In 1931 at the age of just 25 years, the young Austrian mathematician Kurt Goedel proved an astonishing mathematical theorem that made him instantly famous and a celebrity in mathematical circles around the world (see picture; in the Anglo-Saxon literature Goedel is usually referred to as "Godel" skipping the Umlaut in his German name). Despite its very abstract nature and the lack of any every day practical use, Goedel's theorem - the so called Incompleteness Theorem - has had a dramatic and deep impact on mathematics itself and its foundations. It also had a substantial impact on the philosophy of the 20th century and our understanding of the general limitations of computers and algorithms. I will explain here how Goedel's theorem actually caused the emergence of the new science of Artificial Intelligence (AI) and theoretical computer science in the 1940s and 1950s and how it motivated such key AI pioneers like Alan Turing to get involved. The birth of AI and the course AI has taken ...

An insane drone crash caught on video shows a bicyclist totally eating it when a drone ends up in the middle of a road race. The rogue drone somehow strayed from its intended course and tripped up a cyclist competing in the Golden State Race Series in Northern California over the weekend. The rider flips over his handlebars and performs a spectacular full somersault shortly after he rides over the drone that flew straight into his wheels. Somehow, the rider walked away with just road rash. At least the drone pilot offered to buy him a new bicycle.

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.

The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG. In this paper, we introduce PC with p-values (PC-p), a fast algorithm which robustly computes edge-specific p-values and then estimates and controls the FDR across the edges. PC-p specifically uses the p-values returned by many conditional independence tests to upper bound the p-values of more complex edge-specific hypothesis tests. The algorithm then estimates and controls the FDR using the bounded p-values and the Benjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm also help PC-p accurately compute the upper bounds despite non-zero Type II error rates. Experiments show that PC-p yields more accurate FDR estimation and control across the edges in a variety of CPDAGs compared to alternative methods.

It costs a stupendous amount of money to send something from the surface of Earth to the surface of Mars, and there are severe limits on the volume and mass that you can send at any one time. In order to stuff the maximum amount of science into the minimum amount of space, NASA has had to get creative, with landers and rovers designed to be lightweight and foldable. At NASA's Jet Propulsion Laboratory, in Pasadena, Calif., engineers have long been trying to cram as much robot as possible into the absolute minimum amount of space, and a team of roboticists there recently showed us their latest creation: PUFFER, the Pop-Up Flat Folding Explorer Robot. It's designed to pack down nearly flat for transport, and then re-expand on site to investigate all the places a bigger rover can't quite reach. The overall idea with PUFFER is that you'd pack a bunch of them along with the next Mars rover, and send them out whenever you want to go somewhere that it would be either risky or impossible for the larger rover to go.

The Internet of Things (IoT) is an uninterrupted connected network of embedded objects/ devices with identifiers without any human intervention using standard and communication protocol. It provides encryption, authorization and identification with different device protocols like MQTT, STOMP or AMQP to securely move data from one network to another. IoT in connected Government helps to deliver better citizen services and provides transparency. It improves the employee productivity and cost savings. It helps in delivering contextual and personalized service to citizens and enhances the security and improves the quality of life.

A new working paper finds that the arrival of one new industrial robot in a local labor market coincides with an employment drop of 5.6 workers. These papers have not been peer-reviewed, but are circulated by their authors for comment and discussion. With the NBER's blessing, Making Sen$e is pleased to feature these summaries regularly on our page. The following summary was written by the NBER and doesn't necessarily reflect the views of Making Sen$e. With America's workers already squeezed by forces ranging from international competition to offshoring to new information technologies, concern is growing about the impact of robots on jobs and wages.

In the near future, as artificial intelligence (AI) systems become more capable, we will begin to see more automated and increasingly sophisticated social engineering attacks. The rise of AI-enabled cyberattacks is expected to cause an explosion of network penetrations, personal data thefts, and an epidemic-level spread of intelligent computer viruses. Ironically, our best hope to defend against AI-enabled hacking is by using AI. But this is very likely to lead to an AI arms race, the consequences of which may be very troubling in the long term, especially as big government actors join the cyber wars. My research is at the intersection of AI and cybersecurity.

So the government is looking for a way ― probably using facial recognition, but potentially using eye scans or other measures too ― to verify which travelers have left the country by collecting biometric data right before passengers board a flight. This isn't a new idea: Many countries already use face scans extensively in their airports and train stations, and the U.S. has been working on its own way to track exiting travelers for more than 20 years. However, CBP is now under extra pressure to choose a method and get a system into airports, said Theresa Cardinal Brown, an immigration expert at the Bipartisan Policy Center.

The UK's National Health Service (NHS) and Intel are working together to make cancer detection more efficient through artificial intelligence. Last week, the University of Warwick, University Hospitals Coventry & Warwickshire NHS Trust (UHCW) alongside Intel said a new collaboration between the groups will push forward the classification of cancer cells "more efficiently and accurately through ground-breaking artificial intelligence." A team of scientists, hosted by the University of Warwick's Tissue Image Analytics (TIA) laboratory and led by Professor Nasir Rajpoot are currently creating a digital repository of known tumor and immune cells based on thousands of human tissue cells. This database of cancer information will then be used by algorithms to recognize these cells automatically. While some types of cancer are more aggressive than others, time is almost always an issue.