Frederic Kaplan: How I built an information time machine

78,570 views ・ 2014-01-09

TED


Please double-click on the English subtitles below to play the video.

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This is an image of the planet Earth.
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It looks very much like the Apollo pictures
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that are very well known.
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There is something different;
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you can click on it,
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and if you click on it,
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you can zoom in on almost any place on the Earth.
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For instance, this is a bird's-eye view
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of the EPFL campus.
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In many cases, you can also see
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how a building looks from a nearby street.
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This is pretty amazing.
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But there's something missing in this wonderful tour:
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It's time.
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i'm not really sure when this picture was taken.
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I'm not even sure it was taken
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at the same moment as the bird's-eye view.
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In my lab, we develop tools
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to travel not only in space
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but also through time.
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The kind of question we're asking is
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Is it possible to build something
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like Google Maps of the past?
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Can I add a slider on top of Google Maps
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and just change the year,
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seeing how it was 100 years before,
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1,000 years before?
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Is that possible?
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Can I reconstruct social networks of the past?
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Can I make a Facebook of the Middle Ages?
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So, can I build time machines?
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Maybe we can just say, "No, it's not possible."
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Or, maybe, we can think of it from an information point of view.
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This is what I call the information mushroom.
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Vertically, you have the time.
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and horizontally, the amount of digital information available.
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Obviously, in the last 10 years, we have much information.
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And obviously the more we go in the past, the less information we have.
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If we want to build something like Google Maps of the past,
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or Facebook of the past,
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we need to enlarge this space,
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we need to make that like a rectangle.
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How do we do that?
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One way is digitization.
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There's a lot of material available --
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newspaper, printed books, thousands of printed books.
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I can digitize all these.
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I can extract information from these.
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Of course, the more you go in the past, the less information you will have.
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So, it might not be enough.
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So, I can do what historians do.
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I can extrapolate.
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This is what we call, in computer science, simulation.
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If I take a log book,
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I can consider, it's not just a log book
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of a Venetian captain going to a particular journey.
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I can consider it is actually a log book
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which is representative of many journeys of that period.
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I'm extrapolating.
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If I have a painting of a facade,
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I can consider it's not just that particular building,
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but probably it also shares the same grammar
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of buildings where we lost any information.
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So if we want to construct a time machine,
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we need two things.
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We need very large archives,
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and we need excellent specialists.
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The Venice Time Machine,
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the project I'm going to talk to you about,
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is a joint project between the EPFL
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and the University of Venice Ca'Foscari.
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There's something very peculiar about Venice,
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that its administration has been
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very, very bureaucratic.
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They've been keeping track of everything,
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almost like Google today.
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At the Archivio di Stato,
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you have 80 kilometers of archives
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documenting every aspect
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of the life of Venice over more than 1,000 years.
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You have every boat that goes out,
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every boat that comes in.
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You have every change that was made in the city.
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This is all there.
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We are setting up a 10-year digitization program
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which has the objective of transforming
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this immense archive
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into a giant information system.
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The type of objective we want to reach
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is 450 books a day that can be digitized.
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Of course, when you digitize, that's not enough,
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because these documents,
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most of them are in Latin, in Tuscan,
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in Venetian dialect,
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so you need to transcribe them,
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to translate them in some cases,
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to index them,
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and this is obviously not easy.
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In particular, traditional optical character recognition method
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that can be used for printed manuscripts,
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they do not work well on the handwritten document.
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So the solution is actually to take inspiration
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from another domain: speech recognition.
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This is a domain of something that seems impossible,
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which can actually be done,
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simply by putting additional constraints.
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If you have a very good model
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of a language which is used,
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if you have a very good model of a document,
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how well they are structured.
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And these are administrative documents.
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They are well structured in many cases.
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If you divide this huge archive into smaller subsets
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where a smaller subset actually shares similar features,
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then there's a chance of success.
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If we reach that stage, then there's something else:
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we can extract from this document events.
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Actually probably 10 billion events
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can be extracted from this archive.
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And this giant information system
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can be searched in many ways.
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You can ask questions like,
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"Who lived in this palazzo in 1323?"
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"How much cost a sea bream at the Realto market
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in 1434?"
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"What was the salary
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of a glass maker in Murano
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maybe over a decade?"
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You can ask even bigger questions
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because it will be semantically coded.
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And then what you can do is put that in space,
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because much of this information is spatial.
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And from that, you can do things like
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reconstructing this extraordinary journey
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of that city that managed to have a sustainable development
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over a thousand years,
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managing to have all the time
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a form of equilibrium with its environment.
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You can reconstruct that journey,
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visualize it in many different ways.
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But of course, you cannot understand Venice if you just look at the city.
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You have to put it in a larger European context.
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So the idea is also to document all the things
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that worked at the European level.
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We can reconstruct also the journey
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of the Venetian maritime empire,
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how it progressively controlled the Adriatic Sea,
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how it became the most powerful medieval empire
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of its time,
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controlling most of the sea routes
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from the east to the south.
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But you can even do other things,
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because in these maritime routes,
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there are regular patterns.
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You can go one step beyond
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and actually create a simulation system,
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create a Mediterranean simulator
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which is capable actually of reconstructing
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even the information we are missing,
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which would enable us to have questions you could ask
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like if you were using a route planner.
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"If I am in Corfu in June 1323
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and want to go to Constantinople,
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where can I take a boat?"
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Probably we can answer this question
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with one or two or three days' precision.
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"How much will it cost?"
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"What are the chance of encountering pirates?"
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Of course, you understand,
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the central scientific challenge of a project like this one
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is qualifying, quantifying and representing
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uncertainty and inconsistency at each step of this process.
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There are errors everywhere,
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errors in the document, it's the wrong name of the captain,
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some of the boats never actually took to sea.
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There are errors in translation, interpretative biases,
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and on top of that, if you add algorithmic processes,
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you're going to have errors in recognition,
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errors in extraction,
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so you have very, very uncertain data.
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So how can we detect and correct these inconsistencies?
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How can we represent that form of uncertainty?
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It's difficult. One thing you can do
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is document each step of the process,
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not only coding the historical information
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but what we call the meta-historical information,
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how is historical knowledge constructed,
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documenting each step.
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That will not guarantee that we actually converge
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toward a single story of Venice,
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but probably we can actually reconstruct
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a fully documented potential story of Venice.
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Maybe there's not a single map.
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Maybe there are several maps.
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The system should allow for that,
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because we have to deal with a new form of uncertainty,
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which is really new for this type of giant databases.
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And how should we communicate
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this new research to a large audience?
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Again, Venice is extraordinary for that.
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With the millions of visitors that come every year,
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it's actually one of the best places
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to try to invent the museum of the future.
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Imagine, horizontally you see the reconstructed map
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of a given year,
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and vertically, you see the document
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that served the reconstruction,
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paintings, for instance.
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Imagine an immersive system that permits
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to go and dive and reconstruct the Venice of a given year,
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some experience you could share within a group.
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On the contrary, imagine actually that you start
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from a document, a Venetian manuscript,
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and you show, actually, what you can construct out of it,
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how it is decoded,
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how the context of that document can be recreated.
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This is an image from an exhibit
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which is currently conducted in Geneva
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with that type of system.
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So to conclude, we can say that
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research in the humanities is about to undergo
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an evolution which is maybe similar
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to what happened to life sciences 30 years ago.
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It's really a question of scale.
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We see projects which are
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much beyond any single research team can do,
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and this is really new for the humanities,
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which very often take the habit of working
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in small groups or only with a couple of researchers.
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When you visit the Archivio di Stato,
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you feel this is beyond what any single team can do,
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and that should be a joint and common effort.
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So what we must do for this paradigm shift
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is actually foster a new generation
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of "digital humanists"
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that are going to be ready for this shift.
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I thank you very much.
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(Applause)
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