Episode 8

We cover changes that are coming to the UX field because of AI. We look at how design, research, and UX management will all need to adapt to new processes.

Music: The Pirate And The Dancer by Rolemusic

Transcripts

I’ve gotten this question. So the scenario that everyone seems to come up with is: Sure at first it was just the repetitive jobs that got replaced by AI. Then GANs started generating everything. Who needs a designer when a computer can put out 1000 designs a second? Right? Obviously, I wouldn’t be talking about this if I thought this was a problem. Today we are covering how AI will change UX and design This podcast is called design for AI It is here to help define the space where Machine learning intersects with UX. Where we talk to experts and discuss topics around designing a better AI. music is by Rolemusic Im your host Mark Bailey Lets get started music I want to start with an example Everyone knows of deep blue, the chess application that first got everyone’s attention by beating the best chess player. Since then AI has beat the best Go player and can beat anyone at competitive video games. But do you know who has beat the AI systems? Human and AI hybrids. The top ranked chess systems right now that can beat any AI out there are all human-AI hybrids. The human brain and an AI system both make shortcuts. They do them in different ways. They do better filling in for each others weak spots. The best system is always augmented, not replacement. I’m not the only one who thinks this IBM CEO Ginni Rometty recently expressed that “If I considered the initials AI, I would have preferred augmented intelligence.” Now while AI isn’t going to take over UX, or make it obsolete, I do think a lot will change. In the scenario I talked about GANs generating 1000 designs a second. This is actually the case, they can. But automated generation doesn’t mean good. A few years back there was a company that promised to get rid of the need for website design, called The Grid. It delivered underwhelming results. But, you might ask it could get better from another company. Google tried something similar. You may have heard of them testing 42 shades of blue against each other to get just the right blue with the best response rate. That was successful. But when they tried expanding the analytics based design past those very basic items they kept hitting a wall. So why is this the case? To get better AI we need better UX There is mutual benefit on both sides that run in a cycle.

• As AI starts getting used more,
• The ML model produces more data that is useful
• The new model is trained off of that data
• AI becomes more useful
• ML models start spouting up delivering unneeded advice and tasks which just add to confusion instead of solving problems
• The need for a better UX becomes more important
• A better UX is created and refined
• AI gets used more, the cycle starts again.

So if the cycle shows that there is still a need for UX, how will the job itself change. Well just like most other jobs, it is the boring repetitive monotonous parts going away. There will be an automation of design. The part I talked about in the scenario about GAN ML models able to generate 1000 designs a second. That already exists. The new UX designer will become more of a curator instead of a generator if designs. This has already started to happen as the design tools get better and UX design matures anyway. There is no reason to redesign the same widgets over and over for an entire career. The need for systems designers is evidence of this. They design a component once and it is used and customized by everyone using that system. The same should hold true. I’m going to talk about this from the three main areas that have to do with design: Design, Research, and Management

Design

For the designs you do create, nothing new here, but concentrate on empathy. The problem with ML models is understanding humanness. How can you figure out for the app to react to users current context and mood? Some of the cutting edge research right now is called ensemble models. The basic idea is that ML models are good at doing one thing well, so if you take a bunch of models and put them together and add another model to make a decision for which model to use it creates a more robust experience. This is what needs to be designed. Every change in context is going to need a different ML model. As part of knowing the context, I covered previously to know when to tell jokes. That depends completely on context. ...