MindMeld: In the Beginning – Book Introduction

mindmeld

By Thomas B. Cross @techtionary

This is part of a series and introduction to artificial intelligence (AI) in the new ibook MindMeld: CEO & AI – Merging and Mental.  The complete ibook can be found on iBooks.

Artificial intelligence (AI) or the term I will use knowledge technology (KT) is the application of machine systems to problems of human endeavor. For the CEO, the purpose is not necessarily to develop systems that replace humans, but to allow the use of systems that increase human effectiveness and efficiency. This book is not a “how to” or cookbook for “melding” AI into the CEO or corporate organization.  However, there are many key ideas built through decades of research and experience to provide viable directions and ideas.  At the core is that AI must start with the CEO and not just simple tasks.  The goal is to encourage humans to do what they do best, whatever this might be, at a time deemed appropriate, and to allow machines to assume the functions best suited to them, such as power saws, mechanical arms, and payroll computers do. KT has many major functions that are discussed here.

Some of the fundamental KT issues addressed are:

– Ergonomics – human-machine interaction

– Problem-solving and decision-making

– Thinking – Discussing – Arguing

– Communication People-people, people-machine, machine-people and machine-machine

– Dark-side issues (fear, conflict, trust, sabotage privacy, etc.)

– Intelligence – non-intelligence, disinformation

The future

These issues are woven throughout the book, supplying the topical glue between distinct discussion areas. Of course, there are other areas related to these issues that are not covered here. It is the purpose of this book to focus on the concepts and issues of KT that will impact on CEO decisions and business management strategies, productivity, and the key element of any business—its people. Much like auto mechanics determined to increase engine speed, those of us engaged in research in these areas often fail to present adequate and informative reasons for such enthusiastic pursuit. There are many humanistic and bottom-line reasons for explaining KT and its potential benefits to humankind. Experts suggest that automation—the augmentation of manpower by machine power—is not the only way in which fundamental research in heuristic problem-solving is likely to contribute to productivity in our society. The most important productive resource in our economy and, very likely, the most important resource for generations to come, is brainpower. We are now learning a great deal about how this brainpower operates—about the processes of human thinking. This subtle, yet key issue is fundamental to any research in KT. It is the pursuit of humanistic uses of technology that offers the greatest challenge and hope for improving productivity and performance, while also improving the quality of life. There are no right or wrong answers, only new problems to be solved. In the past, machines held forth the ability to free people from mundane, tedious, and repetitious tasks and chores. KT promises to facilitate human communication and interaction in ways unavailable or impossible without these systems. Moreover, as these systems become more “intelligent” and more competent, they should gradually acquire the ability to make decisions on their own. The application of self-directed thinking machines offers mind-boggling benefits to education, quality control, and field engineering, to name only a few applications. The development of KT systems that can go places too toxic or otherwise unfit for humans is reason enough to pursue the development of such systems.

mindmeld

Knowledge Technology Limitations and Possibilities

We now stand at the foot of the KT mountain, trying to find the most efficient path to the top. Today, the limitations of KT are associated with these general areas:

– Computing capability

– Problem definitions

– Machine languages

– Human knowledge ability

Of these four areas, the basic horsepower problems of computing “engines” is the easiest to tackle. Computing processing capability has crossed 100 petaflops (quadrillion floating point operations per second) this year and double-double and redouble that in the years ahead. Communications technology now easily exceeds a 100 billion bits per second using fiber optics and even wireless. Storage technology now often exceeds a hundreds of trillions bits in a refrigerator, and soon a breadbox; and so on, and so on. Technology has often exceeded most projections and expectations. Yet, the processes of how humans approach a problem or even have a conversation about anything is falling short.  How we explain these problems to machines are closely related. If we cannot explain problems to one another how in the world can we explain them to machines.  If we do then the outcomes can be as expected as we have for humans. Although current human interfaces and programming languages offer vast improvements, still their development remains in the dark ages.  The ability of humans to adequately understand, define, analyze, structure, and utilize a machine model is also quite fundamental in its development. People have a hard time identifying problems and reaching solutions in a normal human environment. When you introduce the handicap of trying to explain a problem using today’s programming languages, the task is even more difficult. It is, of course, a major fallacy of computer folklore that machines can, in any real way, replace people. However, this does not mean that the machines or systems of the future will not be as intelligent as people. Fears concerning these developments are both justified and unjustified. This seeming paradox portends that we can achieve all of our hopes for machines and for humans as well.

There is a dark side to computer advancement that corresponds to the dark side of humans. Despite their benefit to people, intelligent machines often become monsters of the ID; they can be used in any possible way for war, invasion of privacy, and human destruction. In this case, machines are only extensions of humans and can be used as their warriors. This issue both leads to and stems from the area of human knowledge ability. Humans must recognize their own frailties, inadequacies, and limitations, and how they affect the technology. This is different from the problem definition issue. Humans might not as yet be adequately equipped to understand all of the forces behind intelligence, thinking, or other human mental processes. We might need to evolve a bit further before we can develop machines that can be as intelligent in as many ways as we are. This is not to say that humans will not design, build, and use these “power tools”; rather, it might take a massive effort of machine power, human-machine interface, and new problem-solving methods just to develop machines that have an average IQ. This book is intended to be an intermediate step between understanding the vast complexities of how knowledge technology works and realizing the practical effects it has on how we manage our business affairs. We might not need to know one to understand the other, but it could help.

More can be found in the book.

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