VOICE OVER: Ancient inscriptions are a treasure to historians – the words of long-gone civilisations set in stone.
THEA SOMMERSCHIELD: Inscriptions are very interesting because they were written
directly by the ancient peoples themselves.
VOICE OVER: Buried within the texts of proclamations and records and prayers
are clues as to where and when these artefacts come from.
But piecing together those clues can be an incredible puzzle.
YANNIS ASSAEL: Sometimes, we only get some parts of the word. There's parts of the text that are lost in millennia.
We will never know what was there.
THEA: Their study is really complex.
VOICE OVER: Now, scientists and historians are solving this ancient puzzle using some very modern technology.
THEA: Ithaca is is the first deep neural network which restores ancient text and also places it in both time and space.
VOICE OVER: Yannis and Thea are researchers from two very different fields.
THEA: So, Yannis and I were doing our doctorates together in Oxford.
YANNIS: I was studying machine learning and Thea was studying ancient history.
THEA: I was specialising in ancient inscriptions.
YANNIS: And in one of our conversations, Thea was describing to me what are some of the most challenging tasks
in a historian's workflow.
THEA: And we saw the co-operative potential to bring the two disciplines together.
VOICE OVER: To demonstrate how machine learning could help with the study of ancient artefacts,
we’ve come here to the Ashmolean Museum in Oxford in the UK.
THEA: This is the Parian marble. It is a very famous Greek inscription that comes from the Greek island of Paros and it dates to probably 264-263 BC.
VOICE OVER: Being centuries old, the inscription on the marble can be hard to read –
even if you do happen to speak ancient Greek.
Often letters, words or whole chunks might be missing from the original.
That’s where Yannis and Thea’s new tool can come in handy.
THEA: You can see here that parts of the slab are missing. There are breaks in the stone.
So, let’s pretend that the break was actually just about more here and covers the word 'Delphi'.
YANNIS: Now, what you see here is our online interface of Ithaca, where we will paste the text of the inscription,
and we will hide that word with question marks.
VOICE OVER: The neural network uses its knowledge of thousands of other similar inscriptions
to try and guess the missing word.
YANNIS: And we can see that the most probable prediction that Ithaca is suggesting is 'Delphi'.
VOICE OVER: The artificial intelligence that powers Ithaca is based on cutting-edge technology
developed here at Google’s DeepMind.
YANNIS: So, Ithaca is a deep neural network, and deep neural networks are inspired by the biological neural networks
like the ones in our brains.
VOICE OVER: Ithaca’s networks replicate some of the work done by historians,
analysing what an inscription says to fill in missing words or find clues as to its origins.
THEA: The text itself contains crucial evidence and hints and clues for locating it and for dating it.
For instance, grammar or certain formulaic language might be really distinctive of a time and place,
or the text itself might mention a famous person.
VOICE OVER: But Ithaca is able to combine and consider far more sources of information than a human brain could manage.
YANNIS: Ithaca was trained in over 80,000 ancient Greek inscriptions and is able to discover underlying patterns
in all of them, and combine knowledge that can come from totally different parts.
THEA: A human cannot possibly harness all of the existing information from all of the inscriptions of all of the ancient
world – that’s a lot of inscriptions, by the way.
VOICE OVER: When the text of an inscription is entered, Ithaca provides options for any missing words,
along with probabilities for each guess. But that’s not all.
THEA: If we scroll down, we will get to Ithaca’s attribution hypotheses –
more specifically, when and where this text was written.
VOICE OVER: For both time and location, Ithaca displays a bar chart of probabilities.
THEA: Instead of giving one static, inscrutable output, we want to provide historians
with multiple hypotheses for them to evaluate.
VOICE OVER: To show how the algorithm is working, the interface highlights
which words or sections of the text it used to make its predictions.
YANNIS: Ithaca actually learns by itself to pay attention to different parts of the inputs that can contribute to making accurate predictions.
And what was fascinating when we were collaborating was to see that Ithaca had actually learned to focus on specific parts of the inputs
that we knew that the historians of our team would also do.
VOICE OVER: In isolation, Ithaca simply provides probabilities, but the team wanted to see how well it could work alongside historians.
They used known texts that the historians weren’t already familiar with, but with certain letters deliberately removed.
The historians had to fill in the blanks, first on their own, then using Ithaca’s predictions as an aid.
YANNIS: And we actually saw that the accuracy tripled.
VOICE OVER: Alone, historians correctly guessed the missing parts 25% of the time.
Ithaca achieved 62% accuracy. But combined, they managed to fill in 72% of the gaps correctly.
YANNIS: And this is a wonderful demonstration of human and AI collaboration.
VOICE OVER: Collaboration has been a key theme throughout this work. Thea and Yannis worked with multiple institutes
around the world, pooling a wide variety of expertise.
YANNIS: To achieve such interdisciplinary projects, collaboration is key, and communication as well.
We had to bridge the information from the two different disciplines and actually find a common language.
And this was wonderful because I learnt so many things about history at the same time.
VOICE OVER: Now that Ithaca is freely available online, Yannis and Thea hope that it could become a standard tool
for historians’ research.
THEA: As with all innovation, we are aware that it might take some time to be fully adopted into the historian's workflow.
But the endorsements and feedback that we’ve received so far, while actually designing this tool,
has been very, very positive so we’re hopeful.
YANNIS: A publication is a point in time but research is ongoing, and this was just the first step.
In our next steps, we want to exploit further what’s the potential of human-AI collaboration,
and we hope that it inspires a new line of work of using AI for the humanities.