Breakthroughs, discoveries, and DIY tips sent every weekday. Ancient Egyptians may have used opium a . Based on recent examinations, archaeologists now say the drug may even have been a near-daily recreational habit. Opium might have even been widely used across socio-economic classes as long as 3,000 years ago. The evidence is detailed in a study recently published in the, and offers a glimpse into the daily lives of regular Egyptians and royalty alike.

The mysterious dialect of our ancient ancestors could finally be deciphered in full thanks to artificial intelligence. A million cuneiform tablets still exist in the world, experts estimate, but these writings left behind by ancient Mesopotamians require tedious work by archaeologists to translate and catalog their contents. It has been estimated that 90 percent of cuneiform texts remain untranslated. But now, a team of German researchers has figured out a new way to train computers to recognize cuneiform and even make the contents of millennia-old tablets searchable like a website, making it possible to digitize and assemble larger libraries of these ancient texts. This could unlock previously unknown details about ancient life, as the tablets contained details about feats as significant as temple construction, all the way down to squabbles as petty as customer service complaints.

I've been meaning to post for quite some time about a subject that has become very central to me and my work. Maths has been a constant in my life since early childhood. Coming from a family where maths was highly valued it was natural for me to gravitate towards mathematics at university. Recently, I have been reading about the history of mathematics. I was surprised to discover that the first recorded zero appeared in Mesopotamia around 3 B.C. and that the first use of negative numbers appeared in China around 200 B.C.

An artificial-intelligence program is able to predict missing words from cuneiform tablets that are up to 4,500 years old with stunning accuracy. The tablets include information about Mesopotamia from between 2500 BC and 100 AD, but missing text has hindered scientists' abilities to uncover the secrets of the ancient civilization. The AI, which was taught how to read 104 languages, was fed transcriptions of 10,000 cuneiform tablets. It accurately predicted the missing words, phrases and sentences, similarly to how the autosuggest feature on your phone suggests the next line. Mesopotamia is one of the world's oldest known civilizations and gave rise to the Sumerian, Assyrian and Babylonian empires.

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.

Scientists are constantly figuring out how to expand the field of use of this incredible invention, which enables computer software to progressively improve its actions by adopting knowledge gained from previous experience. Machine learning, also referred to as artificial intelligence due to its ability to perform tasks using its own judgment, has been the subject of both praise and controversy. However, the sophisticated algorithms that have served in providing you ads on social networks might have a grand future in philology, archaeology, and linguistics. According to Émilie Pagé-Perron, a Ph.D. candidate in Assyriology at the University of Toronto, we might be closer than we thought to deciphering numerous Middle-Eastern cuneiform tablets written in Sumerian and Akkadian languages, all of which are several thousand years old. Pagé-Perron is in charge of the project officially titled Machine Translation and Automated Analysis of Cuneiform Languages, which currently operates in Frankfurt, Toronto, and Los Angeles, using combined efforts to create a program capable of translating the clay tablets.

Broken and scorched black by fire, the dense, wedge-shaped marks etched into the ancient clay tablets are only just visible under the soft light at the British Museum. These tiny signs are the remains of the world's oldest writing system: cuneiform. Developed more than 5,000 years ago in Mesopotamia, the land between the Tigris and Euphrates rivers where modern-day Iraq now lies, cuneiform captured life in a complex and fascinating civilisation for some three millennia. From furious letters between warring royal siblings to rituals for soothing a fractious baby, the tablets offer a unique insight into a society at the dawn of history. An estimated half a million of them have been excavated, and more are still buried in the ground.

Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of words change over time. We use dynamic embeddings to analyze three large collections of historical texts: the U.S. Senate speeches from 1858 to 2009, the history of computer science ACM abstracts from 1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We find dynamic embeddings provide better fits than classical embeddings and capture interesting patterns about how language changes.