The first step when developing AI is to understand the user need; but just as critical, is knowing the context in which the data is being collected.
The bold future of UX: How new tech will shape the industry
Part 3 ∙ The critical component missing from AI technology
In our last post on artificial intelligence (AI) , we discussed the three pillars that AI needs to consider to be successful: context, interaction, and trust. In this post, we will dive deeper into the idea of context.
It’s no secret that AI is a hot topic in virtually every industry; how to apply it, how it will advance the industry, how it will improve the experience for the customer. It was a major topic at the 2018 Consumer Electronics Show (CES), and articles that either expound the virtues of AI or predict that it will be humankind’s downfall are in the popular press on a regular basis. It’s clear that while the opportunities are seemingly endless, there is a critical component missing from much of the AI technology out there: In what context is the data (that allows AI to learn) being collected?
Don’t build it just because you can
When we think of the buzz around AI, we must pause to ensure we are “not building AI just because we can.” While the opportunity is great for efficiency, people will hear this statement and immediately fear for their jobs. But successful manufacturing companies know that the key is striking the right balance between robots and people. The first step is to understand what user need is addressed with the robots. Some examples of this include:
- Safety: There were 991 construction deaths in the US in 2016 and thousands of injuries. How many of these injuries and deaths can be avoided through automation?
- Accuracy: There are over 600,000 bridges in the US to inspect, how much of this can be done with drones?
- Efficiency: AI could increase agricultural farm yields by 70% by 2050
- Convenience: 100 million people will be 80+ years old, what jobs can be done by robots to improve quality of life
What’s missing, and is currently doing a disservice to AI, is context. Around Valentine’s Day, a story came out where AI was asked to come up with new Valentine’s Day candy heart messages. But without context, it produced quite a few messages that would confuse (and possibly anger) anyone that received them. (I know I wouldn’t want to receive a heart that said “Sweat Poo” or “Stank love”.)
When we build AI tech, there are three stages where context must be considered:
- Before it’s built: Beyond uncovering the user need that the tech will address, we must make sure that the context in which it will be used gets into the AI process. This will ensure we collect the right data.
- During: When the data goes in, it must have context. For example, if you are collecting data on behavior in a car compared to a bedroom or kitchen, it’s clear that the context would be important.
- Using the collected data: Currently, AI is a ‘black box’ – you throw in data and see what comes out. But the user must use AI to do something. If we take a user-centered design approach to how the insight might be used, this is when we will really see how powerful AI can be.
The potential for AI is astounding, and it will likely be one of the defining technologies of the 21st century. However, AI is only going to be as good as the data and information that we feed to it. By providing AI with the proper context for it to advance properly, we are helping to ensure that AI is delivering on its promise of simplifying life for the end users.
What are your thoughts on the idea of context in AI? Start the discussion by leaving a comment below!
The next post in our future tech blog series will move from software to hardware with a discussion around robotics.
This blog post is part three of a series, The bold future of UX: How new tech will shape the industry, that discusses future technologies and some of the issues and challenges that will face the user and the UX community. Read Part 1 that discussed Singularity and the associated challenges with UX design and Part 2 which provided an overview of focus areas for AI to be successful.