Transcriber: Angel Ding Reviewer: Marina Lee
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)