- Have you heard of experimental research and prototyping
with emerging technologies?
It's about using technology as a medium to push designs
and the technologies themselves into
new and unexpected directions.
At frog, we do these experiments often in
a lab-like setting we call frogLabs and it's guided by
user-centered design principals as well as market realities.
When we approach a technology we'd like to explore
with prototyping rather than a design concept,
we start by creating a hypothesis about the technology.
This way, like a design idea, we can use it to create
several experiments, technical proof of concepts
that will help validate or disprove the hypothesis.
With frogLabs, these experiments often build a foundation
of categories or themes that help frame what's necessary
to validate or invalidate.
Okay, here's how you can begin putting this into practice
just like we do.
We had a team work through this using
the Explore Technologies worksheet.
And for this example, we chose to use machine learning
as the chosen technology.
Machine learning is the science of getting computers
to act without being explicitly programmed.
Think about the Recommended section on Amazon
or the Watch Next suggestions on Netflix.
So here we have the four components
that will help you prototype.
We've got listing the possibilities
of your chosen technology.
So this is really where you want to push.
This is what we refer to often as lateral thinking, alright?
So, think about maybe the things that you know are currently
being used with this technology and then try to think
of the craziest things you can imagine that you could
use with those.
So, in the case of machine learning,
what if machine learning curated your social media for you?
Not something we see currently, or that we don't know about,
kind of a crazy idea.
What if, instead of it was just Amazon and Netflix,
what if it was all of your TV content?
What if it just did that for you?
What about online learning?
What if it aggregated that and it helped you figure out
what you wanted to learn next or maybe what was related
that you hadn't thought of before?
A recipe generator.
Maybe you don't know to search for chicken stir fry,
you just know you have chicken in the refrigerator
and you've got some vegetables and machine learning
could perhaps just come up with recipes for you.
The next one is hypothesis statements.
So here, what you want to do is pick one of these.
In this case, they chose curation of social media.
And so now, you want to create hypothesis statements.
Hypothesis statements kind of scare some people
if you're not familiar with it from a scientific standpoint.
But really, it's just a simple statement saying,
I think this thing or something can do this.
And so, in this case, we think machine learning could be
trained to recognize images in a content feed.
We think machine learning can be trained
to know your user persona.
So that's like, it would kind of understand that
my user liked a particular type of content
and didn't like another type of content, for example.
And on and on and on.
And so you want to create as many of those
hypothesis statements as possible.
And you can kind of see as we get down to here
it kind of goes out even further.
We think that machine learning can remedy most users'
pain point with images.
So you know how maybe you don't want to see everybody's
pictures of their cats all the time?
Maybe machine learning can help you out with that.
The next thing you want to do is you sort of take
these hypothesis statements and you use your superhuman
power of pattern recognition skills.
We're great at this.
And you categorize them.
So this could be a really fun part,
and it often just naturally happens where people
start really getting into this
and debating like where these might go.
And sometimes it even leads to new hypothesis
and that's okay, don't feel like this has to be linear.
So you take those categories, in this case,
some of these are about aggregating social media content
as they're curating.
Some of them are about identifying the social media content.
Others are optimization, simplification.
So then, taking those categories, think about now,
rubber hits the road, what are you actually going to make
as a proof of concept?
What prototype are you gonna make that's gonna help you
learn about that or validate these ideas.
So in this case, we'll prototype an app that does
identification leveraging image recognition.
We'll create an app that aggregates
the desired feeds all into one.
And we'll create a prototype that optimizes your network
information with your feed so people looking at your content
will actually get some benefit because it'll sort of
figure out, ah, there's somebody that really likes to see
cats but they don't like to see a lot of food,
or what have you.
And so that's how you work through that.
So today, you can't create a user experience without
designing with technology.
If your goal is to experiment with a technology to learn
what might be possible, leverage those four components.
Lateral thinking, hypothesis, categorization,
and proof of concepts
to prototype with emerging technologies.