Dr. Bulent Atalay, Professor Emeritus of Physics at the University of Mary Washington and the University of Virginia, USA
History is marked by cultural revolutions, births, and, sometimes, rebirths. Almost two millennia after the Golden Ages of Classical Antiquity, civilization underwent a cultural rebirth. The Italian Renaissance, spanning the 15th and 16th centuries, represents another great age of humanity. Toward the end of the 15th century, Leonardo, Michelangelo, and Raphael ushered in the High Renaissance, producing the defining works of art… as great or greater than those of Classical Antiquity. Similarly, the Scientific Revolution, spanning the 17th century, and shaped by Galileo, Kepler, and Descartes, took place, reaching a crescendo with Newton. This revolution effectively made humankind modern by demonstrating that nature could be understood through mathematical principles. Finally, the first three decades of the 20th century saw a Renaissance of Science, with the awe-inspiring contributions of Einstein, and a three-year period in the late 1920s as its High Renaissance, the emergence of the quantum revolution.
It is during cultural revolutions that individual and group genius arises. The present lecture springs from the last chapter of the speaker’s newest book, Beyond Genius, which examines the internal and external conditions for surpassing genius to appear and change the world. Recreating these conditions will never make us another Leonardo or an Einstein, but it cannot fail to make us more creative and productive than we would be otherwise.
The groundwork for the digital age was laid by mathematicians John von Neumann and Alan Turing in the late 1930s and 40s, with individual and networked computers proliferating during the rest of the 20th century. But only in the 2020s has Artificial Intelligence (AI), foreseen by Turin, suddenly begun to flower. Is this the High Renaissance of the Digital Age? And if so, who will emerge as its most significant innovators?
Scientist, artist, and author Bulent Atalay has been described by NPR, PBS, the Washington Post, and National Geographic as a “Modern Renaissance Man.” His academic background is in theoretical physics, distilled from work at Georgetown, the University of California-Berkeley, Princeton, Oxford, and the Institute for Advanced Study Princeton.
He is the author of three books on the intersection of art, science, and mathematics. In his best-selling books, Math and the Mona Lisa (Smithsonian Books, 2004) and Leonardo’s Universe (National Geographic Books, 2009), the focal point was Leonardo da Vinci. His latest and most ambitious book, Beyond Genius (Pegasus Books), is a compendium of genius in general, with a special focus on the handful of transformative geniuses, Leonardo, Shakespeare, Newton, Beethoven, and Einstein. Copies of the Beyond Genius will be available for purchase and signing following the talk. Visit his website www.bulentatalay.com
Dr. Atalay travels around the world lecturing at academic institutions and on cruise ships on the "A-subjects," art, archaeology, astrophysics, atomic physics, and Ataturk, confessing he knows much less about the "B-subjects," business, banking, biology, and botany... He has lectured at Caltech, Princeton, Yale, Harvard, Stanford, Oxford, NASA, NIST, and NIH… He is a professor emeritus of physics at the University of Mary Washington and the University of Virginia.
Prof. Antonio Carcaterra, Sapienza University of Rome, Italy
A point of view is proposed regarding the responsibility linked to the actions of apparently autonomous machines, i.e., not continuously controlled and supervised during their actions by humans. These devices, many of which classified within the disciplinary area of mechatronics and robotics with the involvement of complex algorithms, some of which pertain to the sphere of popular artificial intelligence, will have a very important social impact in the near future. It is clear how the immersion of these devices in a complex social context, even in physical contact with men engaged in cooperative activities with machines, can determine a large series of circumstances in which this interaction, for various and non-obvious reasons, produces potentially harmful effects, alongside the obvious advantages that promote their use. One suggestive example among all, familiar even to the non-specialist reader, is that of autonomous driving, to which for clarity, where useful, we will refer our examples (the considerations set out being however applicable to every decision-making problem, regardless of the case in question). In this presentation we will focus our attention on the process of determining choices through those foundations of decision theory which is the common ground on which most of the decision-making techniques known today operate (including various artificial intelligence techniques), used both in machines and by man. We will give particular emphasis to the minimum principles, as a central and universal element of rational choices, principles that govern machines, men and natural phenomena themselves. It is from the connection between the need for a compromise, which implies the adoption of minimum principles, and the incomplete controllability of the scenarios that rational choices produce, promoting a new point of view on the concept of responsibility.
Prof. Antonio Carcaterra — is the Head of the Department of Mechanical and Aerospace Engineering at Sapienza University of Rome. His research interests include dynamics of structures and complex systems, mechatronics, control of dynamic systems and vehicles, autonomous drones using artificial intelligence. He is the holder of several patents, and is currently managing numerous fundamental and industrial research projects. He has authored about 200 scientific publications. He is currently a senior fellow of the Sapienza School of Advanced Studies, president of the Sapienza Innovation Consortium, scientific manager of National Center for Sustainable Mobility – part of the European program entitled “Next Generation”, key scientific manager of the investment fund I3P on space technology funded by the European Fund of Investment. He was an adjunct professor at the Department of Mechanical Engineering at Carnegie Mellon University, a research scientist at the Marine Engineering Institute of the Italian National Council of Research, past director of the doctorate program in Theoretical and Applied Mechanics, director of the Doctoral School in Sciences and Technologies for Industrial Innovation, president of the degree program in Mechanical Engineering, and a member of the Transport Systems Safety Commission for the Italian Ministry of Transport and Infrastructure.
Dr Gabriela Dutrénit, Universidad Autónoma Metropolitana, Mexico
There is already extensive literature on processes of technological capability accumulation (TCA) at the company level. These capabilities differ between companies and are at the basis of their innovative activity and competitiveness.
The literature on developing countries and emerging economies shows that companies tend to adopt adaptive TCA strategies, rather than strategies aimed at leading processes to move the technological frontier based on R&D activities. The productive structure of these countries is heterogeneous; there are companies that compete near the frontier of knowledge and technology, while others are far behind. Many of the most modern companies are multilatinas, that is, multinationals that have their headquarters in Latin America, for example in Mexico. These multilatinas are large companies (more than 10,000 employees), with many production plants (generally more than 10 plants) located in different countries and continents. Many of these companies are connected to global value chains,
The objective of this presentation is to analyze both internal factors and the incidence of the economic, environmental, cultural, sociopolitical and scientific and technological spheres, including science, technology and innovation policies, in the TCA processes of Mexican multilatinas. The processes of digitalization and introduction of industry 4.0 are particularly explored, differentiating the drivers of multilatinas that are or are not connected to global value chains. Based on a multiple case study methodology, a set of multilatinas in the auto parts and cable production industries for the generation, transmission and distribution of electrical energy are compared.
Dr. Gabriela Dutrénit — is an economist with a PhD in Science and Technology Research Studies from the Science Policy Research Unit (SPRU), University of Sussex, UK. She is a professor in the postgraduate Program in Economics, Management and Policy of Innovation at the Universidad Autónoma Metropolitana (UAM), Mexico. also a “Distinguished Professor” of the UAM and a regular member of the Mexican Academy of Science. Dr. Dutrenit is president of the Latin American Chapter of LALICS (Latin American Network for Economics of Learning, Innovation, and Competence Building Systems). Her research interests include innovation and development; learning and technological capability accumulation at the firm level; university–industry linkages; research and development (R&D), and innovation policy. She has coordinated several evaluations of the Mexican STI policy.
Dr. Rainer P. Hasenauer, Honorary Professor, WU Vienna, Austria
This keynote presentation is based on the book “Intrapreneurship Management: Concepts, Methods, and Software for Managing Technological Innovation in Organizations“ by Rainer Hasenauer and Oliver Yu, to be published by Wiley - IEEE Press in June 2024. Internal innovation, or Intrapreneurship, with employees thinking and behaving like entrepreneurs, is the driving force for organizational competitiveness and economic growth. Successful intrapreneurship management requires Organization Readiness for internal innovations and Market and Technology Readiness for innovation projects. Artificial Intelligence (AI) can be particularly effective in supporting the fulfillment of all these readiness. Specifically, AI, especially the emerging Emotion AI and Creativity AI, can develop and apply an extensive knowledgebase of emotional assessments and successful creativity experiences for the in-depth understanding and precise fulfillment of the needs and wants of not only intrapreneurs and internal supporters in building powerful innovative culture and teamwork, but also those of prospective external adopters for the effective development and marketing of innovative technologies.
Additionally, rapidly evolving AI can apply a range of advanced methodologies to provide the optimization of available resources for achieving the combined balance among the integrated Economic-Ecologic-Equity values and risks of all Intrapreneurship participants: Intrapreneur, Internal Supporter, and Final Adopter.
This presentation will provide detailed representative examples for these exciting current and prospective AI applications to enhance Intrapreneurship Management.
Dr. Rainer P. Hasenauer — is Honorary Professor of Marketing and Lecturer in Marketing of High-Tech Innovation and Technology Marketing at the Institute for Marketing Management, Vienna University of Economics and Business (WU Vienna).
He is an entrepreneur who has long been involved with high-tech companies as a co-founder and a business angel. He is also a business developer focusing on innovative technologies. He initiated and co-founded the HiTec Marketing Research Association in Vienna (www.hitec.at) and the Cross Border HiTec Center (www.hitechcentrum.eu), where he serves as senior advisor.
Dr. Hasenauer’s primary teaching and research interests lie in market entry of high-tech innovation in B2B markets, and in measuring innovation half-life and technology acceptance in B2B markets. He teaches at the Vienna University of Economics and Business, the Vienna University of Technology, and the Campus02 University of Applied Sciences in Graz. He has held guest lectureships at the Institute for Advanced Studies in Vienna, the University of St. Gallen (CH), the University of Klagenfurt, and the University of Economics in Bratislava. His research work is predominantly project-driven for B2B markets and comprises community-based innovation, marketing testbeds for market entry, and multidisciplinary communication in high-tech innovation. Applications include satellite navigation and remote sensing, robotics, sensors, functional materials, flow batteries and remote power supply, threat analysis for safety and security systems of complex products and systems such as road tunnel ventilation and power plants, and applied operations research with focus on multi-criteria decision models.
He has served on the advisory boards of high-tech investment groups, on an expert board for high-tech start-up incubators, and on the supervisory board of a global market leader in the field of safety-critical real-time communication systems.
Dr. Mel Horwitch, Visiting Scholar/Research Affiliate, MIT Sloan School of Management, USA
This keynote speech focuses on the current state and direction of the AI-business strategy relationship. Relevant background of business strategy is examined, with particular attention given to the inherent variety and complexity of business strategy. In particular, emphasis is placed on an ongoing need to integrate disparate explicit and tacit elements and as well as on other tensions existing in strategy—all of which are challenges for AI. The development of a set of concepts dealing with AI and strategy is discussed, observing that up to now AI largely serves as a strategic tool assisting and augmenting existing processes in a generally focused or specialized fashion. Lessons from knowledge management’s [KM’s] AI experience are noted, especially claims that AI-enabled KM systems represent some success in serving both explicit and tacit knowledge. The current leveraging of AI by some of the best known strategy consulting firms is reviewed. Energetic progress in incorporating AI into such consulting practices is observed. This talk concludes by speculating that a later unanticipated phase of the AI-strategy relationship could feature AI-enabled strategy systems that enhance the more general management, instinctual, and tacit parts of strategy.
Dr. Mel Horwitch — is currently Visiting Scholar/Research Affiliate at the MIT Sloan School. Previously, at Central European University in Budapest, he was University Professor, CEUBS Dean, and CEUBS Innovation and Entrepreneurship Project Director. He also served for fourteen years at NYU-Poly (now NYU’s Tandon School of Engineering) as Professor of Technology Management, twice as Department Chair, and as Institute for Technology and Enterprise Director. He publishes and teaches extensively on technology strategy, entrepreneurship, and large-scale innovation, most recently focusing on how innovation and other building blocks help in developing a more strategic society. His publications include Clipped Wings: The American SST Conflict, Technology in the Modern Corporation: A Strategic Perspective (editor and contributor), Energy Future (chapter contributor), and articles in such journals as Management Science, Policy Science, MIT Sloan Management Review, Technology in Society, Journal of Engineering and Technology Management, and Journal of High Technology Management Research, and numerous cases. He was also Dean of Management and Professor at Theseus Institute (now EDHEC) in Sophia Antipolis, France. He also served on the MIT Sloan School and Harvard Business School [HBS] faculties and was Visiting Scholar at UCSD’s Rady School, Hitotsubashi University in Tokyo, and London Business School. In 2021, PICMET selected Dr. Horwitch as a PICMET Fellow for his impact on technology management research, education, and leadership. He earned an AB from Princeton University and an MBA and Doctorate from HBS and served a US Peace Corps Volunteer in Thailand.
Dr. Jay Lee, University of Maryland College Park, USA
Handout
Industrial AI, Big Data Analytics, Machine Learning, and Cyber Physical Systems are changing the way we design product, manufacturing, and service systems. It is clear that as more sensors and smart analytics software are integrated in the complex industrial products, predictive technologies can further learn and autonomously optimize performance with resilience. This presentation will give an introduction about recent advances of Industrial AI for highly complex engineering systems. First, Industrial AI systematic approach will be introduced. Case studies on lessons learned from fleet-based asset system including semiconductor, wind farm, electrification, high speed transport, and healthcare/medical systems, etc. will be given. In addition, training industrial AI skills through data foundry for high performance and real-time data analytics in future talents will be discussed.
Dr. Jay Lee — is Clark Distinguished Professor in the Mechanical Engineering of the Univ. of Maryland College Park. He is also the founding director of Industrial AI Center (www.iaicenter.com ). Previously, he served as Ohio Eminent Scholar, L.W. Scott Alter Chair, and Univ. Distinguished Professor at Univ. of Cincinnati, and was the founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (www.imscenter.net) in partnership with over 100 global company members.
Dr. Lee was on leave from UC to serve as Vice Chairman and Board Member for Foxconn Technology Group during 2019-2021 to lead the development of Foxconn Wisconsin Science Park in Mt. Pleasant, WI (www.foxconnwiofficial.com). In addition, he advised Foxconn business units to successfully receive six WEF Lighthouse Factory Awards since 2019. He is a member of Global Future Council on Advanced Manufacturing and Production of the World Economics Council (WEF), a member of Board of Governors of the Manufacturing Executive Leadership Council of National Association of Manufacturers (NAM), Board of Trustees of MTConnect, as well as a senior advisor to McKinsey. Previously, he served as Director for Product Development and Manufacturing at United Technologies Research Center (now Raytheon Technologies Research Center) as well as Program Director for a number of programs at NSF.
He was selected as 30 Visionaries in Smart Manufacturing by SME in Jan. 2016 and 20 most influential professors in Smart Manufacturing in June 2020, and received SME Eli Whitney Productivity Award and SME/NAMRC S.M. Wu Research Implementation Award in 2022. His book on Industrial AI was published by Springer in 2020.
Mohan Nair, CEO Emerge Inc., USA, and Edmund Hillary Fellow, New Zealand
When markets transform, companies caught unprepared are left behind. But those who recognize that change is in the air, who are prepared for market shifts, not only prevail but soar to new competitive heights. Transformation is usually equated with digital transformation for businesses which want to accelerate their capabilities especially in the world of AI. But the real change is one that is prepared years before such structural changes and is a set of capabilities that must be created, energized or even purchased to create the eyesight and insight to see in the fog of future. AirBnB, Disney, Uber were not created from disruption. They were created from transformative mindset employed during a structural change in the market resulting in a new recipe unseen by the majority. Now they are the majority.
This presentation will focus on the “7 Deadly sins of business transformation” and cover the virtues of businesses which used technology whilecreatinga business model recipe to enact their vision. Focusing on the “seven deadly sins” that businesses must avoid in order to survive and thrive during market fluctuations, this presentation will offer a way to view transformation and guide through anticipating, understanding and riding the waves. It will
Examine the new principles of transformation in business.
Explain the value of purpose/cause in organizations.
Explore market momentum and how to identify them.
Focus on value propositions and “values propositions”.
Elaborate on the role of a performance platform in the achievement of an organization.
Mohan Nair — is an author, a corporate executive and a proven innovator with 6 startup creations within large enterprise. He is Chief Executive of Emerge Inc., where his focus is on innovation and innovator.
Mr. Nair served as Chief Innovation Officer, Senior VP of Cambia Health Solutions for 10 years. Leading a high-performance innovation team called Innovation Force, he launched six startups. Healthsparq, his first startup, was sold to Kyruus in Jan 2021. Prior to Cambia, Mohan was Chief Marketing Executive/EVP for Regence Blue Cross Blue Shield. He formed the consumer-directed digital front-end, telesales, business development and was instrumental in leading the transformation of Regence Group.
He was President of 2 startups, one in cost/performance management and the other in network management. Earlier in his career, he held executive positions in Mentor Graphics Corporation and Intel.
Mr. Nair is the author of the book “Strategic Business Transformation: the 7 Deadly Sins to Avoid” (Wiley & Sons, 2010). He has authored 2 other books and over 20 refereed journal articles on Performance Measurement, Balance Scorecard and Servant Leadership. He writes for Forbes, RamaonHealthcare.com, and is a contributing columnist for Innovation Leader.
Mr. Nair was Adjunct Professor of Business at JL Kellogg School of Management for 12 years, and University of Oregon MBA program for 5 years. He has also been a guest lectured at Harvard, Berkeley, University of Auckland (New Zealand), and Advisor to the President at George Fox University. He is a TEDx speaker where he outlines his unique business transformation philosophy.
Dr. Marie Elisabeth Paté-Cornell, Stanford University, USA
Artificial intelligence plays two different roles in risk analysis: managing information, or implementing (suggesting) automatic decisions. Risk management decisions involve preferences including a risk attitude that is implemented in the software, but may not be in line with that of the decision maker, or the preferences of the people who are targets of the decision. This can be the case in engineering, but also in medicine or national security. The treatment of risk attitudes is seldom pointed out, yet the decision maker may not be aware of preferences that have been included in the software. My recommendations include the description of AI systems in ways that reveal the preferences and risk attitudes included in the programs, and how to modify them to fit other preferences in their applications.
Dr. Marie Elisabeth Paté-Cornell — is the Burt and Deedee McMurtry Professor in the School of Engineering and a Senior Fellow (by courtesy) of the Stanford Freeman-Spogli Institute for International Studies. Her specialty is engineering risk analysis, with applications to complex systems (space, medical, offshore oil platforms, cyber security, etc.). Her work has been based on probabilistic and stochastic models and on Artificial Intelligence. She is a member of the National Academy of Engineering, the French Académie des Technologies, the NASA Advisory Council, and a Distinguished Visiting Scientist of the Jet Propulsion Lab. She was a member of the President’s Foreign Intelligence Advisory Board (2001 to 2008). She holds a BS in Mathematics and Physics, Marseille (France), an Engineering degree (Applied Math/CS) from the Institut Polytechnique de Grenoble (France), an MS in Operations Research (OR) and a PhD in Engineering-Economic Systems (EES), both from Stanford University. She is the author or coauthor of numerous publications including several Best Paper awards. She was awarded the 2002 Distinguished Achievement Award of the Society for Risk Analysis (of which she is a Fellow), the INFORMS Ramsey Medal of Decision Analysis (2010), an Honorary PhD from the University of Strathclyde (2016), and the IEEE Ramo medal for Systems Engineering and Science in 2021.
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