[visionlist] Live free e-Lecture by Prof. Ioannis Pitas "AI Science and Engineering, a new scientific discipline? Societal and environmental impact", Tuesday, 15th November 2022, 17:00-18:00 CET

[Ioanna Koroni]

Dear AI enthusiast/scientist/engineer/student,

 

Prof. Ioannis Pitas, a prominent AI researcher internationally, will deliver
the e-lecture: 

"AI Science and Engineering, a new scientific discipline? Societal and
environmental impact"

on Tuesday 15th November  2022 17:00-18:00 CEST (8:00-9:00 am PST), (12:00
am-1:00am CST).

 

You can join for free without registration using the zoom link:
<https://authgr.zoom.us/j/94255772113> https://authgr.zoom.us/j/94255772113
Passcode: 867064

Attendance is free.

 

Lecture:  "AI Science and Engineering, a new scientific discipline? Societal
and environmental impact"

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:

1.	"Artificial Intelligence Science and Society Part A: Introduction to
AI Science and Information Technology"

 <https://www.amazon.com/dp/9609156460?ref_=pe_3052080_397514860>
https://www.amazon.com/dp/9609156460?ref_=pe_3052080_397514860

2.	"Artificial Intelligence Science and Society Part B: AI Science,
Mind and Humans"
<https://www.amazon.com/dp/9609156479?ref_=pe_3052080_397514860>
https://www.amazon.com/dp/9609156479?ref_=pe_3052080_397514860
3.	"Artificial Intelligence Science and Society Part C: AI Science and
Society"  <https://www.amazon.com/dp/9609156487?ref_=pe_3052080_397514860>
https://www.amazon.com/dp/9609156487?ref_=pe_3052080_397514860
4.	"Artificial Intelligence Science and Society Part D: AI Science and
the Environment"

 <https://www.amazon.com/dp/9609156495?ref_=pe_3052080_397514860>
https://www.amazon.com/dp/9609156495?ref_=pe_3052080_397514860

About the Lecturer: This lecture and book are a result of a two-year effort
by Prof. Ioannis. Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP
fellow) and was influenced by his being principal investigator of 75+ R&D
projects on Computer Vision, Machine Learning, Digital Media and chairing
the International AI Doctoral Academy (AIDA). Prof. I. Pitas is Director of
the  Artificial Intelligence and Information Analysis (AIIA) lab at the
Aristotle University of Thessaloniki (AUTH), Greece. He was chair and
initiator of the IEEE Autonomous Systems Initiative (ASI). He has
(co-)authored 15 books, 45 book chapters and over 950 papers in the above
topics. He has 34500+ citations to his work and h-index 87+. He is ranked
319 worldwide and first in Greece in the field of Computer Science (2022).

 

This lecture is part of the SIG  <http://icarus.csd.auth.gr> Icarus 'Digital
days' lectures offered by the AIIA Computer Vision and Machine Learning (
<https://aiia.csd.auth.gr/computer-vision-machine-learning/> AIIA.CVML) R&D
group of the  Artificial Intelligence and Information Analysis (
<https://aiia.csd.auth.gr> AIIA) lab at the Aristotle University of
Thessaloniki (AUTH), Greece. These lectures are disseminated through
multiple channels and email lists (we apologize if you received it through
various channels). 

 

Post scriptum: To stay current on CVML matters, you may want to register in
the CVML email list, following instructions in:
https://lists.auth.gr/sympa/info/cvml

 

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