Building the Intelligent and Integrated Infrastructure for Low-Altitude Economy

Abstract

As a natural resource, lower airspace has long been neglected and has not been fully utilized. In recent years, with the rapid development and applications of new low-altitude aircrafts such as drones and eVTOLs, low altitude flights and related activities open the doors to lower airspace and create a new form of economy. However, how to enable safe, orderly, efficient and cost effective "high heterogeneity, high-density, high-frequency, and high-complexity" low altitude flying activities, accelerate the development of large-scale low-altitude economy and create a trillion-dollar new industry, is still a burning core issue that needs to be solved urgently. In response to this problem, the International Digital Economy Academy (IDEA) is investing on the research and development of an intelligent and integrated infrastructure for low-altitude economy at Shenzhen, by digitizing the entire lower airspace into a computable airspace through various sensing and monitoring technologies, developing the low-altitude airspace operating system – smart and integrated lower airspace system (SILAS). SILAS aims to solve the "undetectable, unreachable, uncontrollable" problems today with the aircrafts in the lower airspace and enable efficient and safe large-scale low-altitude flights. SILAS serves both the upstream air traffic control authorities by providing new digital tools to open airspace and manage airspace, routes, and flights; and the downstream operating companies for low altitude economy by providing them with flight services and safe-guards for their large-scale low-altitude flight activities; It provides means to fully utilize the lower airspace and exploit its value. This talk will share some of the preliminary work done by the team. We hope that this talk could provide some valuable inspirations for the development of this new form of economy.

Biography

Dr. Shipeng Li is a Chair Scientist at International Digital Economy Academy (IDEA) and the Managing Director of Low-Altitude Economy Research (LASER) Institute. He was the Executive Director of Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). He was a founding member of Microsoft Research Asia (MSRA) and served as the Research Area Manager. Dr. Li was the EiC (2018-2019) of IEEE Transactions on Circuits and Systems for Video Technology. He is quite influential in multimedia, IoT, artificial intelligence and low-altitude economy areas, and holds 206 US patents and published 330+ papers with 28,300+ citations (H-index: 88). He has trained 4 MIT TR35 Innovator Award Winners. Dr. Li received B.S. and M.S. from USTC and Ph.D. from Lehigh University. He is a Standing Director for the Chinese Institute of Electronics, the co-founder and Joint Secretary General of AI Industry Technology Innovation Strategic Alliance (AITISA) under China MOST. Dr. Li is an IEEE Fellow and an IEAS (International Eurasian Academy of Science) Academician.

The first AGI will be Federated

Abstract

As established scaling laws indicate, the future performance improvements of LLMs depend on the amount of computing and data sources we can leverage. Where will we get the necessary compute and data to drive the continued advances in LLMs that the world now has grown to expect? I believe all roads lead to federated learning. Federated and de-centralized approaches to machine learning will be how the strongest LLMs (and foundation models more generally) are trained in the relatively near future; and in time, we will see federated as one of the core enablers of the entire AI revolution. In this talk, I will describe why the future of AI will be federated, and describe early solutions developed by Flower Labs and CaMLSys that address the underlying technical challenges that the world will face as we shift from a centralized data-center mindset to de-centralized alternatives.

Biography

Nic Lane (http://niclane.org) is a full Professor in the department of Computer Science and Technology at the University of Cambridge and holds a Royal Academy of Engineering Chair in De-centralized AI. He is also a Fellow of St. John’s College. At Cambridge, Nic leads the Cambridge Machine Learning Systems lab (CaMLSys; https://mlsys.cst.cam.ac.uk/). The mission of CaMLSys is to invent the next-generation of breakthrough ML-centric systems. Alongside his academic roles, Nic is the co-founder and Chief Scientific Officer of Flower Labs (https://flower.ai), a venture-backed AI company (YCW23) behind the Flower open-source federated learning framework. Flower Labs seeks to enable an AI future that is collaborative, open and decentralized. Nic has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering research, performed during his PhD thesis, that devised machine learning algorithms used today on devices like smartphones. Nic was the 2020 ACM SIGMOBILE Rockstar award winner for his contributions to “the understanding of how resource-constrained mobile devices can robustly understand, reason and react to complex user behaviors and environments through new paradigms in learning algorithms and system design.”

Embedded AI Systems for Autonomous Driving and Smart Health

Abstract

Embedded Artificial Intelligence is rapidly emerging as a transformative computing paradigm, enabling intelligent, real-time, and privacy-preserving interactions with the physical world. In this talk, I will present our recent work on Embedded AI, including real-time inference on resource-constrained platforms, and AI-empowered systems for autonomous driving and smart health.

First, I will introduce a novel real-time deep learning framework that integrates model architecture optimization with real-time scheduling to enable the concurrent execution of multiple deep learning tasks. I will then discuss Soar, the first end-to-end intelligent roadside infrastructure system designed to enhance the safety of autonomous driving. Deployed on the campus of CUHK, this system is the first real-world open testbed that showcases the potential of infrastructure-assisted autonomous driving. Next, I will discuss several new systems that achieve real-time 3D collaborative perception and high-definition (HD) mapping for autonomous driving, with centimeter-level accuracy. Finally, I will introduce our work on integrating multi-modal sensors, federated learning algorithms, and foundation models to monitor multidimensional digital biomarkers associated with aging-related degenerative diseases. This system addresses key challenges such as limited data labels, data heterogeneity, and constrained computational resources. In collaboration with medical experts and local hospitals, we are currently deploying this system in large-scale clinical trials involving over 1,000 subjects.

Biography

Prof. Guoliang Xing is currently a Professor in the Department of Information Engineering at The Chinese University of Hong Kong, an IEEE Fellow, and received his Ph.D. from Washington University in St. Louis in 2006. He was previously an Assistant Professor and Tenured Associate Professor at Michigan State University, USA, from 2008-2017. He received the US NSF CAREER Award in 2010, the Withrow Distinguished Faculty Award from Michigan State University in 2014, and the Research Excellence Award and the Outstanding Fellow Award from CUHK in 2024. Prof. Xing's group currently leads several large-scale Embedded AI projects on urban smart infrastructure, autonomous driving, and smart health. His work has received six Best Paper Awards, four Best Demo/Poster Awards, and seven Best Paper Finalist honors at premier international conferences including MobiCom, MobiSys, SenSys, and IPSN. Several mobile technologies developed by Prof. Xing have been commercialized by the industry.