SwissCognitive Guest Blogger: Zachary Amos – “5 Archaeological Discoveries Made by AI”

Artificial intelligence (AI) transforms many industries, and archaeology is no exception. It leverages machine learning and advanced data analysis to make it easier for researchers to discover and analyze ancient artifacts and sites.

Whether using satellite imagery to locate lost civilizations, deciphering ancient texts or predicting excavation sites, AI enhances the speed and accuracy of archaeological discoveries. As interest grows in AI’s ability to uncover hidden historical insights, it’s becoming a powerful tool for shedding new light on past mysteries.

1. Mapping Lost Civilizations

AI has proven invaluable in analyzing satellite imagery to uncover ancient cities and structures that have long been hidden from view. One remarkable example is the Nazca region in Peru. Deploying an AI system led to the discovery of 303 new figurative geoglyphs in just six months. This accomplishment would have taken years with traditional methods.

AI uses machine learning algorithms to sift through vast amounts of satellite data and quickly identify patterns and anomalies human eyes might miss. This ability to process large datasets rapidly and precisely makes AI far more efficient and accurate. This allows archaeologists to make discoveries faster and on a much larger scale.

2. Uncovering Hidden Texts

AI is a trailblazer for archeologists trying to read ancient texts that are too damaged for the human eye to decipher. One groundbreaking example is the Herculaneum scrolls, buried under volcanic ash and charred beyond recognition. Deep learning techniques allow researchers to read beneath the surface of these fragile artifacts.

Machine learning algorithms identified ink regions in the flattened papyrus, which would have otherwise remained invisible. Deep learning’s ability to sort and interpret massive numbers of images revolutionizes how these texts are classified and understood. This method reveals previously unreadable content and speeds up the analysis of ancient languages to accelerate discoveries in historical research.

3. Predicting Excavation Sites

AI is increasingly used to predict the most promising excavation sites by analyzing geographical data, historical records and patterns from past discoveries. Examining these large datasets can accurately identify likely locations for hidden artifacts and ancient structures.

Technologies like retrieval augmented generation (RAG) further enhance this process by providing access to the latest reliable information and enabling archaeologists to verify their claims in real time. This combination of AI’s data processing power and advanced technologies ensures efficiency and precision. It allows researchers to focus on areas with the highest potential and reduce time and resources spent on less promising sites.

4. Restoring and Reconstructing Artifacts

AI is crucial in reconstructing fragmented artifacts and structures by helping archaeologists visualize and restore damaged or lost pieces. It uses generative adversarial networks to rapidly manipulate portraits and landscapes and predict missing elements. One notable example is the RePAIR project, which aims to piece together ancient frescoes from thousands of fragments discovered in Pompeii.

AI systems analyze these fragments, predict how they fit together and help restore the art. This technology has also been applied to ancient pottery and sculptures, where AI predicts the shape of missing pieces, allowing archaeologists to recreate the original forms. Speeding up the reconstruction process and improving accuracy transforms restoration work, saving time and making it possible to recover more historical treasures.

5. Studying Human Evolution

AI enhances the study of ancient human migration patterns by analyzing genetic material and fossil evidence with unprecedented precision. Researchers can process complex datasets using deep learning models to trace how early humans moved and settled across different regions.

For example, deep learning models used to study the Mesopotamian floodplain environment achieved an impressive 80% detection accuracy in identifying archaeological sites. This level of precision allows scientists to understand human migration routes and settlement patterns. It also offers insights into the movements of ancient populations that would be difficult to uncover through traditional methods.

Why Staying Informed About AI Advancements Matters

Staying informed about the role of AI in archaeology opens the door to understanding new, groundbreaking discoveries that change how people view the past. AI’s potential to uncover even more hidden historical insights is immense as technology advances.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag 5 Archaeological Discoveries Made by AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: womblebonddickinson.com – “Greenwashing vs. Real Impact: How to Spot the Difference in AI Sustainability Claims”

Organisations using AI to support sustainability

AI has huge potential to make businesses more sustainable. Already being deployed by companies like Google to efficiently cool their data centres, in hospitality to track and reduce food waste, and by governments including Indonesia and Peru using AI and satellite data to show near-real-time vessel movements in the ocean to combat illegal and unsustainable fishing.

From a legislative stance, businesses will soon have to comply with the Corporate Sustainability Reporting Directive, which obligates financial market participants to disclose their non-financial and diversity information. Businesses are, therefore, actively looking for green solutions that can improve their marketability and ultimately their bottom line. AI is being touted as something that can manage environmental impacts and climate change while also improving business efficiency – a win-win.

Is AI sustainable?

However, when implementing AI solutions, there is often little detail given at the micro-level on how AI will save the planet any more effectively or efficiently than traditional computer-human operations. Greenwashing occurs when environmental claims are unproven, over-inflated, or just incorrect. The Advertising Standards Authority (ASA) has been cracking down on greenwashing in advertising, recently issuing reprimands to HSBC, Alpro, and Innocent, among others.

When implementing AI and measuring the energy savings it can produce, this needs to be offset against the electricity consumption of AI systems themselves, as this is potentially substantial. It has been calculated that AI’s global carbon footprint might foreseeably be equal to that of the aviation industry.[…]

Read more: www.womblebonddickinson.com

Der Beitrag Greenwashing vs. Real Impact: How to Spot the Difference in AI Sustainability Claims erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright by https://www.advancedsciencenews.com

Astronomy has a rich history of data gathering and record keeping. Most ancient civilizations developed their own version of astronomy, where significant solar and celestial events were used to establish calendars, support navigation, and played a strong cultural and spiritual role. Oral traditions, such as those used by the indigenous inhabitants of Australia, allowed celestial information to be preserved across millennia. Architectural marvels, including Stonehenge, the Thirteen Towers of Chankillo in Peru, and various temples in Egypt, all show strong alignments with the solstice sunrise or sunset position. These demonstrate a sophisticated understanding of the annual motion of the Sun along the horizon, which would likely have taken decades, or even centuries, of monitoring and record keeping to determine.

Fast forward several thousand years, and modern-day astronomers collect data about celestial objects using an assortment of telescopes and particle detectors. This observational data provides astronomers with information on the position, size, mass, and chemical composition of a myriad of astronomical phenomena such as planets, stars, pulsars, black holes, and galaxies. Our understanding of the Universe is further enhanced through the use of computer simulations, generating even more data for modelling, predicting, and supporting analysis of the observational data.

With access to datasets where sizes are measured in Petabytes, and soon, Exabytes, astronomers have been turning to machine learning (automated processes that learn by example) and artificial intelligence or AI (computers making decisions or discoveries that would usually require human intelligence) to help sift through the data….

Read more: https://www.advancedsciencenews.com

Der Beitrag Artificial Astronomers erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.