BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.3.7.4.2//EN
TZID:Europe/London
X-WR-TIMEZONE:Europe/London
BEGIN:VEVENT
UID:25@edgeaihub.co.uk
DTSTART;TZID=Europe/London:20251203T140000
DTEND;TZID=Europe/London:20251203T150000
DTSTAMP:20260115T153516Z
URL:https://edgeaihub.co.uk/events/national-edge-ai-hub-webinar-series-ser
 vice-oriented-evolution-of-modern-ai/
SUMMARY:National Edge AI Hub Webinar Series: Service-Oriented Evolution of 
 Modern AI
DESCRIPTION:\n\nJoin us for the next instalment of the National Edge AI Hub
 ’s webinar series\, "Service-Oriented Evolution of Modern AI\," by Dr. Z
 heng Li of Queen's University Belfast. His talk will explore the architect
 ural evolution of modern AI across three major waves: predictive\, generat
 ive\, and agentic. Drawing on analogical reasoning and lessons from softwa
 re engineering\, he reveals how AI is shifting from monolithic systems tow
 ard service-oriented\, collaborative paradigms.\n\nAttendees will gain ins
 ights into:\n\n 	The limitations of current AI approaches and emerging tre
 nds\n 	How service-oriented principles can guide future AI development\n 	
 Opportunities such as agent-friendly APIs and serverless AI agents\n 	Why 
 reusing software architecture knowledge can accelerate AI innovation\n\n\n
 \nWhen: December 3rd\, 2 PM GMT\nWhere: Zoom\, link will be provided to al
 l who register.\n\n\n\nAbstract: It is well known that understanding the e
 volution of technologies and its cause is essential for more discoveries a
 nd innovations. In the Artificial Intelligence (AI) domain\, it has also b
 een identified that scrutinising the development context and path of AI wi
 ll be able to help both academia and industry better understand the curren
 t AI limitations\, reveal future AI trends\, and facilitate AI/digital tra
 nsformations. Given the dramatic boom of modern AI\, this webinar tends to
  potentially unearth the evolution pattern along the recent three AI waves
  (namely predictive AI\, generative AI and agentic AI)\, and accordingly t
 o guide AI research and development to focus on the most promising directi
 ons. We employed analogical reasoning as the research method and referred 
 to the existing software architectural styles to inspire our understanding
  of the architectural evolution of modern AI technologies. We see a servic
 e-oriented trend in modern AI's working mechanisms\, and the offering of A
 I power seems to be transiting from a heavyweight and monolithic paradigm 
 to an organisational and collaborative paradigm with more and more specifi
 c separation of concerns. Following this service-oriented evolution trend\
 , we borrow software architecture lessons and foresee opportunities to gro
 w the current AI wave to a further height\, e.g.\, standardising AI agent-
 friendly APIs and developing serverless AI agents. Ultimately\, we draw te
 ntative conclusions: What is happening in the AI domain has happened befor
 e in the software engineering domain. It is worth reusing software archite
 cture knowledge to evolve the architecture of AI technologies.\n\nAbout th
 e Speaker: Zheng Li received his Ph.D. degree and M.Phil degree from the A
 ustralian National University (ANU) and the University of New South Wales 
 (UNSW) respectively. During the same time\, he was a graduate researcher w
 ith the Software Systems Research Group (SSRG) at National ICT Australia (
 NICTA). He is now a lecturer at the School of Electronics\, Electrical Eng
 ineering and Computer Science\, Queen’s University Belfast\, UK. Previou
 sly\, he was a tenured assistant professor at the Department of Computer S
 cience\, University of Concepción\, Chile. Before that\, he was a postdoc
  researcher with the Cloud Control group at Lund University\, Sweden. He w
 as also a visiting research fellow with Software Institute at Nanjing Univ
 ersity\, China. Before studying abroad\, he had around four-year industria
 l experience in China after receiving his M.Sc.Eng. degree from Beijing Un
 iversity of Chemical Technology and the B.Eng. degree from Zhengzhou Unive
 rsity. His research interests include big data analytics\, edge/cloud comp
 uting\, empirical software engineering\, and performance engineering.
CATEGORIES:Research Webinar,Webinar
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:STANDARD
DTSTART:20251026T010000
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
END:STANDARD
END:VTIMEZONE
END:VCALENDAR