While the internet has the potential to give people ready access to relevant and factual information, social media sites like Facebook and Twitter have made filtering and assessing online content increasingly difficult due to its rapid flow and enormous volume. To explore how social media users perceive the trustworthiness and usefulness of these services, we applied a research approach designed to take advantage of unstructured social media conversations (see Figure 3). While investigations of trust and usefulness often rely on structured data from questionnaire-based surveys, social media conversations represent a highly relevant data source for our purpose, as they arguably reflect the raw, authentic perceptions of social media users. To create a sufficient dataset for analysis, we removed all duplicates, including a small number of non-relevant posts lacking personal opinions about fact checkers.
The competition aimed to assess the state of the art in AI systems utilizing natural language understanding and knowledge-based reasoning; how accurately the participants' models could answer the exam questions would serve as an indicator of how far the field has come in these areas. A week before the end of the competition, we provided the final test set of 21,298 questions (including the validation set) to participants to use to produce a final score for their models, of which 2,583 were legitimate. AI2 also generated a baseline score using a Lucene search over the Wikipedia corpus, producing scores of 40.2% on the training set and 40.7% on the final test set. His model achieved a final score of 59.31% correct on the test question set of 2,583 questions using a combination of 15 gradient-boosting models, each with a different subset of features.
In the bowels of the ship, Todd Humphreys, an associate professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, worked with his team to feed the super-yacht's crew false navigation data using a few thousand dollars worth of hardware and software. The protocol, called the TESLA signature, is designed to complement location data with a cryptographic "signature," so Galileo's satellites would send both navigation data and the cryptographic signature to the receiving client. Humphreys also points to the U.S. Department of Homeland Security's recent document on anti-spoofing, "Improving the Operation and Development of Global Positioning System (GPS) Equipment Used by Critical Infrastructure," as a sign that the right parties are taking GPS spoofing seriously. U.S. Department of Homeland Security, National Cybersecurity & Communications Integration Center, National Coordinating Center for Communications Improving the Operation and Development of Global Positioning System (GPS) Equipment Used by Critical Infrastructure, http://bit.ly/2oZewfz Logan Kugler is a freelance technology writer based in Tampa, FL.
Among the 22 Turing Laureates in attendance at the conference were: Front row, from left: Whitfield Diffie (2015), Martin Hellman (2015), Robert Tarjan (1986), Barbara Liskov (2008). Among the 22 Turing Laureates in attendance at the conference were: Front row, from left: Whitfield Diffie (2015), Martin Hellman (2015), Robert Tarjan (1986), Barbara Liskov (2008). Butler Lampson, the 1992 Turing Laureate ("for contributions to the development of distributed, personal computing environments and the technology for their implementation: workstations, networks, operating systems, programming systems, displays, security, and document publishing"), said, "There's plenty of room at the top; there's room in software, algorithms, and hardware." A panel on Moore's Law was moderated by John Hennessy (left) and included Doug Burger, Norman Jouppi, Butler Lampson (1992), and Margaret Martonosi.
Charles William "Charlie" Bachman, the "father of databases" who received the ACM A.M. Turing Award for 1973 for creating the first database management system, died June 13 at the age of 92. Born in Manhattan, KS, in 1924, Bachman earned his B.S. in mechanical engineering in 1948, as well as an M.S. in mechanical engineering from the University of Pennsylvania. He went to work for Dow Chemical in 1950, using mechanical punched-card computing devices to solve networks of simultaneous equations representing data from Dow plants. In 1957, Bachman became head of Dow's Data Processing Department, through which he became a member of Share Inc., and a founding member of the Share Data Processing Committee. In 1960, Bachman joined the General Electric (GE) Production Control Services Group in New York City, using a factory in Philadelphia to test designs for a system to automate factory planning, scheduling, operational control, and inventory control. The resulting MIACS was based on the ...
Computation is a process that is defined in terms of an underlying model of computation, and computational thinking is the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms." If we replaced it with similar words, like "procedures" or "sequences," we would arrive at such vacuous "definitions" as, say, "Medicine is a process that is defined in terms of an underlying model of medicine, and medical thinking is the thought processes involved in formulating problems so their solutions can be represented as medical steps and procedures." And "Drama is a process that is defined in terms of an underlying model of drama, and dramatic thinking is the thought processes involved in formulating problems so their solutions can be represented as dramatic steps and sequences." If such influence is indeed the basis for a comparison, then additional covariates should be controlled for, including the mean estimated valuation per patent, number of employees in the industry, and additional financial and industry-specific characteristics.
Despite the fact that he does not see very well, Alexei Efros, recipient of the 2016 ACM Prize in Computing and a professor at the University of California at Berkeley, has spent most of his career trying to understand, model, and recreate the visual world. Drawing on the massive collection of images on the Internet, he has used machine learning algorithms to manipulate objects in photographs, translate black-and-white images into color, and identify architecturally revealing details about cities. Here, he talks about harnessing the power of visual complexity. You were born in St. Petersburg (Russia), and were 14 when you came to the U.S. What drew you to computer science?
The ACM U.S. Public Policy Council (USACM) was established in the early 1990s as a focal point for ACM's interactions with U.S. government organizations, the computing community, and the public in all matters of U.S. public policy related to information technology. USACM and EUACM have identified and codified a set of principles intended to ensure fairness in this evolving policy and technology ecosystem.a These are: (1) awareness; (2) access and redress; (3) accountability; (4) explanation; (5) data provenance; (6) audit-ability; and (7) validation and testing. As organizations deploy complex algorithms for automated decision making, system designers should build these principles into their systems. USACM and EUACM seek input and involvement from ACM's members in providing technical expertise to decision makers on the often difficult policy questions relating to algorithmic transparency and accountability, as well as those relating to security, privacy, accessibility, intellectual property, big data, voting, and other technical areas.
In this article, we discuss how our Scribe system combines human labor and machine intelligence in real time to reliably convert speech to text with less than 4s latency. Scribe illustrates the broad potential for deeply interleaving human labor and machine intelligence to provide intelligent interactive services that neither can currently achieve alone. Real-time captioning converts speech to text in under 5s to provide access to live speech content for deaf and hard of hearing (DHH) people in classrooms, meetings, casual conversation, and other events. While visual access to spoken material can be achieved through sign language interpreters, many DHH people do not know sign language.
I treat data science problems as complex systems involving comprehensive system complexities, or X-complexities, in terms of data (characteristics), behavior, domain, social factors, environment (context), learning (process and system), and deliverables. Data complexity is reflected in terms of sophisticated data circumstances and characteristics, including large scale, high dimensionality, extreme imbalance, online and real-time interaction and processing, cross-media applications, mixed sources, strong dynamics, high frequency, uncertainty, noise mixed with data, unclear structures, unclear hierarchy, heterogeneous or unclear distribution, strong sparsity, and unclear availability of specific sometimes critical data. It may be embodied in such aspects of business problems as social networking, community emergence, social dynamics, impact evolution, social conventions, social contexts, social cognition, social intelligence, social media, group formation and evolution, group interaction and collaboration, economic and cultural factors, social norms, emotion, sentiment and opinion influence processes, and social issues, including security, privacy, trust, risk, and accountability in social contexts. Environment complexity is another important factor in understanding complex data and business problems, as reflected in environmental (contextual) factors, contexts of problems and data, context dynamics, adaptive engagement of contexts, complex contextual interactions between the business environment and data systems, significant changes in business environment and their effect on data systems, and variations and uncertainty in interactions between business data and the business environment.