The 6 AI opportunities for right now
By: Ben Ralph
Tags: UX, Artificial Intelligence, Learning, Publishing, Product Experience

2. Better understand what your users want

In life to be a strong communicator you need to be as good if not better at listening than you are at talking. Traditionally computers have been designed to be better and faster at displaying output (talking) over improving how fast they are at receiving and analysing input (listening). To use a simple example, from very humble beginnings we now have every different sized screen imaginable from wrist to billboard, but keyboards are still essentially the same as they have always been.

So how do we make our computers better listeners?

Think about a human-to-human conversation where one person asks the other what the weather is like.

Emma: “Hey, I was thinking about going for a walk, what’s it like outside?”

John: “It was cold when I was out there before but you probably won’t need an umbrella.”

Now let’s think about what that conversation might look like with a computer.

Emma: What is the weather?

Computer: What is your postcode?

Emma: 3011.

Computer: What is your suburb?

Emma: Footscray.

Computer: The weather today will be between 12 and 18 degrees

Computer: Medium (50%) chance of showers in the eastern suburbs this morning, slight (30%) chance elsewhere. A dry and mostly sunny afternoon to follow. Winds west to southwesterly 15 to 20 km/h tending south-east to southwesterly 15 to 25 km/h in the morning then becoming light in the evening.

Computer: Would you like a 7 day forecast?

Comparing the two, if the goal is to know if it is a nice time to go for a walk, you can see there are strengths and weaknesses in both. The exchange between Emma and John was much shorter, more friendly and included much more contextually relevant information. John intuitively understood the location (where Emma is likely to walk), and that Emma didn’t need an exact temperature but just a sense of cold/not cold and if an umbrella was needed. On the flip side, John was limited in a few key ways. First, his personal understanding of what cold is and isn’t; second, his information was old (it might have changed since he was outside), and lastly it is impossible for him to know what the chance of rain is.

In the exchange between Emma and the computer, the computer was a little slow off the mark. First needing to determine the location and then offering generic temperature information. Emma would need to extrapolate herself if it would be a good or bad idea to take an umbrella, and if the temperature would be pleasant for a walk, but the obvious advantage was that the information was accurate or at least as accurate as predicting the weather can be.

Using AI to better capture a user’s intent is about trying to merge these two interaction types to get the best of both. Allow Emma to ask the computer, ‘Hey, I was thinking of going for a walk, what’s it like outside?’, then allowing the computer respond. ‘A walk down by the Maribyrnong River might be nice. You won’t need an umbrella, but a jumper would be a good idea.”

Impossible?

Our devices already know many personal things about us and our habitual behaviours.

In this example the computer could determine location based on GPS, her top few walking routes and speed (based on fitness data) as well as, analyse a combination of temperature, wind, humidity, sun, chance of rain and other factors to give an accurate prediction of what Emma should wear rather than just what the temperature is.

In a learning context, there are many opportunities to be more proactive and smart in learning what a user wants to know rather than just offering generic one-size-fits-all information.