Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous social RL approaches either ignore agents dependencies or model them in a computationally intensive manner. In this work, we incorporate agent dependencies efficiently in a compact model by clustering users (based on their payoff and contribution to the goal) and combine this with a method to easily derive personalized agent-level policies from cluster-level policies. We also propose a dynamic clustering approach that captures changing user behavior. Experiments on real-world datasets illustrate that our proposed approach learns more accurate policy estimates and converges more quickly, compared to several baselines that do not use agent correlations or only use static clusters.

In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we propose a probabilistic graphical annotation model to infer the underlying ground truth and annotator's behavior. To accommodate both discrete and continuous application scenarios (e.g., classifying scenes vs. rating videos on a Likert scale), the underlying ground truth is considered following a distribution rather than a single value. In this way, the reliable but potentially divergent opinions from "good" annotators can be recovered. The proposed model is able to identify whether an annotator has worked diligently towards the task during the labeling procedure, which could be used for further selection of qualified annotators. Our model has been tested on both simulated data and real-world data, where it always shows superior performance than the other state-of-the-art models in terms of accuracy and robustness.

Siri means a secret in Swahili and in Norse, a beautiful woman who leads you to victory. To those of us in Appleland using the voice-controlled personal assistant, it stands for "Speech Interpretation and Recognition Intelligence." And when you put Siri to work, she can move productivity mountains. I use Siri to give me directions while driving with Apple CarPlay. I ask her to remind me to stop by the grocery store on my Apple Watch.

More and more, it is becoming necessary to consider working collaboratively, not only for questions regarding skills or because of the very quick evolution of engineering skills, and devices in particular, but also because working alone in a studio or a laboratory will be less and less viable. Interlocution is also essential in artistic practice. After having developed most of my projects alone for a long time, I understand how sharing this experience and competences gives meaning to this activity. You have worked in Brazil for many years. Is it a good place for new media art?

As technology continues to evolve and become more integrated with our daily lives, it's also become more embedded in popular culture. While some TV shows and movies successfully portray technology like artificial intelligence, others don't always take the time to explain the nuances of tech--or if they do, it's not always accurate. For non-tech people who glean much of their tech knowledge from pop culture, this can lead to a lot of misinformation. That's why we asked the members of Forbes Technology Council to clear a few things up. Below they share 12 things pop culture gets wrong about AI, and what the public should know instead. AI will always have a level of containment, as it's constrained to what we're running it on.

We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A natural loss function in this framework is to measure the suboptimality of the decisions induced by the predicted input parameters, as opposed to measuring loss using input parameter prediction error. This natural loss function is known in the literature as the Smart Predict-then-Optimize (SPO) loss, and we propose a tractable methodology called SPO Trees (SPOTs) for training decision trees under this loss. SPOTs benefit from the interpretability of decision trees, providing an interpretable segmentation of contextual features into groups with distinct optimal solutions to the optimization problem of interest. We conduct several numerical experiments on synthetic and real data including the prediction of travel times for shortest path problems and predicting click probabilities for news article recommendation. We demonstrate on these datasets that SPOTs simultaneously provide higher quality decisions and significantly lower model complexity than other machine learning approaches (e.g., CART) trained to minimize prediction error.

Purchases you make through our links may earn us a commission. It's an exciting time to be a Star Wars fan. New episodes of The Clone Wars are hitting Disney each week, The Rise of Skywalker arrives on digital March 17, and Lucasfilm just announced an entirely new era of Star Wars--The High Republic--that will explore the universe hundreds of years before the Skywalker saga takes place. There have never been more ways to get into Star Wars--an ever-expanding universe of mythic fantasy and rich spectacle. If you're feeling especially ambitious, check out the timeline of "canon" media on Wookieepedia for a fairly comprehensive overview of everything Star Wars has to offer: comic books, novels, video games.

The 3rd Workshop on Quality of Open Data will be held in conjunction with BIS conference on June 8-10, 2020 in Colorado Springs, United States. The goal of QOD 2020 workshop is to bring together different communities working on quality in Wikipedia, DBpedia, Wikidata, OpenStreetMap, Wikimapia and other open knowledge bases and data sources. The workshop calls for sharing research experience and knowledge related to quality assessment in open data. We invite papers that provide methodologies and techniques, which can help to verify and enrich various community based services in different languages. Papers approved for presentation at QOD 2020 will be published as a volume in Springer's Lecture Notes in Business Information Processing (LNBIP) series.

I am currently struggling with a NLP task, for which I am unable to find any real relevant literature or other source of information. A chat consists of N messages where each message is sent by either person1 or a person2. In this chat questions from person1 are answered by person2. My goal is to label the individual messages as either a question, answer, or neither. Additionally, I am also trying to find which messages that are labelled as question constitute 1 question and messages that are labelled as answer constitute 1 answer and which questions and answers belong together.

During a session that lasted two hours, European Film Market (EFM) Horizon attempted to determine whether artificial intelligence (AI) is film's new normal. Moderated by AC Coppens (The Creatives' Catalysts), the conference, which encompassed five presentations, explored the presence of AI, in its various forms, at different stages of a film's life cycle. During her introductory keynote speech, Maja Cappello, head of the European Audiovisual Observatory's legal department, focused on the real or imaginary legal issues that the use of AI creates in fiction. Given that legal systems vary dramatically between regions, in the UK, it would probably be possible for AI to hold on to the rights, whereas that wouldn't be the case in continental Europe. Also, another possible issue arises in cases where a machine creates original content: who will be the owner of this product?