Intellect Driven Design – the dominance of thinking.
Let’s first look at how machines are designed. The picture consciousness of machine design tends to be driven by the metaphor of machine as mechanised system. Other metaphors have more recently come into play, particularly in terms of the biological metaphor and the idea of cellular development of systems, as well as evolution. However such metaphors still are usually dominated by a meta-metaphor of mechanics as the sole underlying universal principle.
Where thinking as an activity underpins the design process, technologies arise which tend to be logically constructed. The principle of linear flow comes in to play based upon a clear cause-effect relationship between processes and sub-processes and intended process outputs. As processes become more complex, in turn they may become more complexly understood in terms of decision trees allowing process flexibility to increase. For example, the ability of a machine to change tool automatically at a given process stage.
Rational design tends to form the basis of thinking-driven machine design. This tends to lead to an emphasis on visible data collection inherent in the process where cause-effect impacts can be objectively monitored in and post-process. Performance measurement in terms of critical data points allows for statistical process control. Inherent in the design are core concepts, which can be derived through repeated observation and experimentation from the machine design. One can identify certain rational principles at work. One can ‘derive’, through logical extrapolation, the thinking process inherent in the design. Where thinking is clear and applied, accuracy is an emergent property in the process. For example, certain principles of physics, particularly in terms of electricity and heat processes can be derived from analysis of the design of a rationally constructed laser cutter. One can literally derive the laws of nature at work in the machinery. A basic principle emerges in rationally thought-out technology design: the laws applied can be later rationally derived.
The weakness of thinking driven design, which has a one-sided tendency towards rationalism and logic, is the design of machines which are unable to cope with complex change and environmental factors exhibiting chaotic properties. Recently developments in inductive machine learning attempt to correct this at the process interface through rational in process learning combined with use of basic artificial intelligence.
By thinking, a simplistic definition is here used. Thinking refers to rational thought processes based on logical interpretation and design. Cause and effect is applied to process design and mechanical construction. Designs are based upon rational imaginations or ‘diagrams’, which form blueprints for machine construction. Design parameters are set by applying thought-out principles of physics and mechanics to physical technology construction. Software is designed in a similar way. Imagination relates to the projection of general principles into space and time-specific materials and parts to construct mechanical forms, which act in predictable ways according to the laws of cause and effect, aimed at realising pre-defined extrinsic goals.
Where human thinking is unable to cope with the complexity of a design element, the use of existing data, knowledge bases and technologies are used to aid design and construction. These comprise databases of previous thinking and applied thought.
Flexible manufacturing technology is based on thinking which contains imagination which is future as well as present and past focused. Poorly designed technology will apply to only present and past-based thinking. Cause and effect is related to ceteris paribus assumptions, where it is assumed that what is currently now will continue largely unchanged into the future. In metaphorical terms we build our house of straw because weather conditions have always tended towards calmness with little or no wind. Where imagination is allowed to be more flexible our thinking is allowed to follow the principles of the cause and effect decision tree. We apply risk and probability analysis. We analyse trends and our thinking becomes an imagination of the technology in operation under different scenarios. This can be termed ‘what if?’ thinking. It involves an activity of creating maps and models of possible futures, potential cause and effect ‘pathways’. It then becomes possible for thinking to inform a more flexible design which allows machines to become adaptive to changes in the emerging future.
Flexible technology has tended to be based on this kind of imagination-based thinking. It requires trend analysis on extrapolation of trend data into applied design. For example, machines which can deal with different batch sizes or can adapt to declining part sizes have emerged in electronics manufacturing.
One of the problems of intellect driven design occurs at the human-to-machine interface. The majority of machines require some for operation. Designers tend to look for predictable behaviours in operators and design accordingly. Ergonomics makes rational assumptions about human physiology, as well as physical movement. Machines are often designed to interface with the human body. Where integration is high, the machine can become a prosthesis to a particular human limb or limb system (or vice versa!). At even higher levels of integration there arises the concept of the ‘cyborg’ with the operator become part of the machine (or vice versa!). Futurologist predict that it will only be a matter of time before technology extends the cyborg concept beyond the limb system into the physical senses (already the case with vision, touch and smell) and even to the human brain itself. Here a kind of circular process occurs (some might call it a nightmare scenario) where rational thinking leads to design of machines which can interface with the very process of thinking itself. The optimists would see this as a means of enhancing rational thinking processes and making them more efficient. The pessimists would see it as an ultimate subjugation of human creativity to the mind of the machine. The practical problem that arises concerns the elements of human behaviour which cannot easily be rationally interpreted or predicted. These include emotional factors such as motivation, and even less tangible elements such as ‘mood’, ‘aptitude’ and ‘creativity’. Humans do not behave predictably all of the time and, as processes become more complex, behaviour becomes even harder to predict in circumstances requiring processes such as creative problem solving, innovation management, team working, inter-functional communication, experimentation, failure mode effects analysis, and so on.
Passion Driven Design – the dominance of feeling
The concept of emotional intelligence has been popularised by Daniel Golman. Perhaps less well known is the concept of ‘seeing with the heart’ as described by the Austrian Philosopher Rudolf Steiner.
Seeing with the heart, on a metaphorical elve, can be described as the ability of a designer to gain a subjective feeling of a technology in implementation based on an exploration of personal emotional response to a visioning or ‘imagination’ of the technology in implementation. Put simply, “How would I (the designer) feel if I were using this machine myself?” Awareness of feelings arising from such imagination can increase the level of understanding of certain parameters in the design from the point of view of the user. Such feelings might comprise positive or negative responses. Such increased awareness may also create a sense of sympathy with potential operators leading to confirmation that the design is favourable or, conversely, that it is in of redesign.
One water-jet manufacturer in Germany invited children to input ideas into the design process, particularly in terms of the appearance of the machine. The assumption that the innocence of the child is more free of the baggage of ‘rationality’ underlies the importance of imagination more informed by feeling or will than thinking alone.
Emotional Response Analysis
Emotional Response Analysis, as mentioned earlier, is a phrase we have coined to describe the data generated by analysing the feelings and reactions of key stakeholders in a technological process. These stakeholders include:
The use of a variety of research methods in order to collect perceptual data yields knowledge and experience of the technological process enabling further innovation, either of the process itself or of new or current other processes. Such methods traditionally have included:
·direct and participant observation
·face to face interviews
Ronnie Lessem, in his book Total Quality Learning presents a threefold model of the human being, drawn from established psychological models. The three levels are – cognitive, affective and behavioural. The cognitive level is the level of intellect and surface perception, suited to the most established modes of scientific observation and materialistic science. The behavioural or active level focuses on human action, the achievement of tasks and, in psychological terms is also the realm of motives for action, the most hidden or least conscious level in the human being, sometimes referred to as the ‘will’ level. Innovation requires an awareness of all three levels, not just intellect.
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