My first assignment when I joined Watson Design in 2015 was to revamp a series of interactive demos to show the developer world what could be done with Watson technology. Each Signal Service is good at doing one or two things. AI happens when a few of them are used together and set loose among loads of unstructured data.
The business looked to these demos to drive customer acquisition and retention. But in context, thanks to the design team's push for user research, and developer colleagues who corroborated our insights with their daily experience, we uncovered a broader strategic need to provide a solid developer user experience first. Furthermore, in some cases, services would need to become products in order to set up customers for success.
In total, I ended up working on 7 Signal Services, which can be organized into these [human] cognitive capabilities:
Each service involved working with a new, remote team of research scientists, engineers, product managers, and salespeople. I led small teams of designers and front-end developers locally, out of IBM Design in Austin. Sometimes, I facilitated design thinking workshops between product teams and special customers (we call them sponsor users), to break through alignment plateaus, or pivot.
I worked on the beta and general availability of Tone Analyzer, a service that analyzes human affect, but takes personality and context into account.
This is one of the more popular Signal Services, because of the way it could transform certain industries in the near future. I facilitated a design thinking workshop between customers and the research and product teams to come up with "recipes" of tone, emotion, and behaviors associated with personality traits for customers to leverage in their businesses.
I designed developer resources meant to show the breadth of what could be done with multiple Watson services. With this one product in particular I influenced the broader strategy for the developer experience, strategy on Conversation, and even signalled ethical risks in providing "agnostic" developer resources such as these. This was the subject of a talk I gave at the 2016 O'Reilly Design Conference.
The below demo used Tone Analyzer, Personality Insights, and Natural Language Understanding within the context of a support bot handling complaints people tweet at a company account. It demonstrated how Watson could use people's tone of voice to decide whether to respond with an automated (but personalized) message or not, and use personality for advertising purposes.
The Personality Insights service predicts personality characteristics, needs, and values from user-generated text. It is geared towards businesses interested in understanding customer habits and preferences individually and at scale. In conjunction with one or two other data points, they can even start predicting behavior. This service is also fairly popular, and as such it was important for me to be accurate, particularly due to a few things that the service measures.
Working with the Federal Government, I ran into people who had worked on presidential campaigns. They described their work similarly to the marketing customers we targeted with this service. In my clinical psychology work, I had also encountered research looking at personality, values, and political leaning.
A couple of friends and I threw an app together for the election. It analyzed a person's Twitter posts, generated a personality, values, and behavior profile, and attempted to predict who they would vote for. We created a profile for presidential candidates, based on everything from stump speeches to debates, to emails. But after we started seeing the results, we decided not to pursue the concept further because we thought it wasn't working. We kept getting too many Trump vote predictions. Yep.
These services enable users to start a system for processing unstructured data, give it some guidelines on how to form connections, and allow the system to run with it.
These services enable end-users to deal with information overload.
This demo showing how Watson's long-tail search technology could change the way people find information. We used Stack Exchange Travel as the relatable example, which was one of the services that evolved into a product called Watson Discovery.
This service explored several visualizations for choosing between several choices.
Looking back on my work on this team, I noticed a push and pull in my work between trying to demonstrate technology from the machine's viewpoint, and trying to relate it to how humans acquire and exercise those capabilities themselves. I enjoyed using my psychology background to form analogies between the signal services and how people (including developers) understand them. It not only helped me give direction to other designers on the team, but it helped forge a relationship between the academic R&D teams and design.