Dr. Steve Chan
S&T Advisor
Brief biography:
Dr. Steve Chan serves as a Science & Technology (S&T) Advisor for various organizations within government and industry. He is an inventor with 14 U.S. and international patents, and he is the author of 88 peer-reviewed book chapters, journal articles, and conference papers, which include 37 IEEE papers (for 62 of the refereed publications, he is first author, and 19 of these received Best Paper/Best Presentation awards). He has served as Chief Scientist/Principal Investigator for dozens of governmental/intergovernmental reports (e.g., U.S. Government, ASEAN Member States, United Nations), co-founded numerous research centers within industry (e.g., Fortune 500) as well as academia, and served as center advisor for National Science Foundation-funded consortiums of industry and government laboratories. He previously served as Vice President/Chief Innovation & Strategy Officer for IBM’s Safer Planet & Smarter Cities Division, Chief Technology Officer/Chief Architect roles at MIT, and Senior Fellow at Harvard. He is an alumnus of both MIT and Harvard. His presentations and lectures have been featured at the White House, National Research Council of the National Academies, and World Congress on Information Technology.
Talk Title:
Abstract:
In contemporary times, there has been an advent of, in theory, more user-friendly automated Artificial Intelligence-centric Systems (AIS). The connected Internet of Things (IoT) environment has enabled not only enhanced contextual information for such AIS, but has also facilitated users being able to more naturally and intuitively interface with such IoT devices verbally, textually, etc. As updates/insights are received and users increasingly engage in an Observe-Orient-Decide-Act (OODA) loop fashion, the reliability, consistency, and validity of the exchanges between users and the involved AIS become critical. For this reason, AI incoherency/hallucinations remain a challenge, particularly as AIS adoption continues to increase. Interestingly, a Large Concept Model (LCM)-centric — as contrasted to a Large Language Model (LLM)-centric — approach may have some mitigation promise in this regard. Along with this, the leveraging of certain Reasoning Methods/Processes-related mechanisms to stay more on the monotonic (as contrasted to the non-monotonic) side may also be of value-added proposition on the mitigation front. The described amalgam may offer an interesting pathway to navigate the AI coherency challenge.
Prof. Dr. Andry Alamsyah, S.Si, M.Sc
Telkom University
Brief Biography:
Prof. Dr. Andry Alamsyah is a distinguished academic and researcher specializing in Digital Business Strategy, Artificial Intelligence, and Blockchain Technology. He currently serves as a professor at Telkom University, where he also leads the Artificial Intelligence Committee and the School of Economics and Business Senate. With a Ph.D. from Institut Teknologi Bandung, Andry’s research interests include social computing, big data analytics, blockchain innovation, and the digital economy. He is actively involved in various research projects and has contributed to numerous publications in international conferences and journals. Additionally, he is the founder of the Indonesian Data Scientist Association (AIDI) and holds leadership roles in multiple professional communities, focusing on advancing technology and innovation in Indonesia.
Talk Title:
The Transformative Role of Artificial Intelligence in Society and the Economy
Abstract:
The rapid advancement of technology has profoundly transformed the relationship between innovation, the economy, and human development. Among these advancements, Artificial Intelligence (AI) stands out as the key driver of change—reshaping industries, redefining productivity, and influencing decision-making at every level of society. AI is not merely a technological tool; it is a catalyst for a more intelligent, efficient, and inclusive digital era. Supported by complementary technologies such as blockchain and data analytics, AI plays a pivotal role in shaping the digital economy by enabling automation, transparency, and personalization. Through AI’s ability to learn, adapt, and generate insights, organizations can accelerate innovation while maintaining ethical integrity and environmental responsibility.
As AI-driven ecosystems continue to expand, a new wave of intelligent services and applications is emerging—ranging from digital finance and smart cities to healthcare, education, and public administration. However, the growing influence of AI also raises complex challenges related to data security, privacy, and regulatory oversight. Ensuring algorithmic transparency, protecting user trust, and developing adaptive governance frameworks have become essential to maintaining fairness and accountability in the digital economy. The future of technology, therefore, depends on our ability to harness the power of AI responsibly—balancing innovation with regulation, efficiency with ethics, and progress with protection. When guided by strong governance and shared human values, AI can serve not only as an engine of economic growth but also as a foundation for social resilience and sustainable development.