[SydPhil] Webinars - General Announcement and First Speaker [Prof Witold Pedrycz] at IDEAS -[Xiamen U., Moscow State U, Xiamen City, and Tsinghua U]

[Mirko Farina]

Dear All,

The Human Machine Interaction [HMI] Lab under the guidance of the Center
for International Economic and Technological Cooperation of the Ministry of
Industry and Information Technology (People's Republic of China), in
partnership with both The Institute for Digital Economy and Artificial
Systems [IDEAS] of Xiamen City and with The Research Center for
Technological Innovation at Tsinghua University
<https://protect-au.mimecast.com/s/Xa4PCBNqjlCDOG8LPtzN80n?domain=streaklinks.com>
is
pleased to invite you to attend our series of webinars for 2024.

*Description and Goals*
In our series of webinars, we focus on the overarching implications and
consequences of the ongoing AI revolution. We do so in the context of
Digital Development and Artificial Systems with the goal of encouraging and
promoting an inclusive and responsible digital transformation capable of
addressing constraints on global digital divide; deepening cooperation in
digitization, industrialization, and innovation. Our series also strives to
deliver outcomes in connectivity and thus to increase trustful
collaboration between Westerns and Chinese universities. Through this
platform we therefore aim to foster and promote multidisciplinary,
international academic dialogue.

*Structure*
The series begins [PART A] by providing a primer on the algorithms,
techniques, and statistical methods used by computer scientists and by
addressing [PART B] some foundational theoretical (epistemological and
phenomenological) questions relevant to the issue of digital
development/innovation. It continues [PART C] by broadly assessing -from
the perspective of general policy making- the conditions for the
application of these techniques in society (in fields such as
government/management and healthcare). The series then reviews and
evaluates [PART D] the merits, possibilities, and challenges associated to
the widespread implementations of digital and artificial systems in ‘lived
environments’ (in fields such industrial automation, green computing,
internet of things). Finally, the series ends [PART E] by offering careful
reflections on major ethical and privacy issues (ranging from algorithmic
transparency, accountability, and fairness to responsibility,
interpretability, and general security) related to digital development.

*CONFIRMED SPEAKERS [FULL LIST]:*
https://protect-au.mimecast.com/s/IlK2CD1vlpTok938jc5JT8m?domain=docs.google.com

------------------

The first webinar of the series will take place on:
March 7.2024 6:00 PM (Edmonton Time);
*March 8. 2024 9:00 AM (Beijing Time)*.

The speaker will be Prof  FRSC, *Witold Pedrycz,* Canada Chair
-Computational Intelligence (University of Alberta):
https://protect-au.mimecast.com/s/MBHhCE8wmrtlN7pZ1uprJNH?domain=scopus.com ;
https://protect-au.mimecast.com/s/hZrbCGv0oyCBQ7AzWuQHtvF?domain=scholar.google.com

Title: 'A Unified Data and Knowledge Environment of Machine Learning'

Abstract: Driven inherently by the technologically advanced learning and
architectural developments, ML constructs are highly impactful coming with
far reaching consequences; just to mention autonomous vehicles, control,
health care imaging, decision-making in critical areas, among others. Data
are central and of paramount relevance to the design methodology and
algorithms of ML. While they are behind successes of ML, there are also
far-reaching challenges that require urgent attention especially with the
growing importance of requirements of interpretability, transparency,
credibility, stability, and explainability.   As a new direction,
data-knowledge ML concerns a prudent and orchestrated involvement of data
and domain knowledge used holistically to realize learning mechanisms and
support the formation of the models. The objective of this talk is to
identify the challenges and develop a unique and comprehensive setting of
data-knowledge environment in the realization of the development of ML
models. We review some existing directions including concepts arising under
the name of physics informed ML. Key ways of elicitation and accommodation
of domain knowledge are investigated. An impact on the structuralization of
the ML architectures and the ensuing implications on the interpretability,
explainability and credibility as well as semantic stability are studied.
We investigate the representative topologies of ML models identifying data
and knowledge functional modules and interactions among them. The detailed
considerations on the facet of explainability including new ideas of
semantic stability are covered. We also elaborate on the central role of
information granularity in this area. Illustrative examples involving
rule-based models, neural networks, logic-oriented networks are discussed.


LINK to join the event on MS Teams
https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTI4ZDkxZGMtMTZlNS00YjA0LWJhMTQtODg1YTQxM2I5NzIz%40thread.v2/0?context=%7b%22Tid%22%3a%22301eaa44-6f71-406e-ab85-4cbeec1c1016%22%2c%22Oid%22%3a%22eb9bdfbf-2621-4854-86f6-eec757c6b1f0%22%7d

Full Poster available for perusal at:
https://protect-au.mimecast.com/s/ULPSCJyBrGfBDJKjpuvbj_q?domain=drive.google.com


Specific announcements for subsequent meetings (as well as a reminder for
the one advertised here) will follow in due time

Thank you
Cheers,
Mirko

---
Mirko Farina 法觅舸, PhD, MPhil
Website: https://protect-au.mimecast.com/s/RebOCK1DvKTDGo4R8tGZcLS?domain=mirkofarina.weebly.com

Professor
Head of Human Machine Interaction Lab [HMI Lab]
Institute for Digital Economy & Artificial Systems [IDEAS]
Xiamen University [XMU] and Lomonosov Moscow State University [MSU]
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