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The Future of Design Thinking, AI & Data Humanization Explained | Takram’s Vision for the Future — Transcript

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0:12I was using some of the technologies and
0:14materials that was still new
0:17and creating, I think, what we called
0:19interactive art. So, this one was a
0:22human-scale structure that you can go in
0:24and it shifted shape in response
0:28to the movement of people inside.
0:30The goal at that time was to critically
0:32communicate this idea, the implications
0:35of this idea, interactive architecture.
0:37What does it mean for you to be living
0:39in a space that is truly or physically
0:42interactive?
0:44Does it feel comfortable? Does it feel
0:46invasive? Or perhaps, does it feel like
0:48an extension of yourself? So, there was
0:50a bunch of kind of more than technical
0:53questions that I had when creating this.
0:55But, the questions from the audience
0:56that I got whenever I put this piece in
0:59an exhibition context
1:00was this.
1:03How does this work?
1:06It's a bit like a curse as a person from
1:08a technical background showing something
1:10that is above technical.
1:13They ask this question,
1:15quickly figure out the sort of algorithm
1:17behind the piece,
1:19they play for a bit, and then they just
1:21leave.
1:24No offense, it's not their fault, it's
1:25100% my fault.
1:28The only way I knew in communicating
1:30technology was just to be
1:33consumed as entertainment.
1:36So, I decided to move to London
1:39to study under Dunne & Raby,
1:41who coined the term critical design. I'm
1:43sure they have been here before.
1:47To be able to communicate and talk about
1:51technological and engineering matters
1:53from a much more sort of humanistic
1:56perspective.
2:00Since then, I've been always practicing
2:02both design and engineering.
2:06Sometimes I design user interfaces while
2:08reading technical papers.
2:10Sometimes I code
2:12while reading, I don't know, things like
2:14object-oriented ontology.
2:19Founded in 2006,
2:22our studio Takram has so many people who
2:25share an experience like mine.
2:28The struggle
2:29or the constant movement between design
2:31and engineering.
2:33No wonder the studio was named as such.
2:3614 years later, we're still operating
2:39dealing with the same struggle.
2:42But we also have expanded our expertise
2:45beyond design and engineering
2:48to incorporate
2:50broader disciplines.
2:52So, we decided to drop design
2:53engineering and started simply call
2:55ourselves Takram.
2:57We not only relocated our studio in
3:00Tokyo three times
3:02due to kind of continuous expansion of
3:05the team,
3:06we also
3:08went beyond Japan. We have one one
3:10studio now in London, which I'm based
3:12in,
3:13another in New York,
3:15and we're hoping that we can open
3:17another studio in China sometime next
3:19year.
3:22As I mentioned,
3:23our expertise have sort of expanded out
3:26of this much more narrowly defined
3:28design engineering innovation world
3:32to include things like business,
3:34spatial,
3:36speculative context, etc.
3:42We choose to design across sectors and
3:44disciplines.
3:46But overall,
3:47our what we design can be grouped into
3:50these three: products and services,
3:52brands and strategy,
3:54features and experimental.
3:58We make camera lenses, mobile apps, cars
4:01and uniforms.
4:03Working at every stage of development
4:05from concept to execution.
4:09We create brands and communication. The
4:11outputs here are more sort of
4:13physical, digital, strategic,
4:16encompassing things like
4:18packaging, identities,
4:20and even sometimes business models.
4:24We also use design to expand our future
4:26possibilities.
4:28Once we've created this
4:30kind of whole new AI voice assistant
4:33interaction
4:35by using Swarovski's crystal
4:38as a sort of future-telling crystal
4:40ball. So, like, Alexa, Alexa, tell me my
4:43future.
4:44While swinging in between these
4:46different disciplines,
4:49we identified a pattern
4:51that allowed us to navigate
4:53seemingly different or sometimes perhaps
4:55even
4:57seemingly completely opposite
4:58considerations in product development.
5:01It's a realization that, you know, like,
5:03everyone talks about combining multiple
5:05disciplines,
5:06but that only works in an environment
5:09where one can truly and constantly in
5:12flux
5:13being able to oscillate between those
5:15disciplines.
5:19So, we call this mindset pendulum
5:20thinking,
5:22referring to the nature of swinging and
5:25switching.
5:26For example,
5:27in design and engineering, it's really
5:29important for us to
5:31keep our design mind and engineer mind
5:34separate
5:36within ourselves.
5:37But, more like kind of moving between
5:40the two seamlessly rather than trying to
5:42combine the two mindsets into one
5:44thought process.
5:48So, in the studios,
5:50we swing our pendulums across
5:52different planes of reference,
5:55like design and engineering,
5:58thinking and doing, like all the design
6:00that the folks are doing,
6:02digital physical,
6:04problem finding and solving,
6:06and data-driven design and design-driven
6:09data,
6:11which is
6:12the segue into
6:14the thing that I'm going to talk a
6:15little bit about today.
6:18Design and data and design.
6:21An area which we think
6:23can be better explored and perhaps in a
6:26more
6:28alternative directions.
6:33The history of visualized data starts at
6:36the 10th century.
6:39As you can see here,
6:42since the beginning of time,
6:44as our ability to measure and collect
6:46data evolved,
6:48so did our ability to interpret it
6:51and visualize it.
6:54Built on top of building on top of these
6:56great precedents,
6:58we have done some kind of visualization
7:01work that is for creating approachable
7:04and actionable data
7:06through the use of design.
7:08So, I'm going to show a little bit about
7:10it.
7:14In 2015,
7:16we were approached by the Japanese
7:18government to collaborate on creating
7:21and exploring data visualization
7:24for their various data sets.
7:27The data that we received was immense.
7:30It covered everything from population
7:33data to corporate activities,
7:36from welfare to the financial state of
7:39local governments.
7:41Even the data about population density
7:43alone was super rich
7:46with granular information about
7:49different aspects of our everyday life.
7:51Things like
7:52change in the population throughout the
7:54day, change in the number of commuters
7:56throughout the day, etc.
7:59But the problem was
8:02the policy makers
8:04had to heavily rely on data scientists
8:07to interpret it and make sense of it
8:09all.
8:10So as designers who have been kind of
8:13bridging the gaps between different
8:14fields, we thought this was a challenge.
8:16This is This was an opportunity for us
8:19to try and connect policy makers to data
8:23allowing them to swing their own version
8:24of pendulum of
8:27between being a policy maker
8:30and a data scientist.
8:34The first thing we did in the project
8:36was quite straightforward.
8:38We sort of
8:39prototyped a sort of simple version of
8:42data visualization framework platform
8:45which can eat and map almost most of the
8:49types of data that we have been
8:51provided.
8:52And sat together with policy maker and
8:54data scientists so that we can sort of
8:57mix and match mix and match different
8:58data sets and show in front of us.
9:01And discuss together
9:02what kind of the most effective ways to
9:05combine the data sets, present side by
9:08side,
9:10and also the most effective ways to
9:12present each of them.
9:15The resulting prototype is called
9:17Recess.
9:21>> Recess is the world's largest
9:22visualization system for nations'
9:25economic big data.
9:27Its approach to this advanced challenge
9:29has resulted in a highly valued
9:31leading-edge project.
9:35In order to develop and create this
9:37complex
9:38and design within a limited amount of
9:39time,
9:40prototyping was an essential part of the
9:42process,
9:43which enabled efficient hypothesis
9:45testing.
9:47Through this trial and error, we
9:48developed the prototype's beautiful but
9:50coherent visualization of big data.
9:55The Japanese economy today, particularly
9:57in the regional economy,
9:59faces severe structural issues caused by
10:01population decline along with other
10:03social factors.
10:08We propose that this prototype
10:10contributes to the development of the
10:12regional economy
10:13by providing small and medium-sized
10:15businesses with valuable insights from
10:18nationwide data analysis and
10:20visualization.
10:23Sophisticated both aesthetically and
10:25functionally,
10:26Recess is engineered with low-latency
10:28rendering capability and interactivity.
10:33Using the development and regional
10:35economy as one example,
10:37this prototype proves that design and
10:40data visualization techniques can
10:42positively contribute to socioeconomic
10:44issues.
10:54>> As mentioned in the video, Recess
10:56enabled policy makers to really sort of
10:59explore government data to generate new
11:02insights.
11:04For example,
11:05if you map the movement of population in
11:07each prefecture like this,
11:09then you start to understand some of the
11:11key elements
11:13that attract people to move and start
11:15brainstorming around
11:17that aspect of knowledge.
11:21If you map the supply chain of Toyota,
11:23then you can start seeing some of the
11:25places that Toyota is dependent upon
11:28and start thinking about how to mitigate
11:30the risks of adverse pressures on those
11:33areas.
11:37If you map how different industries are
11:41dependent interdependent upon each
11:42other,
11:44then you can sort of start assessing the
11:46resilience of certain areas that you're
11:48interested in.
11:50So, all of these dynamics became
11:52intuitively understandable through the
11:55power of visual visualizations.
12:00It really sort of became an x-ray
12:03scanner for policy makers that connects
12:05policy making with real-world data.
12:08As a matter of fact, even today, Recess
12:11is used by different departments of the
12:13national government as well as local
12:16government representatives.
12:22The power of intuitive understanding
12:24through visuals were also allowing them
12:27to refer to the actual data
12:30gave users Recess
12:32the ability to iterate upon their
12:34hypothesis.
12:36They can quickly build and test
12:39their intelligence models that are
12:41linked to the real-world data.
12:48While this project Recess gave us an
12:49opportunity to explore data on a
12:52governmental level,
12:54we're also interested in
12:56everyday like sort of all the how an
12:58ordinary people interact with data in
13:00their day-to-day life.
13:03Unlike political decision makers or data
13:06scientists,
13:07consumers like us
13:09are not typically specialists in the
13:11types of data that we're dealing with.
13:15Or moreover,
13:17they often misinterpret or misunderstand
13:20the significance or the implications of
13:22it.
13:24That was the motivation behind this
13:25self-initiated project that I'm going to
13:27talk about, which explores the
13:29relationship between data
13:32and personal health.
13:34We targeted cyclists cycling, which is
13:37one of the which is said to be
13:39apparently one of the most data
13:41intensive activities on the planet.
13:45The data would include things like
13:46geolocation,
13:48speed, cadence, ascent, descent,
13:51heart rate, etc.
13:55Through the research,
13:56it became clear
13:58that many of the casual cyclists like
14:00myself,
14:02who have
14:03who use GPS trackers and other smart
14:06devices, were all overwhelmed by the
14:08amount of data available to them.
14:12Perhaps sometimes they are useful and
14:14helpful,
14:15but they don't really give you kind of
14:18new insights or in-depth understanding
14:20of how you are performing.
14:24So, we decided to focus on designing
14:26data around people, not the other way
14:28around. It's nothing to do with making
14:31data look prettier. It's designing
14:34data around you.
14:37The result resulting product is called
14:39Otto,
14:40a data tracker and presenter that fits
14:43into people's everyday life.
14:51>> It's really great to have much more
14:54balanced level of information. Something
14:56that simplifies things can just gives
14:59you enough to keep going and get better
15:02without becoming distracted.
15:06>> It doesn't give you numbers, but it
15:08gives you more like overview of
15:10different aspects of your health. Before
15:14going to a ride, I I use Otto because it
15:17gives me several tips, not only weather
15:19and nutrition, but also hydration tips.
15:22So, you're informed of how much water
15:24you will need through the ride.
15:28After I ride it, Auto tracks my
15:30nutritional values and it's linked to
15:33some of my personal recipes, so I would
15:36know what's the best food to eat to
15:39refuel myself.
15:43It hasn't changed my daily routine, but
15:46it's now it's just part of my daily
15:48life.
16:15>> So, we started with a simple humble
16:17bathroom scale.
16:18And Auto is an integrated monitoring
16:21device to accompany you accompany the
16:23user
16:26and present their own data narrative,
16:29per se, in an engaging, inspiring, and
16:31more personal way.
16:35It provides information when and where
16:38people would need to interact with it.
16:42While it seamlessly blends into blends
16:44the data interactions into everyday
16:46life,
16:48it also presents in a much more leaner
16:50format.
16:52So, the the concept of the word lean
16:54data or data diet
16:57were leading the project.
17:01What became clear is the importance of
17:04context when it comes to understanding
17:05something.
17:07We humans heavily rely on context, as we
17:09all know,
17:10to understand anything and everything.
17:13If you wanted to go on a diet,
17:16you'd probably concerned about it when
17:17you're on a bathroom scale,
17:19right?
17:21But also equally,
17:22you'll be concerned about it when you're
17:24in your kitchen preparing your next
17:26meal.
17:28That's also how Auto helps you
17:31in terms of presenting data and
17:33information needed for specific context.
17:36So, the data needs to be obtained and
17:38presented
17:39with a context in form informing how and
17:42when people can act upon it.
17:48Another area we thought to be
17:49interesting to pursue with research
17:53was to make it more personal and
17:55relatable experience. So, we captured
17:57the entire skyscape using the flight
18:00path data available online
18:02and translated it into a virtual reality
18:05experience.
18:07So, this is a video
18:09from
18:11the exhibition that we self-organized in
18:13back in 2016 for London Design Festival.
18:17So, you wear these gloves
18:19and you wear these
18:21Google VR headset
18:23and you literally pinch in to zoom in to
18:26reveal the details of each airplane, one
18:29of the one of these airplanes that are
18:31flying above you in the virtual space.
18:36And these gloves got a haptic feedback
18:38for pinching and zooming in that's got a
18:40little bit of physical tick
18:42so that you can feel
18:44what you're doing in even in your
18:46virtual space.
18:48With this, people could interact with
18:50the data in this case,
18:52the planes flying around you,
18:55directly via hand gestures.
18:58You could almost stand at your home
19:00address, look above
19:02to see how many planes would be flying
19:04above your hometown
19:07if, say,
19:08a new runway would be to be built in a
19:11nearby airport.
19:13And that kind of experience can be
19:14really really strong strong trigger for
19:17someone to act upon it.
19:19Because it's super personal.
19:23The insight here
19:25is that technologies like VR can
19:28translate data into more personal human
19:30scale experiences.
19:33We could literally think and feel the
19:36data
19:37fully utilizing our special awareness.
19:41Which we thought was super powerful way
19:43when it comes to interacting with data
19:45and relating to it.
19:49Bit of a pause.
19:51Looking back what we have presented so
19:53far.
19:58We've experimented at different levels.
20:01Governmental, personal,
20:03virtual reality.
20:05But these things, hypothesis building,
20:07testing, contextual understanding,
20:08special awareness.
20:12These experiments are basically
20:14an attempt
20:16to transform data into something more
20:19accessible
20:21and relatable
20:23and actionable.
20:25But the most important thing here
20:27is that by doing so
20:29it creates a more room, a more space for
20:33human ability, human capability, and our
20:35thinking to kick in. Our idea starting
20:38to interact with data.
20:41That is the most important thing and
20:42then that is what
20:45we believe
20:46is the true potential of what people are
20:49referring to as data humanization.
20:55Data has become an important human
20:56artifact. It has become a subject of
20:59design.
21:00When fully utilized, the combination of
21:03data and design can enable different
21:05capabilities of people and empower them.
21:08It's not just a way to present or
21:11communicate, but it's a way to adopt
21:13data truly as a tool for people
21:16to observe, think, and act.
21:23With data humanization concept in mind,
21:27when we got this offer from Ravi,
21:30we decided to create another prototype
21:33for Design Deba for today.
21:35So, it's a sort of world premiere-ish
21:37um occasion. It's a sneak preview.
21:41So, I'm going to show the prototype
21:43that we've been work that we've been
21:44working on in the in the last couple of
21:46months.
21:49Right. So, the
21:51obviously, there isn't a single day that
21:53passes without talking or at least
21:57without hearing about climate crisis,
21:58climate change, and sea level rising.
22:06There's so much data available about
22:09climate change
22:11and sea level rising.
22:13We also roughly know how the different
22:16aspects
22:18of our life accelerate this global
22:20crisis.
22:21And yet, we fail to put them to action.
22:28What if we can evaluate the different
22:31decisions and actions we make every day
22:34and see the impacts that they have on
22:36our environment around us?
22:41And the prototype is called Rise AR.
22:44It's an AR experience, augmented reality
22:46experience, that questions our thoughts
22:49and actions regarding climate change,
22:52and shows its impact.
24:48>> So, he asks it's enough to ask you a set
24:51of questions related to your day-to-day
24:53activities, and you will answer to them
24:56with a super simple approachable almost
24:58kinderish left and right
25:01gestures.
25:03And depending on your answer,
25:05the virtual water level rises or falls.
25:09So with the apps where we are using
25:11filming, um I found
25:15our studio to be going completely under
25:17water as you saw.
25:18And so maybe we can
25:21think about next time we come to Design
25:22Daba, maybe we take trains
25:26or at at least we can take um
25:29climate uh carbon offset program when we
25:31are flying. Maybe something that we can
25:33talk about later.
25:38So,
25:40I'd like to demonstrate this app because
25:42it's a mobile phone app and then we
25:44created for this occasion. So, why not
25:46try now? And uh you must have been
25:48wondering what this
25:50ping pong bat has been all about.
25:53So,
25:55and um
25:58so we will be doing a demo on stage and
26:01who's going to be answering those quiz?
26:04Everyone.
26:06So, for doing that, I'd like to welcome
26:09my colleague Ken Fujiyoshi to the stage.
26:18So,
26:19Ken is going to be holding a phone. It's
26:21just a sort of gimbal that we had device
26:23that he's holding so that he has a less
26:25to go.
26:27And uh he's going to do all the Tinder
26:29swipe and answering on behalf of all of
26:31us.
26:33And yeah, we are answering by using this
26:36bat. So, you have one side in red, one
26:39side in yellow.
26:41And as you saw in the film, you might
26:42you might have not noticed because of
26:44the sort of quality of the film image,
26:46but
26:47always on the left hand side answer is
26:49colored in red and the answer on the
26:52right hand side is always on yellow,
26:55sorry. The other way, sorry.
26:58So, you tell me your answer by showing
27:02me the color of the answer.
27:05So, you ready again? Good.
27:07Cool.
27:10All right.
27:12So,
27:13who wants to save us from drowning?
27:17Oh, yes.
27:19Yay.
27:22Don't want to be drowned. Help me,
27:23please, right? Um how did you get to
27:26Design Indaba?
27:29Rail coach
27:31or plane?
27:34I think it seems we are all on plane. We
27:36are all guilty.
27:38Maybe except for the virtual
27:41participants who are in the SimCast
27:44locations. It cuts off 80% of the carbon
27:47emissions from the entire travel. That's
27:49great thing to do.
27:51What What What's your dinner last night?
27:53Was it more like beef burger? Was it
27:56more like veggie burger?
28:00I was on veggie.
28:02It's really hard. I think
28:05uh
28:06I have to say with a great regret, it's
28:09a red.
28:11Ah.
28:13Save me.
28:15Save me. It's too early. It's just two
28:17questions questions out of 10. We've got
28:20loads of more time.
28:22What transport did you use to go about
28:25in Cape Town? Is it mostly public
28:27transport, walking,
28:29or taxi all the time?
28:33Serious.
28:35Serious.
28:37Oh my god.
28:38Right, it's taxi all the time, all
28:40right. I think we are doomed.
28:45But it depends, maybe taxi might have
28:47been electrified if the electric
28:49electricity was generated with renewable
28:51sources, then it might be okay.
28:53Did you carbon offset your flight to
28:55South Africa?
28:56If you are from South Africa, your last
28:58last flight.
29:01Never considered.
29:06Great, some yellow I can see, but that's
29:08Never considered. Although
29:11carbon offsetting program has said to be
29:14completely No, not really, kind of it's
29:16maybe better than doing nothing, but it
29:17takes 25 years to pay start to pay off,
29:20right? So, something to note.
29:23Where are all your old smartphones now?
29:26Are they chucked away in a drawer or
29:29someone else's pocket or in the
29:31recycling center?
29:34Good.
29:35Good. I think we are yellow.
29:38Someone's pocket.
29:41Cool.
29:42We slightly
29:44Make sure that we we answer all the
29:46questions before the water level
29:48hits the ceiling, right? And then it's
29:49going to You can see some kind of
29:52theater chairs flowing and bashing into
29:54me probably.
29:56When you prepare food at home, how do
29:59you dispose food waste?
30:05Separate organic bin. That's me.
30:09I'm just obsessed with it.
30:11All right. I think we are I can I think
30:14I can say red. Separate organic bin.
30:16Yay! Woo!
30:23Let's go quickly. You want to fly to the
30:25Arctic. Go before it's too late
30:29or stay home for the future's sake. It's
30:31a trick question.
30:37Well
30:40Mhm.
30:42difficult. It's meant to be sparring
30:44discussions, right? We need to start
30:46talking about these tricky questions.
30:49I think I'm going.
31:01Your friend is not having a child for
31:03the planet.
31:04What would you say to them?
31:06Great. Then you can adopt children then.
31:10Or
31:11great activism. You can spend all your
31:13time in advocating acting.
31:16What do you say?
31:24I think we are on red.
31:27The The thing is there's no shaming in
31:30people who are acting or not acting.
31:33There's no blaming or shaming. So both
31:36choices in the last question were
31:38completely same result.
31:42So we are doing okay. I'm still I think
31:44if I jump then I can survive. How are
31:47you contributing to Design Indaba going
31:50carbon neutral?
31:52Are you going to be virtual participant
31:54from next year?
31:56Or you coming and still eat vegan?
32:01Yeah.
32:03There's something special about this
32:05event.
32:08Yeah.
32:14So the last question.
32:15You were asked to reduce your carbon
32:17footprint. Where do you start?
32:20Easy. Organic everything, right? Or
32:24no idea. We need to research first.
32:32We are on yellow. No idea. We need to
32:35research first.
32:38And the result is
32:41Correct.
32:43Yeah.
32:51All right.
32:54Cool.
32:56Thank you again the amazing performance,
32:58amazing kind of camera movement and
33:01swiping. Thanks again.
33:07And if any of you
33:10were wondering
33:11about where these parts are going, these
33:15puddles are going.
33:17Design Indaba team made did amazing job.
33:19So, they're going to be donated to the
33:21charity, to the local charity and then
33:22the plan is that they're going to build
33:24and design a cladding in a primary
33:27school
33:28in Cape Town. A huge applause to Design
33:32Indaba team.
33:44A final note.
33:46What I've shown you today is what we can
33:49do beyond the creating understandable
33:51data or perhaps understanding vision and
33:54perhaps eloquence, too.
33:56And how we can unlock human capabilities
33:59by transforming data and vision into
34:01more accessible,
34:03relatable, and actionable experiences
34:06through the power of design
34:08and the power of
34:10events like this.
34:13Some of
34:14the things that we experimented here, um
34:17we introduced here, are listed here.
34:20All of them are stemming from our innate
34:22ability
34:23to imagine something alternative
34:26and make connections between distant
34:28observations.
34:31Data is a form of externalized
34:33knowledge.
34:35To make it really work with humans,
34:37we designers need to embrace
34:40and experiment how data can truly inform
34:44and empower actions.
34:47We will work further on data
34:49humanization projects and I hope that
34:51many of you will join us in this
34:54exciting experiment.
34:56Thank you for listening.

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