KEYNOTES

Human rights in digital era

Abstract:
The lecture deals with the legal problems of Human Rights in the digital era. Based on the analysis of the case law of the Court of Justice of the EU, the impact on the digital age is visible, especially through the emphasized data protection and the right to privacy. Do we have “right to be forgotten” and is the transfer of data of Facebook users to the US allowed? In the future, there will be important questions regarding the role of judges and robots. Will the robot adjudicate in litigation before courts? Will the robot take over the role of a judge as a human being?

Bio:
Prof. Dr. Verica Trstenjak is former Advocate General of the European Court of Justice in Luxembourg and a professor of European Union law. From 2004 to 2006 she was a judge at the General Court of the European Union in Luxembourg and from 2006 to 2012 an Advocate General of the European Court of Justice in Luxembourg (the highest legal position in EU). Dr. Trstenjak is Professor of European law in Austria and Slovenia (at University in Ljubljana (part time) and at different LLM and other university programs in Austria (University of Vienna, Sigmund Freud University in Vienna) and teaches European law at the summer schools (University of Vienna (Strobl), University of Salzburg, University of Innsbruck – university course in Alpbach). She has written expert opinions for law firms and arbitration in many cases concerning European law and legal protection in the EU. She has published several books and more than 300 articles (also with SSCI) and has been a lecturer at different universities and at different international and European conferences. In 2020 she got an Austrian state decoration from Austria President Van der Bellen: Cross of Honour for Science and Art, First Class.

Diversity, Bias, online social relations and related issues

Abstract:
The Web has transformed our lives by allowing us to transcend temporal, geographical and cultural borders. It is a fact that we get exposed daily to a seemingly unbound amount of diversity and, therefore, of opportunities; think for instance of the emergence of global digital platforms. At the same time, the Web often does not allow for prolonged interactions, thus amplifying the negative effects of diversity, in particular when it is unknown and unexpected; think for instance of the emergence of online fake news, bias and unethical behavior. In this talk we distinguish between two types of diversity, that we call inessential and essential diversity. We start from an analysis of inessential diversity, by far the one mostly studied so far, trying to put the current work on various related issues e.g., bias, transparency, fairness, under a new perspective. The second part of the talk will concentrate on essential diversity describing the first results of a large project whose main goal is to exploit diversity towards the empowerment of new types of online social relations.

Bio:
Fausto Giunchiglia: Professor of Computer Science, University of Trento, EURAI fellow, member of the Academia Europaea. Previously studied or had positions at the Universities of Genoa, Stanford, Edinburgh, and at the research center IRST (now FBK, Trento). Current research interest: Computational models of the mind and implications on how the known is grounded in the unknown. Around 10 Best Paper Awards; 50+ invited talks in International events; chair of 10+ international events, among them: ICDCS, KSEM, ODBASE, IJCAI, KRR; editor or editorial board member of around 10 journals, among them: Journal of Autonomous Agents and Multi-agent Systems, Journal of applied non Classical Logics, Journal of Software Tools for Technology Transfer, Journal of Artificial Intelligence Research. He was member of the IJCAI Board of Trustees, President of IJCAI, President of KR, Inc., Advisory Board member of  KR, Inc., Steering Committee of the  CONTEXT conference. Fausto has covered all the spectrum from theory to technology transfer and innovation. He was involved in more than 20 international R&D projects, including several EC projects. Relevant to the topic of this talk, Fausto is currently involved in the following projects: WeNet – The Internet of Us. https://www.internetofus.eu/ CYCAT – Center for Algorithmic Transparency. http://www.cycat.io/ More details can be found at: http://knowdive.disi.unitn.it/

Knowledge Graphs & Natural Language in Finance

Abstract:
Decision-making in finance involves discovering and summarizing relevant information, generating trade ideas, finding liquidity and counterparties, performing post-hoc analyses, and publishing reports. At Bloomberg, we leverage recent developments in machine learning, knowledge graphs, and language technology to enable intelligent ways for our clients to obtain market advantage in every step of their decision making process, at scale, and with high precision and low latency. In this talk, we cover the use of the Bloomberg Knowledge Graph and advanced natural language processing (NLP) techniques in the following areas: (a) information and relationship extraction to assist our journalists with automated news generation, (b) named entity recognition and linking, topic classification, clustering, and summarization to assist our clients in consuming content, (c) language modeling and semantic parsing to facilitate natural-language-based discovery of content, (d) dialog understanding to aid in structuring instant messages, the primary medium for over-the-counter trading, and (e) sentiment analysis to generate structured time-series signals for alpha generation. Throughout the talk, we will highlight articles published by our group that can serve as further reading material.

Bio:
Prabhanjan (Anju) Kambadur is the head of the AI Engineering group at Bloomberg, which consists of 180+ researchers and engineers building financial solutions using machine learning, natural language processing, graph analytics, time series analysis, information retrieval, recommender systems, computer vision, and optimization. Before joining Bloomberg, Anju was a research staff member in the Business Analytics and Mathematical Sciences department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning such as matrix sketching, genome-wide association studies, temporal causal modeling, and high performance computing. He received his Ph.D. from Indiana University and has published peer-reviewed articles in the fields of high performance computing, machine learning, and natural language processing.

The Geography of Social Ties

Abstract:
This talk will describe how social networks crisscross the world. Geographic patterns emerge from historical borders, as well as migrations past and present. They correlate with myriad different phenomena, from trade to COVID cases. Furthermore, having social connections to regions experiencing changes, for example in house prices or COVID cases, is tied to corresponding responses in house buying or social distancing, beyond what is expected based on changes within one’s own region.

Bio:
Lada Adamic leads the Computational Social Science Team at Facebook. Prior to joining Facebook she was an associate professor at the University of Michigan’s School of Information and Center for the Study of Complex Systems. Her research interests center on information dynamics in networks. She has received an NSF CAREER award, a University of Michigan Henry Russell award, the 2012 Lagrange Prize in Complex Systems.

Commonsense Intelligence: Cracking the Longstanding Challenge in AI

Abstract:
Despite considerable advances in deep learning, AI remains to be narrow and brittle. One fundamental limitation is its lack of common sense: intuitive reasoning about everyday situations and events, which in turn, requires a wide spectrum of commonsense knowledge about how the physical and social world works, ranging from naive physics to folk psychology to ethical norms. In this talk, I will share our recent adventures in modeling neuro-symbolic commonsense models by melding symbolic and declarative knowledge stored in large-scale commonsense graphs with neural and implicit knowledge stored in large-scale neural language models. I will conclude the talk by discussing the needs for departing from the currently prevalent learning paradigms that lead to task- or even dataset-specific learning, and open research questions for commonsense AI in light of human cognition.

Bio:
Yejin Choi is a Brett Helsel associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research manager at AI2 overseeing the project Mosaic. Her research interests include commonsense knowledge and reasoning, neural language (de-)generation, language grounding, and AI for social good. She is a co-recipient of the AAAI Outstanding Paper Award in 2020, Borg Early Career Award (BECA) in 2018, IEEE’s AI Top 10 to Watch in 2015, the ICCV Marr Prize in 2013, and the inaugural Alexa Prize Challenge in 2017.

Implications and Explanations in User-Generated Data

Abstract:
User-generated data (such as reviews and community Q&A) are a rich source of user insights and experiences that can be very helpful in many different daily life situations, such as when deciding what product to buy, or what hotel to stay. Given its relevance, there has been growing interest in designing systems that can automatically extract knowledge from such types of data. In many cases, in order to extract knowledge from user-generated data it is necessary to make commonsense inferences that will help to better understand the context and the narrative being presented. In this talk I’ll start by going over different approaches that take advantage of commonsense knowledge to solve different downstream tasks related to natural language understanding and artificial intelligence. Then, I’ll focus on the specific characteristics of “user-generated” data and discuss two possible ways that we can frame commonsense: through implications and through explanations.

Bio:
Estevam Hruschka is a Staff Research Scientist at Megagon Labs. Prior to Megagon Labs, he was co-leader of NELL (the Never-Ending Language Learning System). Also, he was an associate professor of computer science at the Federal University of Sao Carlos (Brazil), Fellow of the Brazilian National Research Agency, Young Research Fellow of the Sao Paulo State Research Agency (in Brazil), Google Research Award recipient (for Latin America) and a Visiting Professor at Carnegie Mellon University (Pittsburgh, PA). His work focuses on both theoretical and applied problems and he is mainly interested in how to build intelligent computer systems capable of deeply understanding Natural Language in a Never-Ending Learning approach. Between 2017 and 2020, he was with Amazon (Alexa Search Team) in Seattle, WA.

The web of data: how are we doing so far?

Abstract:
Throughout its history, the web has shaped our understanding and interactions with data. In the age of AI, this is mostly the data that it helps create, find, organise, and utilise, through its myriad of interconnected applications and user communities. This data takes many forms: digital traces we leave behind while being online, user-generated content, datasets published by scientists and government, or labels produced on crowdsourcing platforms to train machine learning algorithms. The web of data was supposed to bring it all together through links, metadata, shared vocabularies, and standardised technologies. We’ve come a long way since the first linked open data cloud was published in 2007 with 12 datasets. But we’ve also encountered road blockers that we’re still to overcome. Huge investments have been made in opening different streams of data to developers, yet publishers struggle to show evidence of the impact of these investments and become sustainable. Finding and making sense of data online is as critical as it has ever been, especially as more and more jobs come to rely on it. Lots of data turns out to be flawed, eroding our social bonds and trust in institutions. Despite a rise in knowledge graphs, data siloes are more common than ever and governments are building virtual borders to data flows in the name of digital sovereignty. In this talk I will present recent research that provide insights into the state of the web of data today. Just like the web stands for a melange of services and platforms, from search to shopping to social networks, the web of data is a concept with multiple facets – we need to unpack these different facets to understand how much progress we’ve made and where the challenges really lie. Data on the web is not just about the standards and protocols promoted by the linked open data cloud; in a wider interpretation, it amounts to the (sparsely linked) graph of web tables embedded in documents, to millions of online datasets in various formats, but also to charts that present data in accessible ways. The web of data is a mechanism to publish and reuse data, a social network, a marketplace, and a platform to help train the AIs of this world, all affected to a larger or lesser degree by technopolitics. I will discuss these different interpretations, supported by studies into open data portals, data communities, and crowdsourced datasets, and deep-dive into technical, user experience, innovation and policy questions and their impact on present and future developments in this space.

Bio:
Elena Simperl is professor of computer science at King’s College London, a Fellow of the British Computer Society and former Turing fellow. According to AMiner, she is in the top 100 most influential scholars in knowledge engineering of the last decade, as well as in the Women in AI 2000 ranking.  Before joining King’s College early 2020, she held positions at the University of Southampton, as well as in Germany and Austria. She has contributed to more than 20 research projects, often as principal investigator or project lead. Currently, she is the PI of two grants: H2020 ACTION, where she develops human-AI methods to make participatory science thrive, and EPSRC Data Stories, where she works on frameworks and tools to make data more engaging for everyone. She authored more than 200 peer-reviewed publications in knowledge engineering, semantic technologies, open and linked data, social computing, crowdsourcing and data-driven innovation. Over the years she served as programme and general chair to several conferences, including the European and International Semantic Web Conference, the European Data Forum and the AAAI Conference on Human Computation and Crowdsourcing.

Enabling the quantum revolution — pioneering advances to achieve quantum computing and impact at scale

Abstract: Pioneering the next revolution requires scientific, technological, and community development. It requires discovery and innovation, partnership and collaboration. We’re traveling on a revolutionary journey together — a journey to scale. Solutions to planet-scale challenges require us to redefine computing to unlock breakthroughs. Redefining computing to be empowered by quantum mechanics — quantum computing — promises to enable some of these breakthroughs. But redefining computing requires redefining the full stack, in order to realize the full potential of quantum computing at scale. It also requires redefining how to work together and achieve breakthroughs. I’ll describe how we are defining a new era of computing, what we need to do to reach the full potential of quantum computing, and the challenges we need to continue to tackle, together, as a community to spark the next revolution in computing.

Bio: Dr. Krysta Svore is General Manager of Quantum Systems at Microsoft. She believes empowering people with the power of quantum computing, today and tomorrow, will be one of the greatest revolutionary steps in our history. She leads a team dedicated to realizing a commercial-scale quantum computing system and ecosystem to solve today’s unsolvable problems. She spent her early years at Microsoft developing machine-learning methods for web applications, including ranking, classification, and summarization algorithms. In 2018, Dr. Svore was named one of the 39 Most Powerful Women Engineers according to Business Insider. Dr. Svore serves as a member of the Advanced Scientific Computing Advisory Committee of the Department of Energy and as a member of the ISAT Committee of DARPA. She is an appointee of the National Quantum Initiative Advisory Committee. She has received an ACM Best of 2013 Notable Article award and was a member of the winning team of the Yahoo! Learning to Rank Challenge in 2010. She chaired the 2017 Quantum Information Processing Conference. Dr. Svore is a Kavli Fellow of the National Academy of Sciences, a Senior Member of the Association for Computing Machinery (ACM), a representative for the Academic Alliance of the National Center for Women and Information Technology (NCWIT), and a member of the American Physical Society (APS). Dr. Svore has authored over 70 papers and has filed over 25 patents. She received her PhD in computer science with highest distinction from Columbia University and her BA from Princeton University in Mathematics with a minor in Computer Science and French.

AI Grand Challenges: Past, Present and Future

Abstract:
Innovative, bold initiatives that capture the imagination of researchers and system builders are often required to spur a field of science or technology forward. A vision for the future of AI and some significant challenge goals were laid out by Turing Award winner and Carnegie Mellon University Professor Raj Reddy in his 1988 AAAI Presidential address and published in AI magazine then. Recently – in the Spring 2021 issue of the same magazine – I provided an accounting of the progress that has been made in the field, over the last three decades, towards the challenge goals. While some tasks such as the world-champion chess machine were accomplished in short order, many others such as self-replicating systems require more focus and breakthroughs for completion. A new set of challenges for the current decade were also proposed, spanning the Health, Wealth and Wisdom spheres. Commentary by thought leaders from different vantage points was also presented to round out the analysis and avenues for future research. In this keynote on Earth Day, I will try and summarize some of the above and hint at how AI can aid progress towards the UN’s Sustainable Development Goals (SDGs). I hope that the discussion sparks additional innovation – spanning the technical, policy and social pillars – in the process of weaving an AI Web for Good. Easy-to-use AI systems that act independently in stimulus-rich environments and also team with humans in confusing scenarios is what the designers are seeking. The mantra should be: Of the people, by the people with machines; for the people!

Bio:
Ganesh Mani is an adjunct faculty member at Carnegie Mellon University. Ganesh co-founded Advanced Investment Technology (combining investment management and machine learning), which was acquired by State Street Corporation, creating its Advanced Research Centre for helping manage multibillion-dollar institutional portfolios. Ganesh has contributed to other AI start-ups, including an entity that’s now part of Nasdaq-listed iCAD, employing machine-learning techniques for early cancer detection. Ganesh has an MBA in finance, a PhD in AI from the University of Wisconsin-Madison and a BTech in Computer Science from IIT Bombay. He is a board member of The Indus Entrepreneurs (TiE.org), Pittsburgh chapter, and on the advisory board of the FDP Institute. Ganesh’s work has been patented and featured in a Barron’s cover story as well as in academic journals.