AI Science and Engineering: A new scientific discipline? Lecture

Abstract

Is “AI Science and Engineering” an un upcoming scientific discipline that can fuse AI, brain and mind studies and social engineering? As Artificial Intelligence (AI) studies and research flourish worldwide, it is worth debating whether we already observe the birth of a new discipline in the exact sciences that goes beyond the classical specializations of Computer Science (CS) and Electrical/Computer Engineering (ECE). It is natural that AI and its various domains, notably Machine Learning, will share methods and curriculum with both CS and ECE. However, this is not really enough to move from our current Data/Information Society to a Knowledge Society. We certainly need a fusion of AI with brain and mind studies, notably Neuroscience, Cognitive Science and Psychology. Furthermore, AI has a huge impact on both our society and environment. We already observe strong AI applications in social engineering domains, e.g., on recommendation systems, on-line marketing, social media,  meta-societies and fake news. It is natural that such applications will influence the AI Science and Engineering discipline itself. Furthermore, as humans consist of matter and live in a spatiotemporally evolving environment, life and environment studies, e.g., on matter complexity, can have a fundamental impact on both the understanding of life and human intelligence and on the development of AI. However, as students cannot be polymaths, are all the above issues too many to fit in one scientific discipline? Is there a danger that we sacrifice scientific depth to interdisciplinarity? Can we envisage other sister-disciplines, e.g., Mind and Social Science and Engineering and/or Bioscience and Engineering that can address students with interests and background in Liberal Arts and Sciences or Biology/Medicine/Health Sciences?     

No matter their exact form, AI Science and Engineering and its sister disciplines have many great challenges to address. Here is a partial list:

• Is AI Science and Technology a scientific discipline in its own right?
• How can we quantify knowledge?
• Can Virtual Reality truly empower meta-societies or is it just a hype? 
• Can AI-powered human-centred computing surpass human intelligence?
• Can we create self-conscious machines?
• Can Mind and Social Engineering manipulate human behaviour and social functions?
• How do social media facilitate disinformation?
• What are the envisaged effects of AI and IT on our personal relations and sexual life?
• How can we not only protect but also monetize our personal data?
• Can AI help devising new political systems?
• How are irrationalism, anti-elitism, and social media disinformation related?
• Can new technologies ignite social revolutions?
• Is life and intelligence due to matter complexity?
• Can we patch parts of our brain?
• Is climate controllable through Geoengineering?
• Can humanity progress without resorting to energy-intensive technologies?

As each of them needs an entire book to be properly addressed, this lecture will simply introduce few of these challenges for further discussion and debate.

All the above issues are addressed in the new 1050+ page book “Artificial Intelligence Science and Society” consisting of four volumes (parts) debating all technical and social grand challenges of AI Science and Engineering in an understandable and scientifically accurate manner.

Bibliography

1. I. Pitas, “Artificial Intelligence Science and Society Part A: Introduction to AI Science and Information Technology”

2. I. Pitas, “Artificial Intelligence Science and Society Part B: AI Science, Mind and Humans”
3. I. Pitas, “Artificial Intelligence Science and Society Part C: AI Science and Society”
4. I. Pitas, “Artificial Intelligence Science and Society Part D: AI Science and the Environment” 

Figure: Data volume increase in past decade.

Figure: The knowledge pyramid.

Presentation

AISE discipline v2.0

In order to keep up with the reality described above, many universities are offering under- and post- graduate programs in AI. These programs may cover a wide range of topics, including machine learning, computer vision and robotics. Some programs may be focused on the technical aspects of AI, while others may take a more interdisciplinary approach, examining the social, ethical, and economic implications of AI. It is possible that AI could become a separate degree program at some universities in the future, but for now it is typically studied as part of a computer science or related degree program.The lists offered below consist of a sumation of the above mentioned programs as well as links to AI-centered curricula.