The Dead Sea Scrolls, found by a shepherd boy in 1947, dating from roughly 200 BC through 100 AD, were remarkably well-preserved. Exciting finds like the Scrolls and the hieroglyphs of ancient Egypt tempt us to think that when a lost document is found, we will easily physically read it once we understand the language. Sadly, many surviving documents are so damaged that they cannot be read using traditional methods. All we know is that they are/were documents. Nowadays, the 1,700-year-old En-Gedi Scroll--one of the most ancient snippets of the Old Testament ever uncovered--isn't much to look at.

The firm has also developed a drone able to fly around the underground corridors of coal mines to detect gas emissions and other potential threats. Marcin Dziekanski, coordinator of the drone project of the Silesian metropolis, an alliance of more than 40 cities in the coal-mining Katowice region, said they use drones to monitor the smoke produced by coal-heated individual houses. "They fly over Katowice, over the buildings, as well as over other cities, enabling us to intervene, in cooperation with the city police, showing that we are monitoring our space, our environment," he told AFP, adding that "we are creating a set of good practices that we are sharing with others." Spartaqs considers itself above all a research firm looking into new technologies, though it has already sold a dozen drones--at an average price of 50,000 euros ($55,000) a pop--in Poland and Georgia. But the company has realised that buyers like the Saudis and the Americans, who are very interested in certain models, want to see "the plant where they are produced." So they have begun looking for investors, including abroad, who would like to participate in the development of a serial production line.

By Country (United States; Canada; Mexico and Rest of North America) 6.3.

A collection of 5,000-year-old cave paintings depicting various figures and symbols has been discovered in Spain. The drawings were discovered in the rocky area of San Juan, near the town of Albuquerque in the province of Badajoz in western Spain. They are around 4 inches in length and include some anthropomorphic figures, as well as an arrow and other symbols, according to Spanish daily newspaper La Vanguardia. The doodlings were discovered by Agustín Palomo, an historic researcher who lives locally to the caves, while he was looking for a type of tomb known as a Dolmen. Mr Palomo immediately recognised their significance, given their location not far from two other well-known sets of cave drawings - 'Risco de San Blas', of the Sierra de la Carava and those of Azagala - the latter of which were only discovered around 20 years ago.

Erratic oil prices in recent years have made economic diversification essential, and AI is an alternate solution. Having made an early start, these states are positioned to become a key player in AI technology. Dividing the Middle East into four main regions, first, the UAE, second, Saudi Arabia, the GCC 4 comprising of Bahrain, Kuwait, Oman and Qatar on third and lastly Egypt, a PWC research expects the Arab states to accrue two percent of the total global benefits in the next ten years. Projected to mark the highest gains, the UAE would get nearly 14 percent on its GDP in 2030 while the kingdom of Saudi Arabia should make over US $135 billion by that time as well, this being nearly 12.4 percent of its GDP. Assigning large budgets for the speedy implementation of AI, these two GCC states have made a major impact.

The company's app and platform lets wineries and wine producers create, manage, and market an augmented reality experience for their own labels via a smartphone app. Now, says the company, 524 wineries from across the world are participating in the trial and accessing the marketing potential of a marriage of artificial intelligence and augmented reality. "I don't believe anything like this has been done to this level before – bringing together artificial intelligence, augmented reality, clever technology, 500 different wineries with different visuals and branding, as well as different languages," says app creator and Winerytale founder Dave Chaffey. "This platform is purpose-built for mass adoption and accessibility to any winery wanting to take advantage of a brand marketing and sales future that will undeniably involve augmented reality." The technology is designed to work on any wine label, using artificial intelligence to scan and recognize labels, and augmented reality to showcase the wine's backstory by beaming it from an imaginary space inside and outside of the bottle.

Michael Kwet is a Visiting Fellow of the Information Society Project at Yale Law School. He is the author of Digital Colonialism: US Empire and the New Imperialism in the Global South, and hosts the Tech Empire podcast. "Beggars" and "vagrants" are not welcome in Parkhurst, South Africa, a mostly white, middle-class suburb of about 5,000 on the outskirts of Johannesburg's inner city. Criminals are on the prowl, residents warn, and they threaten their neighborhood security. To combat crime, the locals came up with a solution: place CCTV surveillance cameras everywhere. However, these are not the camera networks of times past. Thanks to advancements in machine learning and AI, CCTV systems are now equipped with sophisticated video analytics that can track a wide range of behaviors, objects, and patterns, in addition to individual faces. Armed with powerful new tech, communities of color can be watched, flagged, policed, and intimidated into submission. I've spent the past several years studying the video surveillance industry in South Africa. During that time, a private corporation called Vumacam has been quietly assembling a "smart" CCTV surveillance network in the suburbs of Johannesburg. Earlier this year, the company announced it would blanket Joburg with 15,000 cameras.

Processes are a crucial artefact in organizations, since they coordinate the execution of activities so that products and services are provided. The use of models to analyse the underlying processes is a well-known practice. However, due to the complexity and continuous evolution of their processes, organizations need an effective way of analysing the relation between processes and models. Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. In this paper we present the notion of anti-alignment as a concept to help unveiling runs in the model that may deviate significantly from the observed behavior. Using anti-alignments, a new metric for precision is proposed. In contrast to existing metrics, anti-alignment based precision metrics satisfy most of the required axioms highlighted in a recent publication. Moreover, a complexity analysis of the problem of computing anti-alignments is provided, which sheds light into the practicability of using anti-alignment to estimate precision. Experiments are provided that witness the validity of the concepts introduced in this paper.

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r .t.the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances.

Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied without handling these additional constraints. In this paper we show, through the use of an air pollution network example, how using probabilistic machine learning can mitigate some of the technical constraints. Specifically we experiment with modelling the calibration for individual sensors as either distributions or Gaussian processes over time, and discuss the wider issues around the decision process.