The Abbey Library of St. Gall in Switzerland is home to approximately 160,000 volumes of literary and historical manuscripts dating back to the eighth century--all of which are written by hand, on parchment, in languages rarely spoken in modern times. To preserve these historical accounts of humanity, such texts, numbering in the millions, have been kept safely stored away in libraries and monasteries all over the world. A significant portion of these collections are available to the general public through digital imagery, but experts say there is an extraordinary amount of material that has never been read--a treasure trove of insight into the world's history hidden within. Now, researchers at University of Notre Dame are developing an artificial neural network to read complex ancient handwriting based on human perception to improve capabilities of deep learning transcription. "We're dealing with historical documents written in styles that have long fallen out of fashion, going back many centuries, and in languages like Latin, which are rarely ever used anymore," said Walter Scheirer, the Dennis O. Doughty Collegiate Associate Professor in the Department of Computer Science and Engineering at Notre Dame.

Franz Joseph Gall gained fame and notoriety in the 1800s for his theories about the mind. Gall believed that the shapes and bumps of the skull provided a lot of information about a person. These theories, phrenology, attributed skull shapes and bumps to personality, traits and morality. In the 1830s and 1840s, phrenology gained popularity in the USA. Based on skull measurements, physician Charles Caldwell claimed Africans were mentally inferior.

An empty street, a row of parked cars at the side: nothing to indicate that you should be careful. But wait: Isn't there a side street up ahead, half covered by the parked cars? Maybe I better take my foot off the gas -- who knows if someone's coming from the side. We constantly encounter situations like these when driving. Interpreting them correctly and drawing the right conclusions requires a lot of experience. In contrast, self-driving cars sometimes behave like a learner driver in his first lesson.

By the time his play "R.U.R." (which stands for "Rossum's Universal Robots") premiered in Prague in 1921, Karel Čapek was a well-known Czech intellectual. Like many of his peers, he was appalled by the carnage wrought by the mechanical and chemical weapons that marked World War I as a departure from previous combat. He was also deeply skeptical of the utopian notions of science and technology. "The product of the human brain has escaped the control of human hands," Čapek told the London Saturday Review following the play's premiere. "This is the comedy of science." In that same interview, Čapek reflected on the origin of one of the play's characters: The old inventor, Mr. Rossum (whose name translated into English signifies "Mr.

Empirically, this optimization lead to 0.5 - 2.5% savings on expansions in our experiments A fundamental concern in real-time planning is the presence (Cserna, Gall, and Ruml 2019). of dead-ends in the state space, from which no goal is reachable. SafeRTS interleaves exploration and safety proofs during Providing real-time heuristic search algorithms that are its planning phase. As a direct consequence, it attempts complete in domains with dead-end states is a challenging safety proofs on nodes that become internal to the LSS by problem. Recently, the SafeRTS algorithm was proposed for the end of the search iteration. As shown in Cserna, Gall, and searching in such spaces (Cserna et al. 2018). SafeRTS exploits Ruml (2019), it would be equally or less difficult to achieve a user-provided predicate to identify safe states, from the same or better safety coverage by doing safety proofs after which a goal is likely reachable, and attempts to maintain a all the LSS expansions. SafeRTS has an anytime behavior backup plan for reaching a safe state at all times.

Unbeknownst to scientists, two parasites have been battling it out in the oak scrub habitat of South Florida. An evolutionary biologist has discovered the first known example of a bizarre interaction between gall wasps and a wispy orange plant called the love vine. The love vine, researchers found, latch onto the egg-containing growths deposited by wasps onto the underside of oak leaves and feed off the contents as the larva matures, eventually leaving behind the mummified carcass of an adult wasp. Researchers gathered samples along a 1,000-mile stretch through Florida for study in the lab, and noticed something unusual: S-shaped twists of love vines growing between the galls (as seen above). 'I had never seen this,' said Rice University evolutionary biologist Scott Egan, who has studied gall-forming insects for 17 years.

A team of scientists at Universität Bonn in Germany has developed not-at-all-creepy software able to predict the future. However, before heading out for a lottery ticket, potential users should be aware that the software is currently at its best when predicting what a chef might be about to do or need when preparing a salad. The research is concerned with predicting actions, and the self-learning software is pretty good at it, once it's gone through a few hours of training videos. In this case, the software was fed 40 videos of around six minutes each in which different salad dishes were prepared consisting of an average of 20 actions. It also sat through 1,712 videos of 52 different actors making breakfast.

As humans, we've gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of "time" – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question. Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent.

Although informal models of evidential reasoning have been successfully app'ied in automated reasoning systems, it is generally difficult to define the range of their applicability In addition, they hay., not provided a basis for coherent management of evidence bearing on hypotheses that are related hierarchically. The Dempster-Shafer (D-S) theory of evidence is appealing because it does suggest a coherent approach for dealing with such relationships However, the theory's complexity and potential for computational inefficiency have tended to discourage its use in reasoning systems In this paper we describe the central elements of the D-S theory, basing our exposition on simple examples drawn from the field of medicine. We then demonstrate the relevance of the 0-S theory to a familiar expert system domain, namely the bacterial organism identification problem that lies at the heart of the MYCIN system. Finally, we present a new adaptation of the D-S approach that achieves computational efficiency while permitting the management of evidential reasoning.within

The drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. Particularly appealing is the mathematical theory of evidence developed by Arthur Dempster. We are convinced it merits careful study and interpretation in the context of expert systems. This theory was first set forth by Dempster in the 1960s and subsequently extended by Glenn Sharer. In 1976, the year after the first description of CF's appeared, Shafer published A Mathematical Theory of Evidence (Shafer, 1976). Its relevance to the issues addressed in the CF model was not immediately recognized, but recently researchers have begun to investigate applications of the theory to expert systems (Barnett, 1981; Friedman, 1981; Garvey et al., 1981). We believe that the advantage of the Dempster-Shafer theory over previous approaches is its ability to model the narrowing of the hypothesis set with the accumulation of evidence, a process that characterizes diagnostic reasoning in medicine and expert reasoning in general. An expert uses evidence that, instead of bearing on a single hypothesis in the original hypothesis set, often bears on a larger subset of this set. The functions and combining rule of the Dempster-Shafer theory are well suited to represent this type of evidence and its aggregation. For example, in the search for the identity of an infecting organism, a smear showing gram-negative organisms narrows the hypothesis set of all possible organisms to a proper subset. This subset can also be thought of as a new hypothesis: the organism is one of the gram-negative organisms.