Keynotes
- Hamid Garmestani (Georgia Institute of
Technology):
Hydrogen Economy and NanoMaterials
- Lotfi A. Zadeh (The University of California,
Berkeley):
Toward Human Level Machine Intelligence - Is it Achievable?
The Need for a Paradigm Shift
Abstract: Officially, AI was born in 1956. Since then, very
impressive progress has been made in many areas - but not
in the realm of human level machine intelligence. Anyone
who has been forced to use a dumb automated customer
service system will readily agree. The Turing Test lies far
beyond. Today, no machine can pass the Turing Test and none
is likely to do so in the foreseeable future.
During much of its early history, AI was rife with
exaggerated expectations. A headline in an article
published in the late forties of last century was
headlined, "Electric brain capable of translating foreign
languages is being built." Today, more than half a century
later, we do have translation software, but nothing that
can approach the quality of human translation. Clearly,
achievement of human level machine intelligence is a
challenge that is hard to meet. Humans have many remarkable
capabilities; there are two that stand out in importance.
First, the capability to reason, converse and make rational
decisions in an environment of imprecision, uncertainty,
incompleteness of information, partiality of truth and
possibility. And second, the capability to perform a wide
variety of physical and mental tasks without any
measurements and any computations. A prerequisite to
achievement of human level machine intelligence is
mechanization of these capabilities and, in particular,
mechanization of natural language understanding. In my
view, mechanization of these capabilities is beyond the
reach of the armamentarium of AI - an armamentarium which
in large measure is based on classical, Aristotelian,
bivalent logic and bivalent-logic-based probability theory.
To make significant progress toward achievement of human
level machine intelligence a paradigm shift is needed. More
specifically, what is needed is an addition to the
armamentarium of AI of two methodologies: (a) a
nontraditional methodology of computing with words (CW) or
more generally, NL-Computation; and (b) a
countertraditional methodology which involves a progression
from computing with numbers to computing with words. The
centerpiece of these methodologies is the concept of
precisiation of meaning. Addition of these methodologies to
AI would be an important step toward the achievement of
human level machine intelligence and its applications in
decision-making, pattern recognition, analysis of evidence,
diagnosis and assessment of causality. Such applications
have a position of centrality in our infocentric
society.
- David Wilkinson (The University of British
Columbia):
Material and Engineering Challenges for the Fuel Cell
Industry
Abstract: There are significant global environmental and
supply issues with existing energy paths today. Global
emission and fuel regulations, global fuel and power
structure, energy security, and cost are driving new
technology and non-conventional approaches. The fuel cell
and direct electrochemical fuels provide the promise of
being one of the long-term solutions to the improvement of
energy efficiency, energy sustainability, energy security,
reduction of greenhouse gases and urban pollution.
Technical progress as well as investments in fuel cells for
transportation, stationary, portable, and micro fuel cell
applications have been significant in recent years. The
present view is very optimistic for fuel cell power
generation and the status is presently at the field trial
level and early commercialization stage, moving into volume
commercialization. However, fuel cells will need to be
competitive on an economic basis with the established and
highly developed internal combustion engine and other forms
of power generation. Significant technical challenges still
remain today in a number of areas including reliability,
durability, cost, operational flexibility, technology
simplification and integration, fundamental understanding
and life cycle impact. New advanced materials and
associated engineering design will be required to close
these technical gaps. This presentation will provide a
perspective on fuel cell technology today, research and
development directions, and the material and engineering
challenges the fuel cell industry faces.
- Saeid Nahavandi (Deakin University):
Knowledge Visualisation for Large Complex Systems
Abstract: One of the indispensable tools of the information
age is computer modelling and simulation. It is used
extensively in design, operations, analysis,
decision-making, optimisation, and education and training.
World class operations rely heavily upon simulation to
develop efficient systems and operations that produce
quality products and services. Computer simulation enables
scientists and engineers to better comprehend and predict
three-dimensional and time-dependent phenomena in science
and engineering disciplines.
This talk will focus on challenges associated with
modelling and simulation of large complex systems and how
best the knowledge can be visualised and used as a means of
communication to various levels of management in any
organisation.