AI models have latency, meaning the time it takes to perform a task is perceived as slow for most usecases. While great leaps are being made in performance for example with faster machines, faster models or other techniques, there will be plenty of application opportunities to use more AI. Cost will also be a consideration.
So how do we manage wait time in a better way? A few clever UX models are emerging such as the “type” effect in conversational ux as a way to manage expectations or the (is typing…) present in most real time applications.