Many researchers hope that AI will leading to a“golden age” of discovery for lost languages, hard to decipher writings, and badly damaged Biblical scrolls. Algorithms can chug through vast numbers of possibilities of interpretation, presenting the scholar with probabilities to choose from.
But even powerful algorithms have their work cut out for them. For example, of the hundreds of thousands of clay (cuneiform) tablets that survive from an ancient part of the Near East called Mesopotamia, many are damaged. We may know the language but we don’t know what’s missing from the text and what difference the missing part makes to what is being said.
Experts try to fill in the missing parts but guessing at all the possibilities is a very time-consuming job, prone to wild-goose chases that could put them off the track indefinitely.
Here’s an example. Suppose the text read something like this:
“The king had three sons and two [large gap]”
It is reasonable to think that the key missing piece of text is “daughters” and that the subsequent text refers to various marriage arrangements for the king’s children.
But there are other possibilities. What if the missing piece of text is
“island kingdoms. And the king was unsure how to divide the inheritance fairly.” It might be a while before we knew enough of the story to tumble to that.
Is it worth trying to figure it out? Some of the earliest known agricultural civilizations originated in Mesopotamia. It is the backdrop to a number of incidents important to various cultures today. Abraham and Sarah set out from Ur, the children of Israel were exiled in Babylon, and a key battle in Shia Islamic history (Karbala) was fought there. So scholars who want to know more of the life and times of ordinary people who have lived in Mesopotamia have been using AI to try to fill in the gaps in the masses of records and documents of many aspects of their lives.
A recent effort involves putting recurrent neural networks to work on the Achaemenid Empire (550 BC–330 BC) founded by Cyrus the Great (pictured). Cyrus’s empire had a deep influence on the development of civilizations in the area. He appears in the Bible 23 times and is discussed by Greek historians of the era.
A recurrent neural network is the kind of predictive text program you may notice on a cell phone. It predicts what you might be intending to say and fills it in for you. For example, “I am run”[ing late. Start without me.] If you (and/or many others) have often typed those words in the past, the program may “predict” what you mean to say and fill it in:
Once the researchers have placed enough text in a format on which the algorithm can work, they will be in a position to sift through the probabilities much faster.
They face some risks, however, as experimental physicist Rob Sheldon, who has studied classical Hebrew and uses neural networks, told Mind Matters News. He tried using Google neural nets to convert an oral science debate into a readable text but found that in every third sentence, the meaning was completely garbled. He concluded, “Evidently the training set wasn’t used to scientific discussions.”
The “training set” is the large number of examples used to prompt the AI to choose one outcome as more probable than another. He concluded,
Thus the missing text that the AI proposes can look sort of reasonable but we don’t really know if it is correct. It’s just notunreasonable.
Dr. Sheldon makes clear that neural networks can be very useful, depending on the problem researchers are addressing:
Neural nets are adept at pattern recognition. Inasmuch as written language is a pattern, neural nets can help. If, for example, you wanted to know which 5 letters were embedded in the “Captcha” image, a neural net could help you. If you want to know, “are these scratches on a paleolithic rock natural?” perhaps a neural net could help you.
