AI, machine learning, robotics: trends, challenges, opportunities

Speaker: Danica Kragić, School of Computer Science and Communication at the Royal Institute of Technology, KTH , Sweden

Short biography:
Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. She chaired IEEE RAS Technical Committee on Computer and Robot Vision and served as an IEEE RAS AdCom member. Her research is in the area of robotics, computer vision and machine learning. In 2012, she received an ERC Starting Grant and 2020 ERC Advanced Grant. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council. She is an IEEE Fellow.

Building machines that are autonomous and intelligent, taking over dirty, dull and dangerous jobs, has been an integral part of human history for a long time. Recent advances in robotics, artificial intelligence and machine learning have demonstrated how these can be utilized in development of technologies that exhibit rather advanced capabilities. In integration with human decision making and experience, artificial systems are today used to make diagnostics in health application, make estimations of weather conditions to secure crops, provide more informed predictions of potential earthquakes, and more. Apart from purely software solutions, we are also seeing the beginning of more advanced hardware solutions, robotic systems that are equipped with various sensor technologies and are built to physically interact with humans at workplaces, and sometimes in the future, even our homes. Humans poses a fantastic ability to acquire complex behaviors from watching another person. It is remarkable to observe how even small children acquire advanced object manipulation skills, first through observation, but then master these through training: extensive and rich interaction with the environment using not only vision but also proprioception and haptic feedback. Initial observations are needed to understand goals of complex behaviors but repeated, extensive interactions with the physical world are necessary to ground the behaviors in own sensing and reuse these in new situations. Robots acquiring behaviors solely from human demonstrations and unstructured videos rather than through explicit programming has been a research vision for a long time – probably even from the time a first robot was built. But still, having a robot that is able to adapt and enrich its knowledge through self-supervised learning remains one of the open challenges. Thus, to be deployed in natural environments, robots need the ability to learn skills autonomously, through continuous interaction with the environment, humans and other robots. Although classically built on rigorous control theory, mathematical and theoretical computer science methodologies, more recently data-driven learning methods, such as Deep Learning and Reinforcement Learning have been demonstrated as powerful technologies for developing robotic systems. Still, most of the practical applications exist in solely carefully structured settings where there exists enough data to train the models.


Toward AI-Human Hybrid Teaming

Speaker: Jonathan Cagan, George Tallman and Florence Barrett Ladd Professor in Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

Short biography:
Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering, Department of Mechanical Engineering, at Carnegie Mellon University, with courtesy appointment in Design. His research focuses on engineering design automation and methods, merging AI, machine learning, and optimization with cognitive science problem solving. With nearly 300 peer-reviewed publications and multiple patents, both his design methods and computer-based design research have been applied in a variety of industries. A Member of the Design Society and Fellow of the American Society of Mechanical Engineers, Jon is recipient of the ASME Design Theory and Methodology, Design Automation, and Ruth and Joel Spira Outstanding Design Educator Awards.

Teams are a core part of the engineering design process. Prior computational design tools have served to support the human team, applied to distinct tasks to output specific information or calculations. As smart (AI) agents emerge, their role has the potential to be different, contributing to teams as a proactive partner or even manager of the human design process. This talk will examine engineering teaming and ways that AI is evolving to change the performance and dynamics of teams. AI agents that assimilate historical and real time data are proving to impact team output, communication efficiency, and group behavior during problem solving. The talk will illustrate recent results and motivate future research in this emerging area of study and practice.


Which way I ought to go?

Speaker: Zrinka Čorak, Vicepresident, INETEC, Croatia

Biographical Sketch:
Zrinka Čorak is Vice-Chairman of INETEC. She completed a Master of Electrical Engineering at the University of Zagreb and earned PhD at University of Zagreb. She graduated from Harvard Business School, Executive Education Program. She is one of the founders of the family foundation Iter Meum Illumina.

The journey of INETEC started as a small family business company focused on providing NDT inspection activities of nuclear power plants 30 years ago. We have grown into a renowned company active in developing robots, scanners, control units, probes and software for navigation, as well as performing the NDT inspection. We continue to provide the NDT inspection within the scope our operations.
With innovation as one of our core values, we have brought forth innovative solutions in technology and business models. We have strived to balance between short-term exploitation of existing markets and long-term exploration of new opportunities.
This presentation outlines how an innovation-driven company has evolved throughout the years across product ranges and business models.

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