Einstein Designer — AI-Powered, Personalized Design at Scale
Since the dawn of manufacturing, there’s been a hard limitation on how we develop products. This applies to traditional manufacturing as well as to software development, which is the focus here. The conventional wisdom is that either quality, time-to-market, or cost has to be compromised. Or as the saying goes: “Good, Fast, Cheap. Pick two.”
This truism is rooted in a pre-AI (Artificial Intelligence) world. The power of AI changes the rules of the game.
For example, at Salesforce Customer Success is one of our most important values. That implies that we will not compromise on quality or time-to-market. The challenge is: how can we make it efficient (cheap) to deliver high-quality (good) products at an industry-leading pace (fast)?
One of the most time consuming and expensive aspects of a software project is User Interface (UI) development. While today we have programmatic and declarative solutions for developing UIs, both approaches fall short when it comes to truly changing the rules of the game. It still takes significant time and substantial resources to build good user interfaces. UI development still eats up a significant portion of the product development lifecycle. What would be the fastest possible way to create UI? Not doing it manually at all. But what does that even mean?
The Future of UI
What if we could generate user interfaces automatically? To take it even a step further, what if we could predict UIs?
To research this challenge and discover if AI can be trained with aesthetic values, we founded the UX R&D team at Salesforce. Our initial vision statement:
We radically improve the way to create user interfaces by applying Artificial Intelligence and Machine Learning.
Data Science for Design
In order to train a machine-learned model, we needed to understand the space we are working with. One of the first questions a data scientist will usually ask when it comes to training a model is: “What are the inputs and what kind of outputs are you looking for?”
To develop an intuition for the space and the potential inputs for a model, we kicked off a project called…