• 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.