This is an image of the planet Earth.
It looks very much like the Apollo pictures
that are very well known.
There is something different;
you can click on it,
and if you click on it,
you can zoom in on almost any place on the Earth.
For instance, this is a bird's-eye view
of the EPFL campus.
In many cases, you can also see
how a building looks from a nearby street.
This is pretty amazing.
But there's something missing in this wonderful tour:
It's time.
i'm not really sure when this picture was taken.
I'm not even sure it was taken
at the same moment as the bird's-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 Google Maps of the past?
Can I add a slider on top of Google Maps
and just change the year,
seeing how it was 100 years before,
1,000 years before?
Is that possible?
Can I reconstruct social networks of the past?
Can I make a Facebook of the Middle Ages?
So, can I build time machines?
Maybe we can just say, "No, it's not possible."
Or, maybe, we can think of 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 much information.
And obviously the more we go in the past, the less information we have.
If we want to build something like Google Maps of the past,
or Facebook of the past,
we need to enlarge this space,
we need to make that like a rectangle.
How do 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 will 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 is 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 buildings where we lost any 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'm going to talk to you about,
is a joint project between the EPFL
and the University of Venice Ca'Foscari.
There's something very peculiar about Venice,
that its administration has been
very, very bureaucratic.
They've been keeping track of everything,
almost like Google today.
At the Archivio di Stato,
you have 80 kilometers of archives
documenting every aspect
of the life of Venice over more than 1,000 years.
You have every boat that goes out,
every boat that comes in.
You have 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 can be digitized.
Of course, when you digitize, that's not enough,
because these documents,
most of them are in Latin, in Tuscan,
in Venetian 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 method
that can be used for printed manuscripts,
they do not work well on the handwritten document.
So the solution is actually to take inspiration
from another domain: speech recognition.
This is a domain of something that seems impossible,
which can actually be done,
simply by putting additional constraints.
If you have a very good model
of a language which is used,
if you have a very good model of a document,
how well they are structured.
And these are administrative documents.
They are well structured in many cases.
If you divide this huge archive into smaller subsets
where a smaller subset actually shares similar features,
then there's a chance of success.
If we reach that stage, then there's something else:
we can extract from this document events.
Actually probably 10 billion events
can be extracted from this archive.
And this giant information system
can be searched in many ways.
You can ask questions like,
"Who lived in this palazzo in 1323?"
"How much cost a sea bream at the Realto market
in 1434?"
"What was the salary
of a glass maker in Murano
maybe over a decade?"
You can ask even bigger questions
because it will be semantically coded.
And then what you can do is put that in space,
because much of this information is spatial.
And from that, you can do things like
reconstructing this extraordinary journey
of that city that managed to have a sustainable development
over a thousand years,
managing to have all the time
a form of equilibrium with its environment.
You can reconstruct that journey,
visualize it 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 worked at the European level.
We can reconstruct also 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 us 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 chance 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 step 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 processes,
you're going to have errors in recognition,
errors in extraction,
so you have very, very uncertain data.
So how can we detect and correct these inconsistencies?
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 large audience?
Again, Venice is extraordinary for that.
With the 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 year,
and vertically, you see the document
that served the reconstruction,
paintings, for instance.
Imagine an immersive system that permits
to go and dive and reconstruct the Venice of a given year,
some 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 from 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 to undergo
an evolution which is maybe similar
to what happened to life sciences 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 very often take 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 this shift.
I thank you very much.
(Applause)