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AAAI-20 Workshop Program

Analysis of content to measure affect and its experiences is a growing multidisciplinary research area that still has little cross-disciplinary collaboration.

The artificial intelligence (AI) and computational linguistics (CL) communities are making strides in identifying and measuring affect from user signals especially in language, while the human-computer interaction (HCI) community independently explores affect through user experience evaluations.

To address the scarcity of standardized baselines, datasets, and evaluation metrics for cross-disciplinary affective content analysis, submissions describing new language resources, evaluation metrics, and standards for affect analysis and understanding are also strongly encouraged.

Both full papers (8 page long including references) and short papers (4 page long including references) that adhere to the 2-column AAAI format will be considered for review.

The workshop will address applicable areas of AI, such as machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning and human machine interactions.

Additionally, with the concurrent advancements in machine learning capabilities, there are algorithms and tools with the impressive ability to automatically analyze and classify massive amounts of data in complex scenarios, but deploying them in specific domains can be challenging.

Principally among them are: 1) Determining optimal techniques to improve AI performance given targeted, limited human input, 2) understanding the extent to which the interaction between humans and AI introduces an attack surface for adversarial techniques to influence the performance of both the human and computer systems, 3) establishing and quantifying trust between humans and AI systems, 4) providing explainable AI where humans are required to do ‘last mile’ synthesis of information provided from a black box algorithm, and 5) defining the scope in which an AI system can operate autonomously in distinct cyber security domains while maintaining safety.

The challenge problem ( is focused on a representative cyber security task that generally requires human interaction.Understanding and addressing challenges associated with systems that involve human-machine teaming requires collaboration between several different research and development communities including: artificial intelligence, cyber-security, game theory, machine learning, human factors, as well as the formal reasoning communities.

In fact, the increasingly digitalized education tools and the popularity of the massive open online courses have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education.

For example, knowledge tracing, which is a intrinsically difficult problem due to the complexity under human learning procedure, has been solved successfully with powerful deep neural networks that can fully take the advantages of massive student exercise data.

For instance, researchers have also devoted to reducing the monotonous and tedious grading workloads of teaching professionals by building automatic grading systems that are underpinned by effective models from natural language process fields.

Despite aforementioned success, developing and applying AI technologies to education is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues.

This means that there are a number of challenging problems in sports to predict and optimise performance but, so far, such problems have largely been dealt with by domain experts (e.g., coaches, managers, scouts, and sports health experts) with basic analytics.

The growing availability of datasets in sports presents a unique opportunity for the artificial intelligence (AI) and machine learning (ML) communities to develop, validate, and apply new techniques in the real world.

While research in AI for team sports has grown over the last 20 years, it is as yet unclear how they relate to each other or build upon each other as they tend to either focus on specific types of team sports or specific prediction and optimisation problems that are but one part of the whole field.

We invite high-quality paper submissions on topics including, but not limited to, the following: We are keen to see applications of AI techniques such as: The workshop will be a full-day and will include a mix of invited talk(s) and peer-reviewed papers (talks and poster sessions).

This workshop provides a forum for researchers, data scientists and practitioners from both academia and industry to present the latest research results, share practical experience of building AI powered IoT solutions, and network with colleagues.

We must bridge the short-term with the long-term perspectives, idealistic with pragmatic solutions, operational with policy issues, and industry with academia, in order to build, evaluate, deploy, operate and maintain AI-based systems that are truly safe.

This workshop seeks to explore new ideas on AI safety with particular focus on the following questions: The main interest of the proposed workshop is to look holistically at AI and safety engineering, jointly with the ethical and legal issues, to build trustable intelligent autonomous machines.

We advocate the urgency of driving and accelerating AI/ML for efficient and manageable cloud services through collaborative efforts in multiple areas including but not limited to artificial intelligence, machine learning, software engineering, data analytics, and systems.This workshop provides a forum for researchers and practitioners to present the state of research and practice in AI/ML for efficient and manageable cloud services, and network with colleagues.

It is well- known that deep learning techniques that were disruptive for Euclidean data such as images or sequence data such as text are not immediately applicable to graph-structured data.This gap has driven a tide in research for deep learning on graphs on various tasks such as graph representation learning, graph generation, and graph classification.

This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, inductive logic programming, program synthesis and analysis, automated planning, reinforcement learning, and financial security.

Despite these successes, graph neural networks (GNNs) still face many challenges, namely, This one-day workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges.

Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged.

And with particular focuses but not limited to these application domains: Submissions are limited to a total of 4 pages for initial submission (up to 5 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 4 pages.

The main goal of this workshop is to share the results of the following four main tracks of DSTC8: The one-day workshop will include welcome remarks, track overviews, invited talks, oral presentations, poster sessions, and discussions about future DSTCs.

in evaluations of different types of AI systems (image recognition, recommender systems, search, voice assistants, etc).Collecting, examining and sharing current evaluation efforts, comprehensive of one system or competitive of multiple systems with the goal of critically evaluating the evaluations themselvesDeveloping an open repository of existing evaluations with methodology fully documented and raw data and outcomes available for public scrutiny Using crowdsourced datasets for evaluating AI systems’ success at tasks such as image labeling and question answering have proven powerful enablers for research.

While crowdsourcing has enabled a burst of published work on specific problems, determining if that work has resulted in real progress cannot continue without a deeper understanding of how the dataset supports the scientific or performance claims of the AI systems it is evaluating.

Topics of interest to this workshop bring together research being conducted in a range of areas, including classical planning, knowledge engineering, partial policies and reinforcement learning, plan verification, and model checking.

Submission deadline: November 1, 2019Notification: December 4, 2019 Javier Segovia-Aguas (Institut de Robòtica i Informàtica Industrial (IRI), Spain,, Siddharth Srivastava (Arizona State University, USA,, Raquel Fuentetaja (Universidad Carlos III de Madrid, Spain,, Aviv Tamar (Israel Institute for Technology, Israel,, Anders Jonsson (Universitat Pompeu Fabra, Spain, Supplemental workshop site: Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors.

Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns.

To provide proper alerts and timely response, public health officials and researchers systematically gather news and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources.

This is especially the case for non- traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data.

Moreover, to tackle and overcome several issues in personalized healthcare, information technology will need to evolve to improve communication, collaboration, and teamwork among patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties.

The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation in order to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting.

The extraction, representation, and sharing of health data, patient preference elicitation, personalization of “generic” therapy plans, adaptation to care environments and available health expertise, and making medical information accessible to patients are some of the relevant problems in need of AI-based solutions.

The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health.

This workshop also encourages submissions on the following interdisciplinary topics: We welcome submissions of long (6-8 pages) or short (2-4 pages) papers describing new, previously unpublished research in this field.

Dell Zhang, Cochair (Birkbeck, University of London, Malet Street, London WC1E 7HX, UK,, Andre Freitas, Cochair (University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL, UK,, Dacheng Tao (University of Sydney,, Dawn Song (UC Berkeley, Supplemental workshop site: This workshop is positioned as a forum to present and discuss novel research directions in interactive and conversational recommender systems as well as constituent AI technologies that represent the next generation of recommender systems and personalized, conversational assistants.

Encouraged by this recent interest in interactive and conversational recommender systems, the workshop aspires to bring together AI researchers from recommender systems, machine and reinforcement learning, dialog systems, natural language processing, human computer interaction, psychology and econometrics for a day of research presentations and open discussion about the future of this high impact and highly cross-disciplinary research area.

Topics include (but are not limited to): We welcome previously unsubmitted work, papers submitted to the main AAAI conference, and papers reporting research already published provided they align well with the workshop topic.

Three types of submissions are solicited: Paper Submissions should be made through the workshop EasyChair web site: Scott Sanner (University of Toronto), Tyler Lu (Google Research), Joyce Chai (University of Michigan), Deepak Ramachandran (Google Research)Email: Supplemental workshop site: Knowledge discovery from unstructured data has gained the attention of many practitioners over the past decades.

In spite of major AI research focusing on data sources like news, web, and social media, its application to data in professional settings such as legal documents and financial filings, still present huge challenges.

We invite submissions of original contributions on methods, applications, and systems on artificial intelligence, machine learning, and data analytics, with a focus on knowledge discovery and extraction in the financial services domain.

The scope of the workshop includes, but is not limited to, the following areas: We also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media.

Morgan Chase and Carnegie Mellon University,, Quanzhi Li (Alibaba Group,, Le Song (Ant Financial and Georgia Institute of Technology, Supplemental workshop site: Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior, i.e., their interaction with the environment and with each other.

This synergistic area of research combines and unifies techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning.

It plays a crucial role in a wide variety of applications including: This wide-spread diversity of applications and disciplines, while producing a wealth of ideas and results, has contributed to fragmentation in the field, as researchers publish relevant results in a wide spectrum of journals and conferences.

North, Suite 400Minneapolis MN 55401-1689Email: Additional InformationSupplemental workshop site: The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization.

Indeed, much scientific and technological growth in recent years, including in computer vision, natural language processing, transportation, and health, has been driven by large-scale data sets which provide a strong basis to improve existing algorithms and develop new ones.

Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and datasets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.

However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications needing decision making under practical constraints of resources and time (e.g., sequential decision making) and being fair to people chatbots interact with.

There is an urgent need to highlight the crucial role of reasoning methods, like constraints satisfaction, planning and scheduling, and learning working together with them, can play to build an end-to-end conversation system that evolves over time.

From the practical side, conversation systems need to be designed for working with people in a manner that they can explain their reasoning, convince humans about choices among alternatives, and can stand up to ethical standards demanded in real life settings.

Electronic submissions to be uploaded at Notifications: November 30, 2019Camera-ready copy due: December 3, 2019: Ullas Nambiar (Accenture, India), Imed Zitouni (Google, USA), Kshitij Fadnis (IBM Research, USA), Biplav Srivastava (IBM, USA) Supplemental workshop site: Question Answering (QA) has become a crucial application problem in evaluating the progress of AI systems in the realm of natural language processing and understanding, and to measure the progress of machine intelligence in general.

The workshop welcomes three kinds of paper submissions:(i) challenge papers (up to 2 pages long) that describe a new challenge in the workshop’s focus area;(ii) short papers (up to 4 pages long) which focus on a single, specific contribution;

Kartik Talamadupula, IBM ResearchVered Shwartz, University of Washington / AI2Jay Pujara, ISI / USCRachel Rudinger, AI2 / UMDMausam, IIT DelhiNanyun Peng, ISI / USCPavan Kapanipathi, IBM Research Supplemental workshop site: Games provide an abstract and formal model of environments in which multiple agents interact: each player has a well-defined goal and rules to describe the effects of interactions among the players.

We invite participants to submit papers based on, but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games;

It will start with a 60-minute mini-tutorial covering a brief tutorial and basics of RL in games, 2-3 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel.

Reproducibility ChallengeAs input to the discussion on recommendations, we will emphasize the submission and acceptance of papers in which researchers describe their experiences from attempting to reproduce a paper(s) accepted at a previous AAAI conference(s) (i.e., try to reproduce the results from a previous AAAI conference paper and report your results).

Submissions should contain a description of the experiment, whether the results of the original paper were reproduced or not, a discussion on reproducibility challenges, lessons learned, and recommendations for best practices as well as a short note on each of the 24 variables presented in by Gundersen, Gil and Aha (2018) (

The workshop will last span a full day and will include invited talks, oral and poster presentations of submitted work, a panel and open discussion on how to make research results presented at AAAI reproducible.

Aha (Naval Research Laboratory,, Daniel Garijo (Univeristy of Southern California, Supplemental workshop site: The purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from three fields: logical (or relational) AI/learning, probabilistic (or statistical) AI/learning and neural approaches for AI/learning with knowledge graphs and other structured data.

Specifically, the workshop will encourage active participation from researchers in the following communities, and integration thereof: satisfiability, knowledge representation, constraint satisfaction and programming, (inductive) logic programming, graphical models and probabilistic reasoning, statistical learning, relational embeddings, neural-symbolic integration, graph mining and probabilistic databases.


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