How to build a time machine: Frederic Kaplan at TEDxCaFoscariU

Frederic Kaplan, engineer, researcher and entrepreneur, Can we build Google maps of the past? Can we rebuild social networks of hundreds of years ago? How can we design time machines? Frederic Kaplan develops tools to travel not only in space, but also through time, and shows us his project to create a time machine for Venice!

This is an image of the planet Earth.

Looks very much like the Apollo picture
that is very well-known.

There is something different.

You can click on it.

And if you click on it, it can zoom
in almost any place on the Earth.

For instance, this is a bird-eye view
of the EPFL campus.

In many cases, you can also see

how a building looks like
from a nearby street.

This is pretty amazing.

But there is something missing
in this wonderful tool.

It's time.

I'm not sure really when
this picture was taken.

I'm not even sure it was taken
at the same moment as the bird-eye view.

In my lab, we develop tools to travel
not only in space, but also through time.

The kind of question we're asking is,

is it possible to build something
like a Google maps of the past?

Can I add a slider on top of Google maps
and just change the year?

See how it was 50 years before,
100 years before, 1,000 before?

Is that possible?

Can I reconstruct
social networks of the past?

Can I make a Facebook of the middle age?

So can I build time machines?

Maybe we can just say:
no, it's not possible.

Or maybe we can figure it from
an information point of view.

This is what I call
the information mushroom.

Vertically you have the time

and horizontally the amount
of digital information available.

Obviously, in the last 10 years,
we have many many information.

And obviously, the more we go
in the past, the less information we have.

If we want to build something like
a Google map of the past,

or Facebook of the past,
we need to enlarge this base.

We need to make that like a rectangle.

How can we do that?

One way is digitization.

There's a lot of material available.

Newspaper, printed books,
thousands of printed books.

I can digitize all these.

I can extract information from these.

Of course, the more you go in the past,
the less information you would have.

So it might not be enough.

So I can do what historians do.

I can extrapolate.

This is what we call,
in computer science, simulation.

If I take a log book, I can consider

it's not just a log book of a Venetian
captain going to a particular journey.

I can consider it's actually a log book

which is representative
of many journeys of that period.

I'm extrapolating.

If I have a painting of a facade,
I can consider

it's not just that particular building,
but probably it also shares

the same grammar of building
we've lost in information.

So if we want to construct
a time machine, we need two things.

We need very large archives,
and we need excellent specialists.

The Venice Time Machine, the project
I am going to talk to you about,

is a joint project between the EPFL
and the University of Venice Ca'Foscari.

There is something
very peculiar about Venice.

It's that its administration
has been very very bureaucratic.

They've been keeping trace
of everything, almost like Google today.

At the Archivio di Stato,
you have 80 km of archives

documenting every aspect of the life
of Venice over more than a thousand years.

You have every boat that goes out,

every boat that comes in, every change
that was made in the city.

This is all there.

We are setting up
a 10-year digitization program

which has the objective of transforming
this immense archive

into a giant information system.

The type of objective we want to reach
is 450 books a day

that are going to be digitized.

Of course, when you digitize,
that's not enough.

Because these documents,
most of them are in Latin,

in Tuscan, in Venitian dialect.

So you need to transcribe them,

to translate them in some cases,
to index them,

and this is obviously not easy.

In particular, traditional optical
character recognition methods

that can be used for printed manuscript,

they do not work well
on a written document.

So the solution is actually

to take inspiration from another domain.

Speech recognition.

This is a domain of something
that seems impossible could actually

be done simply by
putting additional constraint.

If you have a very good model
of a language which is used,

if you have a very good model
of a document,

of the way they are structured,

these are administrative documents,

they are well-structured in many cases.

If you divide these huge archives

into smaller subsets, where small
subsets actually share similar features,

then there's a chance of success.

If we reach that stage,
then there is something else.

We can extract from
these documents 'events'.

Actually probably 10 billion of events
can be extracted from this archive.

And this giant information system
can be searched in many ways.

We could ask questions like,
who lived in this palazzo in 1323?

How much cost a sea bream
at the Rialto market in 1434?

What was the salary
of a glass maker in Murano?

Maybe over decade.

You can ask even bigger questions
because it will be semantically coded.

Then what you can do
is to put that in space.

Because many of these information
are spacial.

From that, you can do things like
reconstructing this extraordinary journey

of that city that managed to have
a sustainable development

over 1,000 years,

managing to have all the time,
a form of equilibrium in its environment.

You can reconstruct that journey,
visualizing in many different ways.

But of course you cannot understand
Venice if you just look at the city.

You have to put it
in a larger European context.

So the idea is also to document

all the things that work
at the European level.

We can reconstruct the journey
of the Venetian maritime empire,

how it progressively
controlled the Adriatic Sea,

how it became the most powerful
medieval empire of its time,

controlling most of the sea routes
from the East to the South.

But you can even do other things.

Because in these maritime routes,
there are regular patterns.

You can go one step beyond
and actually create a simulation system,

create a Mediterranean simulator

which is capable actually
of reconstructing

even the information we are missing,

which would enable to have questions
you could ask

like if you were using a route planner.

If I am in Corfu in June 1323
and want to go to Constantinople,

where can I take a boat?

Probably we can answer this question
with one or two or three days' precision.

How much will it cost?

What are the chances
of encountering pirates?

Of course you understand
the central scientific challenge

of a project like this one is qualifying,
quantifying and representing uncertainty

and inconsistency
at each stage of this process.

There are errors everywhere,
errors in the document,

it's the wrong name of the captain,

some of the boats
never actually took to sea.

There are errors in translation,
interpretative biases.

And on top of that,
if you add algorithmic process,

you are going to have error
in recognition, error in extraction.

So you have very very uncertain data.

So how can we detect
and correct this inconsistency?

How can we represent
that form of uncertainty?

It's difficult.

One thing you can do is document
each step of the process.

Not only coding the historical information

but what we call
the meta-historical information.

How is historical knowledge constructed?
Documenting each step.

That will not guarantee

that we actually converge toward
a single story of Venice.

But probably we can actually

reconstruct a fully documented
potential story of Venice.

Maybe there's not a single map,
maybe there are several maps.

The system should allow for that,

because we have to deal
with a new form of uncertainty,

which is really new for this type
of giant databases.

And how should we communicate

this new research to a larger audience?

Again, Venice is extraordinary for that.

With millions of visitors
that come every year,

it's actually one of the best places
to try to invent the museum of the future.

Imagine, horizontally you see
the reconstructed map of a given Europe.

And vertically you see the document
that served for the reconstruction.

Paintings for instance.

Imagine an immersive system
that permits to go and dive

and reconstruct
the Venice of a given year.

An experience you could share
within a group.

On the contrary, imagine actually
that you start from

a document, a Venetian manuscript,

and you show actually what
you can construct out of it,

how it is decoded, how the context
of that document can be recreated.

This is an image of an exhibit
which is currently conducted

in Geneva with that type of system.

So to conclude, we can say that
research in the humanities

is about undergo an evolution,

which is similar to what happened
to life science 30 years ago.

It's really a question of scale.

We see projects which are much
beyond any single research team can do.

And this is really new for the humanities,
which are very often

taking the habit of working
in small groups,

or only with a couple of researchers.

When you visit the Archivio di Stato,
you feel this is beyond

what any single team can do

and that should be a joint
and common effort.

So what we must do
for this paradigm shift is actually foster

a new generation of digital humanists
that are going to be ready for the shift.

Thank you very much.

(Applause)