AiTech Agora

The ancient Greek word Agora refers to a public open space used for assemblies and markets. It captures the informal nature of our weekly meetings, a place where exchange of knowledge, ideas, and an engaging conversation takes place.

  • Due to the COVID-19 situation, we will organize our meetings virtually for the foreseeable future
  • If you wish to join our upcoming meetings, please subscribe to the AiTech Agora mailing list here, or just send an email with a subscription request to aitech@tudelft.nl. You will then start receiving weekly invitations containing the link to the virtual meeting room and the password
  • If you haven't received the invitation through our mailing list and want to join a particular meeting at short notice, please email aitech@tudelft.nl 
  • Presentation slides from our past meetings are available here. We also record some of our online meetings, the past recordings are available here

Next meeting

May 12, 13:00 – 14:00 CEST

Ujwal Gadiraju: Towards Conversational Human-AI Interaction

Conversational interfaces have been argued to have advantages over traditional GUIs due to having a more human-like interaction. The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. There is a growing familiarity among people with conversational interactions mediated by technology due to the widespread use of mobile devices and messaging services. Today, over half the population on our planet has access to the Internet with ever-lowering barriers of accessibility. In this talk I will present an overview of our recent work, showcasing the benefits of employing novel conversational interfaces in the domains of Health and Well-being, Web Search and Crowd Computing. I will make a case for building conversational human-AI interactions as we shape the future of interaction with AI systems.

 

Bio:

Ujwal Gadiraju is an Assistant Professor at the Web Information Systems group of the Faculty of Engineering, Mathematics and Computer Science (EEMCS/EWI), Delft University of Technology. Ujwal is a Director of the Delft Design@Scale AI Lab and co-leads the research line on Crowd Computing and Human-Centered AI at the WIS group. He is an ACM Distinguished Speaker. Prior to joining the WIS group, Ujwal worked at the L3S Research Center  as a Postdoctoral researcher between 2017-2020. Ujwal received a PhD (Dr. rer. nat.) in Computer Science from the Leibniz University of Hannover, Germany in 2017, and an MSc. Computer Science degree from TU Delft, the Netherlands in 2012. His research interests lie at the intersection of Human-Computer Interaction (HCI) and Information Retrieval (IR), with a special focus on Crowd Computing and Human-AI interaction. Ujwal’s prior work has explored methods to improve the effectiveness of the crowdsourcing paradigm, running large-scale human-centered experiments to understand the interaction between humans and machines, and understanding the societal impact of algorithmic decision-making. For more information see – https://www.ujwalgadiraju.com

 

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Best regards,

AiTech Team

 

Would like to join? Send us an email and we will forward the meeting details (aitech  <> tudelft.nl).

Upcoming meetings

May 12: Ujwal Gadiraju

May 12: 13:00-14:00

Ujwal Gadiraju: Towards Conversational Human-AI Interaction

 

May 19: Jie Yang

May 19: 13:00-14:00

Jie Yang:

ARCH: Know What Your Machine Doesn’t Know

 

Abstract: Despite the impressive performance of machine learning in many tasks, the reliability of those systems remains a severe issue that has largely impeded their application to domains with safety and ethical requirements. Recent discussions in different sub-fields of AI have reached the consensus of knowledge need in machine learning; few discussions have touched upon the diagnosis of what knowledge is needed. In this talk, I will present our ongoing work on ARCH, a human-in-the-loop, reasoning-based tool, for diagnosing what a machine learning system does not know. ARCH leverages human intelligence by engaging domain experts or users to create domain knowledge required for a given task and involving crowds to describe the internal behavior of a machine learning system. With such knowledge of the task and the system, ARCH infers the missing and incorrect knowledge of the system using its built-in probabilistic, abductive reasoning engine. ARCH is a generic tool that can be applied to machine learning in different contexts; in the talk, I will present several applications in which ARCH is currently being developed and tested, including health, finance, smart building, and e-commerce.

May 26: Pradeep Murukannaiah

May 26: 1300-1400 CEST

TBD

 

June 9: Sebastian Kohler

June 9: 13:00-14:00

Sebastian Kohler: TBD