The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions, in the form of bets, about the future of AI. While it is easy to make broad, generalized, or off-the-cuff predictions about the future, it is more difficult to develop predictions that are carefully thought out, concrete, and measurable. This forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when the bets come due. The bets will be documented both online and regularly in this column. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated or too specific to an individual or institution. The goal is not to continue to feed the media frenzy and outsized pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. For detailed guidelines and to place bets, visit sciencebets.org.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Sixth AAAI Conference on Human Computation and Crowdsourcing was held on the campus of the University of Zurich in Zurich, Switzerland on 5 July 2018. There were three full-day workshops in the program: CrowdBias: Disentangling the Relation between Crowdsourcing and Bias Management; Subjectivity, Ambiguity, and Disagreement in Crowdsourcing; Work in the Age of Intelligent Machines; a three-quarter day workshop, Advancing Human Computation with Complexity Science; and Project Networking; and a quarter day Project Networking workshop. This report contains summaries of three of the events.  

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement . We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence.  Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

We can easily understand linked data as being a part of the greater big data landscape, as many of the challenges are the same (Hitzler and Janowicz 2013). The linking component of linked data, however, puts an additional focus on the integration and conflation of data across multiple sources.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15–17, at the Westin Arlington Gateway in Arlington, Virginia near Washington DC USA. The titles of the five symposia were as follows: Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? The highlights of each symposium are presented in this report.

Rinke Hoekstra (VU University from transferring and adapting semantic web Amsterdam) presented linked open data tools technologies to the big data quest. Finally, in the Social to discover connections within established scientific Networks and Social Contagion symposium, a data sets. Louiqa Rashid (University of Maryland) community of researchers explored topics such as social presented work on similarity metrics linking together contagion, game theory, network modeling, network-based drugs, genes, and diseases. Kyle Ambert (Intel) presented inference, human data elicitation, and Finna, a text-mining system to identify passages web analytics. Highlights of the symposia are contained of interest containing descriptions of neuronal in this report.

The success of the Web of Data (WOD) is based on the thorough understanding of, and agreement upon, the se- mantics of data and ontologies. But the Web of Data as a whole is complex, and inherently messy, contex- tualised, opinionated, in short: it is a market-place of ideas, rather than a database. Existing paradigms are in- appropriate for dealing with this new type of knowledge structures. The urgency of dealing with the non-standard charac- teristics of the Web of Data has been recognised, and separate initiatives try to tackle its individual manifes- tations, e.g. inconsistencies, contexts, vagueness, prove- nance, etc. Tomorrow’s Web of Data requires novel se- mantics with efficient (generic) implementations to en- sure semantic clarity, reuse and interoperability. We recently introduced pragmatic semantics as a new semantic paradigm integrating elements from market theory and classical semantics into a framework of op- timisation over truth-orderings, each representing a par- ticular world-view. We propose nature-based algorithms to implement those semantics. We recently started a new research project, called PraSem, with the goal of investigating Pragmatic Semantics both from a theoret- ical and practical perspective.

Identity relations are at the foundation of the Linked Open Data initiative and on the Semantic Web in gen- eral. They allow the interlinking of alternative descrip- tions of the same thing. However, many practical uses of owl:sameAs are known to violate its formal seman- tics. We propose a method that assigns meaning to (the subrelations of) an identity relation using the predicates of the dataset schema. Applications of this approach include automated suggestions for asserting/retracting identity pairs and quality assessment. We also describe an experimental design for this approach.