Interdisciplinary Curriculum: Intelligence Augmentation, or Studying the Evolution of Cognitive Systems for Service Systems

Outline for Interdisciplinary Curriculum
(Pre-Syllabus Intelligence Augmentation, or Studying the Evolution of Cognitive Systems for Service Systems)

Question: What changes in service systems will progress everyone towards easily building, understanding, and working with cognitive systems in their personal and professional lives?  These changes can have a profound impact on intelligence augmentation of people and organizations.

To address the science, design, business and societal implications of cognitive era of computing requires an interdisciplinary curriculum that synergistically complements the core IBM Cognitive Computing Curriculum (which draws most heavily from artificial intelligence syllabi).   A core curriculum provides an understanding of the building blocks of cognitive systems, and how each block functions algorithmically.  However, to be of value these building blocks must be assembled into well-designed solutions that augment intelligence of service system entities.   Specifically, the solutions should augment the performance of people and organizations on real-world tasks and social interactions, and thereby play a positive role in the evolution and development of business and society.

Science: What can be learned by studying the evolution and development of intelligence?

Design: Why is it so hard to build cognitive systems to augment the intelligence of people?

Business:  What should executives and managers know about this rapidly advancing technology?

Societal Implications:  What should everyone (citizens of the 21st century) know about the practical, political, and philosophical implications?

The proposed core IBM Cognitive Computing Curriculum draws most heavily from traditional Artificial Intelligence (AI) courses that focus on intelligence in machines, including core AI machine learning, reasoning, perception, interaction, and knowledge representation courses.  However, more is needed to address to address the needs of learners who do not have advanced programming and math skills, for example:

Science: An interdisciplinary science curriculum must, to an appropriate degree, also address the evolution and development intelligence in brains (biology) and organizations (systems science, especially socio-technical systems, or smart service systems).   Learning, reasoning, perception, interaction, and knowledge are important to understand in the context of brains and organizations, as well as machines.   This provides a broader view on the implementation, development, and measurement intelligent system capabilities, including performance on a range of real-world tasks.

Design:  Intelligence augmentation of people requires design of that experience.  An understanding is needed especially of the role of data (from both machines and experiences of people) to properly design digital cognitive systems, from a human-computer interaction as well as a computer-supported collaborative work and performance support systems perspectives.  These considerations span a wide range of contexts from interaction with devices and environments with local machine intelligence, through systems engineering and human factors of collaboration in teams of augmented individuals in diverse contexts, to design of work in global organizations with support from crowd-sourced and machine intelligence in the cloud.

Business: The historical business case studies of the applications of AI in business, successes and failures, as well as the challenges and opportunities of doing startups or transforming existing large enterprises in the cognitive era should also be part of an interdisciplinary curriculum.  This will include history, state-of-the-art, and projected future of business considerations, including competitive analysis of capabilities.   The economics of AI at multiple levels of business and society are the focus of portion of the interdisciplinary curriculum.

Societal Implications:  An interdisciplinary curriculum must also include a range of topics, both practical, economic, political, and philosophical in nature.  As people adopt intelligent assistants into their lives on smartphones, in cars, in the home, and at work, there are a range of practical matters associated with AI in our day-to-day lives. From a societal implications perspective, if the goals of AI are successful, what it means to be a programmer and a mathematician are likely to dramatically change in the next ten years, and this will impact the AI curriculum outlined above.



The Brain: Structure, Function, and Evolution

The Symbolic Species: The Co-Evolution of Language and The Brain

The Social Brain: The Neuropsychology of Social Behaviors

Social Evolution

Organization Theory

Organization Analysis


Service Science: Study of the the co-evolution of technology and rules systems

The Construction of Social Reality

The Master Algorithm

Design (and Data Issues)


Cognitive Systems Design

Intro to Data Science & Data Ownership Issues–ud359

Case Studies
Design of Products for Google’s “AI-first-world”
Data Privacy and Design: Episodic Memory Design in AI Systems

Human-Computer Interaction

Computer Supported Collaborative/Cooperative Work

Electronic Performance Support/Pervasive Interaction Design (PIxD)

Mohr: Socio-Technical Systems Design

Kline: Multidisciplinary Thinking, SysReps and the Socio-Technical Systems Design Loop

Smart Mobs

Things that make us smart

Augmenting Human Intellect



Artificial Intelligence Industry – An Overview by Segment

Case Studies from Recent Press


Societal Implications
AI: Philosophy, Ethics, Impact

Preparing for the Future of AI




In sum, a proposed core IBM Cognitive Computing Curriculum that draws most heavily from traditional AI courses provides an excellent starting point for learners with strong programming and mathematics skills.    The proposed interdisciplinary components provide a pathway for learners who may or may not have strong programming and mathematics skills, as well as a broadening for those learners who do go deep in the core AI areas.  Learners who master both the core and the interdisciplinary curriculum will be better T-shaped professionals, a type of future-ready talent that is highly sought after in business and government, and especially at IBM.

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