Artificial Intelligence fundamentals: a managerial perspective: Explore more

These resources are part of the course materials and they can be included in Weekly quiz and Final quiz assessment.

VIDEO

The Last 6 Decades of AI — and What Comes Next | Ray Kurzweil

How AI Could Empower Any Business | Andrew Ng https://youtu.be/reUZRyXxUs4?si=HfaJdUdJ244GSQMC

BOOK chapters

Russell – Norvig Artificial Intelligence a Modern Approach (entire chapter 1).

Articles & Papers

Keep Your AI Projects on Track. by Iavor Bojinov - Harvard Business Review. From the magazine (November–December 2023)

AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines…

Leo Breiman. "Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)." Statistical Science, 16(3) 199-231 August 2001. https://doi.org/10.1214/ss/1009213726 ;

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.

Christen, P., & Schnell, R. (2024). When Data Science Goes Wrong: How Misconceptions About Data Capture and Processing Causes Wrong Conclusions. Harvard Data Science Review, 6(1). https://doi.org/10.1162/99608f92.34f8e75b ;

In an era of large, complex data, it is important for data scientists to understand how data being analyzed have been acquired, processed and linked. In this Diving Into Data column piece, Professors Christen and Schnell explore and categorize key aspects of data provenance, highlighting issues that can arise and providing recommendations to help readers identify problems and avoid resulting errors

van Giffen, Benjamin; Barth, Nadine; and Sagodi, André, "Characteristics of Contemporary Arti8cial Intelligence Technologies and Implications for IS Research" (2022). ICIS 2022 Proceedings. 13.

Artificial Intelligence (AI) is often presented as a new phenomenon that is primarily driven by advances in contemporary machine learning technologies. Despite the steep rise, conceptualizations of contemporary AI technologies tend to be vague in many studies. This is problematic not only for positioning and focusing such research, but also for theorizing on the pervasive AI phenomenon. This paper presents a systematic literature review to understand and synthesize distinctive characteristics of contemporary AI technologies. In the course of our ongoing research, the preliminary findings encompass the changing role of data, feature extraction, adaptivity, transparency, and biases. With our future research, we seek to provide guidance on the conceptualizations of AI in IS research and to facilitate a more nuanced and focused theorization of AI in future IS studies.

Jakubik, J., Vössing, M., Kühl, N. et al. Data-Centric Artificial Intelligence. Bus Inf Syst Eng 66, 507–515 (2024). https://doi.org/10.1007/s12599-024-00857-8 ;

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.

Feuerriegel, S., Hartmann, J., Janiesch, C. et al. Generative AI. Bus Inf Syst Eng 66, 111–126 (2024). https://doi.org/10.1007/s12599-023-00834-7 ;

Tom Freston is credited with saying "Innovation is taking two things that exist and putting them together in a new way". For a long time in history, it has been the prevailing assumption that artistic, creative tasks such as writing poems, creating software, designing fashion, and composing songs could only be performed by humans. This assumption has changed drastically with recent advances in artificial intelligence (AI) that can generate new content in ways that cannot be distinguished anymore from human craftsmanship.