In the last 25 years, modern enterprises have become centered on digital systems. IT applications, knit together by enterprise architectures, now pervade all business functions. But there is still doubt and confusion as to how a firm's digital infrastructure should be managed and maintained. On the one hand, modern enterprises must be flexible, capable of generating a stream of new products and providing customers with numerous options. On the other hand, they must be efficient providing timely solutions at low cost.
These contrasting requirements are exemplified by two opposing technical paradigms: flow processes and platform systems. In the talk, I will characterize flow processes and platform systems in terms of their innate properties and organizational implications. I will also indicate when and why each patterns is more valuable. In modern establishments, the two patterns are not mutually exclusive: technologically sophisticated organizations must use both. At the end of the talk, I will speculate on where each pattern is likely to be needed and invite comments on the tensions they are likely to cause within organizations.
Carliss Y. Baldwin is the William L. White Professor of Business Administration, Emerita, at the Harvard Business School. She studies the process of design and its impact of design architecture on firm strategy, platforms, and business ecosystems. With Kim Clark, she authored Design Rules, Volume 1: The Power of Modularity. Her work has been published in a variety of leading journals including Strategic Management Journal, Organization Science, Management Science, Research Policy and Harvard Business Review. She has won numerous awards for research: most recently, she received a Doctor honoris causa from the Technical University Munich in 2014, and in 2015 was named the Distinguished Scholar of the Technology and Innovation Management (TIM) division of the Academy of Management. She is currently working on Design Rules, Volume 2: How Technology Shapes Organizations.
There is an increasing demand for embedding intelligence in software systems as part of its core set of features both in the front-end (e.g. conversational user interfaces) and back-end (e.g. prediction services). This combination is usually referred to as AI-enhanced software or, simply, smart software.
The development of smart software poses new engineering challenges, as now we need to deal with the engineering of the “traditional” components, the engineering of the “AI” ones but also of the interaction between both types that need to co-exist and collaborate.
In this talk we'll see how modeling can help tame the complexity of engineering smart software by enabling software engineers specify and generate smart software systems starting from higher-level and platform-independent modeling primitives.
But, unavoidably, these models will be more diverse and complex than our usual ones. Don't despair, we'll also see how some of these same AI techniques that are making our modeling life challenging can be turned into allies and be transformed into modeling assistants to tackle the engineering of smart software with a new breed of smart modeling tools.
I'm an ICREA Research Professor at Internet Interdisciplinary Institute, the Research center of the Open University of Catalonia (UOC) where I'm leading the SOM Research Lab. I'm also Visiting Professor at the Western Norway University of Applied Sciences. Previously, I've been at École des Mines de Nantes, Inria, University of Toronto, Politecnico di Milano and the Technical University of Catalonia.
My research falls into the broad area of systems and software engineering, especially promoting the rigorous use of software models in all software tasks while keeping an eye on the most unpredictable element in any project: the people involved in it. Current research topics includepragmatic formal verification techniques, analysis of open source communities, open data exploitation and the role AIcan play in software development (and vice versa). Let's use all the tools at our disposal to build Better Software Faster.
Beyond scientific publications, the results of our research are available as open-source tools or as part of transfer contracts. As an example, Xatkit is a spin-off of the team.
Automation-supported compliance checking has become necessary in increasingly automated socio-technical contexts. AI & law research, since the 70's has addressed ways to model ethical and legal knowledge, and has developed approaches that are relevant to compliance-checking.
I will shortly review approaches to the modeling of legal content: Rules- and logic-based models; Argumentation-based models; Case-based reasoning models.
I will the address some recent approaches aimed at providing logical models in a way which is understandable to non-technical people, and consider whether this idea may support developments in automated compliance checking. I will also consider the significance of argumentation-based models and ontologies to provide rationales for compliance assessments.
I will argue for the construction of human-understandable models of law and ethics, to be used for the purpose of compliance checking, also over the functioning of machine-learning based systems. How to integrate logical modeling and machine learning, in eliciting and applying normative knowledge is a challenging task for the future.
Giovanni Sartor is professor in Legal Informatics at the University of Bologna, professor in Legal informatics and Legal Theory at the European University Institute of Florence, visiting professor of Artificial Intelligence and Law at the University of Surrey. He coordinates the CIRSFID-AI for Law and Governance unit at the Alma-AI research center of the University of Bologna. He holds the ERC-advanced grant (2018) for the project Compulaw (2019 - 2024).
He obtained a PhD at the European University Institute (Florence), was a researcher at the Italian National Council of Research (ITTIG, Florence), held the chair in Jurisprudence at Queen's University of Belfast, and was Marie-Curie professor at the European University of Florence. He has been President of the International Association for Artificial Intelligence and Law.
He has published widely in legal philosophy, computational logic, and computer law, AI & law. He is co-director of the Artificial Intelligence and Law Journal and co-editor of the Ratio Juris Journal. His research interests include legal theory, early modern legal philosophy, logic, argumentation theory, modal and deontic logics, logic programming, multiagent systems, computer and Internet law, data protection, e-commerce, law and technology.
Trustworthiness, the combination of security, privacy, resilience, reliability and safety, is especially critical in industrial systems. Life, limb and the environment are at risk.
Unfortunately, these trustworthiness characteristics often conflict. Security would suggest locking that door, but safety demands it be able to be opened in case of emergency. This is resolvable, but often further factors must then be considered. (A bad actor could open the door from the inside. Now what?)
The Industry IoT Consortium has published the Trustworthiness Foundation, which outlines eleven principles to help guide you through the maze. This presentation will show how these principles can help you build a trustworthy system.
Stephen Mellor is the Chief Technical Officer for the Industry IoT Consortium, where he aligns groups for business, technology, trustworthiness and industry for the Industrial Internet.
He is a well-known technology consultant on methods for the construction of real-time and embedded systems, a signatory to the Agile Manifesto, and one-time adjunct professor at the Australian National University in Canberra, ACT, Australia. Stephen is the author of Structured Development for Real-Time Systems, Object Lifecycles, Executable UML, MDA Distilled and Models to Code.
Stephen was Chief Scientist of the Embedded Software Division at Mentor Graphics, and founder and past president of Project Technology, Inc., before its acquisition. He participated in multiple UML and modeling-related activities at the Object Management Group (OMG), and was a member of the OMG Architecture Board, which is the final technical gateway for all OMG standards. Stephen was the Chairman of the Advisory Board to IEEE Software for ten years and a two-time Guest Editor of the magazine.
The term digital twin is being used with increasing frequency, but with little consistency, across multiple industries today. Digital Twin Consortium is working to address this and help industries better understand the advantages and value over the continum of digital twins from discrete to complex.
Learn about the challenges the Digital Twin Consortium membership is working to address and its priorities. Gain a clear understanding through real-world use cases how businesses are recognizing value today through use of digital twins an enabling technologies.
Dan Isaacs is Chief Technology Officer of Digital Twin Consortium, where he is responsible for setting the technical direction for the Member Consortium, liaison partnerships and business development support for new memberships.
Previously, Dan was Director of Strategic Marketing and Business Development at Xilinx where he was responsible for emerging technologies including AI/Machine Learning, including defining and executing the ecosystem strategy for the Industrial IoT. Prior to joining the Digital Twin Consortium, Dan was responsible for Automotive Business Development focused on Automated Driving and ADAS systems.
Dan represented Xilinx to the Industrial Internet Consortium (IIC). He has more than 25 years of experience working in automotive, Mil/Aerospace and consumer-based companies including Ford, NEC, LSI Logic and Hughes Aircraft.
An accomplished speaker, Dan has delivered keynotes, presentations and served as panellist and moderator for IIC World Forums, Industrial IOT Global conferences, Embedded World, Embedded Systems, and FPGA Conferences. He is a member of international advisory boards and holds degrees in Computer Engineering: EE from Cal State University, B.S. Geophysics from ASU.