Getting It Right: R&D Methods for Science and Engineering

By Peter Bock

Getting it Right: R&D Methods for Science and Engineering, Second Edition, is an authoritative guide to the methodologies that produce coherent and complete R&D projects. Based on the author’s experience in large industrial firms, this book addresses the avoidance of common pitfalls that engineers and scientists routinely face in industry and academia. Special emphasis is placed on the comprehensive analysis of project problems, requirements, objectives, the use of standard and consistent terminology and procedures, the design of rigorous and reproducible experiments, the appropriate reduction and interpretation of project results, and the effective communication of project design, methods, results, and conclusions, embedded in a clear and modern framework of the Scientific Method.

This fully updated new edition also includes an extended case study from industry, additional material about the evolution of knowledge and science and technology and a special focus on the discovery and nurture of technical innovation, both of which reinforce the importance of adherence to the described methodology in both academic and industrial venues. Professional engineers and researchers will find a highly consistent and practical reference for the rigorous conduct and clear communication of complex R&D projects. Students will also find a palatable introduction to the critical concepts of knowing, doing, and Getting it Right.

• Presents a standard methodology for conducting rigorous and complete R&D projects
• Includes a detailed case study from an experienced R&D research scientist and engineer
• Provides a consistent framework for knowledge organization and the Scientific Method

• Language: English
• Publisher: Academic Press
• Release date: Apr 11, 2020
• ISBN: 9780128161661

Peter Bock

Bock received an undergraduate degree in Physics from Ripon College in 1962. After finishing his graduate studies, he was invited to join the NASA Apollo Program, eventually becoming the director for orbital simulation software development at NASA headquarters in Washington DC. Following the first successful manned Lunar landing in 1969, Bock was invited to join the faculty of the Department of Computer Science at The George Washington University in Washington DC, where he designed and established a new graduate curriculum in Artificial Intelligence. Over the next 20 years he added courses in neurophysiology, cognitive science, and statistics to the computer science core in the graduate AI curriculum to expand the biological knowledge and sharpen the empirical perspective of the students. During a two-year stay as a visiting professor at the University of Ulm in Germany, Bock and his graduate students developed the well-known Project ALISA (Adaptive Learning for Image and Signal Analysis) with generous funding from the large German corporation Robert Bosch GmbH. For the next 20 years, research funding from both industry and government enabled the support of many doctoral students who are now successfully employed in academia or industry around the world. Bock has published more than 100 scholarly papers and book chapters, as well as presented many invited lectures in government, academia, industry, NGOs, and special public events. Bock retired from George Washington University in 2011, but still directs several doctoral students as well as his own research in AI, focusing primarily on natural language processing, high-dimensional clustering, and the development of artificial neural networks that acquire and apply their knowledge using adaptive statistical learning. In 2012 he was invited to present a TED Talk, which can be viewed at www.youtube.com/watch?v=CpNfy7AUPl4. His long-term research objective has remained unchanged for the last 45 years: the construction of an artificially intelligent being (already named Mada) whose intellectual and emotional capabilities are on a par with human beings. In addition to his technical background, Bock speaks German and is broadly educated in the humanities and social sciences, with special interests in theatre, music, the history of technology and culture, and neonatal developmental psychology. He lives with his wife in downtown Washington DC in the midst of a jumble of computers and musical instruments.

Book preview

Getting It Right

R&D Methods for Science & Engineering

Second Edition

Peter Bock

Table of Contents

Cover image

Title page

Copyright

Foreword

Preface

Acknowledgements

About the Author

About the Illustrator

Part I: Introduction

Introduction

Chapter 1: Research and Development

1.1 The Emergence of Research

1.2 The Emergence of R&D

Chapter 2: Process and Preparation

2.1 Learning the Methodology

2.2 Tools and Resources

Part II: Project Organization

Introduction

Chapter 3: The Project Hierarchy

3.1 Top-Down Planning

3.2 Time and Cost Planning

Chapter 4: The Project Task

4.1 Task Domain

4.2 Solution Method

4.3 Task Range

Part III: Knowledge Representation

Introduction

Chapter 5: An Epistemological Journey

Chapter 6: Categories and Types of Knowledge

6.1 Speculative Knowledge

6.2 Presumptive Knowledge

6.3 Stipulative Knowledge

6.4 Conclusive Knowledge

Chapter 7: Roles of Knowledge Propositions

7.1 Domain Knowledge

7.2 Factors

7.3 Range Knowledge

Chapter 8: The Limits of Knowledge

8.1 Accuracy and Error

8.2 Uncertainty

8.3 Precision

8.4 Knowledge, Truth, and Humility

Part IV: The Scientific Method

Introduction

Chapter 9: Overview

9.1 History of the Scientific Method

9.2 The Modern Scientific Method

The Handshake

Chapter 10: Analysis

10.1 Describe Problem

10.2 Set Performance Criteria

10.3 Investigate Related Work

10.4 State Objective

Chapter 11: Hypothesis

11.1 Specify Solution

11.2 Set Goals and Hypotheses

11.3 Define Factors

11.4 Postulate Performance Metrics

Chapter 12: Synthesis

12.1 Assemble Resources

12.2 Implement Solution

12.3 Apply Solution Method

12.4 Reduce Results

Chapter 13: Validation

13.1 Compute Performance

13.2 Draw Conclusions

13.3 Prepare Documentation

13.4 Solicit Peer Review

Appendix A: Bibliography

Research Methodology

Data Presentation and Visualization

Statistics

Appendix B: Glossary

Appendix C: Tips

The Handshake

APPENDIX D: Summaries and Guidelines

Appendix E: Case Study Figures and Tables

Appendix F: Sample Experiment Protocol

Appendix G: An Algorithm for Discovery

Index

Copyright

Elsevier

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

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Foreword

Fridolin Piwonka, Vice President, Corporate Research and Development (retired)

Robert Bosch, GmbH, Stuttgart, Germany

I first met Peter Bock in 1989 at a symposium he had organized on Adaptive Learning Systems at Schloss Reisensburg, a castle near Ulm in southern Germany. Although since retired, I was then the head of Corporate Research at Robert Bosch GmbH, Germany's largest automotive supplier. Peter and I got acquainted over drinks in the garden overlooking the Danube River, and this was the beginning of a warm friendship and a strong professional relationship.

At that time, Bosch Corporate Research was investigating pattern recognition and classification methods to process signals, images, and three-dimensional data representations like sonograms. Peter, who was on sabbatical leave from his home institution, The George Washington University in Washington DC, had been invited to set up a research project at the newly established Research Institute for Applied Knowledge Processing (FAW) in Ulm, Germany. The objective of the research project was to design and build a large-scale system for sophisticated image and signal processing, based on Collective Learning Systems Theory, a supervised adaptive learning paradigm that Peter had first proposed in the middle 1970s and had been developing and applying ever since. The new adaptive image and signal processing system was named ALISA, which stands for Adaptive Learning Image and Signal Analysis.

I was impressed by the rigorous methods which Peter applied to the design of the ALISA engine and manage the research project at the FAW. To implement his design, he brought together a group of graduate students from The George Washington University and scientists and engineers from Bosch. Strict adherence to the Scientific Method was the guiding methodological principle of the project. It was an enlightening experience for everyone involved in the project to witness the power of this approach in action. The project enjoyed quick success and was subsequently funded by Bosch well into the new century.

Peter brought with him a rich background of knowledge and experience in the design and direction of R&D projects for many different kinds of organizations, including NASA, NIST, the US Navy, various industrial firms, research institutes, and academia. He started his professional career in 1965 doing professional software development for NASA, where he was responsible for the design of a computer simulation to determine the most efficient spatial and temporal sequences for remote sensing activities on the Earth orbital missions for the Apollo Program.

When the Apollo program began to wind down, in 1970 Peter was invited to join the faculty of the Department of Electrical Engineering and Computer Science at The George Washington University (GWU), designing and teaching computer science courses, including artificial intelligence, adaptive learning systems, cognitive science, robotics, and simulation. He was primarily responsible for the development of the graduate degree program in Artificial Intelligence and Cognition. It was here that he pioneered the research in Collective Learning Systems Theory. Reflecting the neurophysiological and psychological processes of the mammalian brain, he designed a hierarchical network of learning automata to simulate the architecture and adaptive functionality of the human cerebral cortex: learning from nature how nature learns.

Over the years, Peter and I had sat together on many occasions and lamented the fact that so many of our young scientists and engineers leave the universities with a good foundation of knowledge in their disciplines, but with little idea how to design and conduct real-life R&D projects successfully. Finally, Peter decided to try to do something about this problem. In 1996, he set up a workshop at GWU to present his ideas for conducting more productive R&D in science and engineering. We brought this workshop to Bosch Corporate Research in Stuttgart. Peter's lecture notes quickly expanded into a set of basic principles and guidelines, which he frequently took on the road to various industrial and academic venues. In 1999, the CS Department at GWU added a formal course on R&D methods, which Peter taught for many years.

At that point it seemed only natural that Peter should transform his lecture materials into a book for the research and development community at large. By combining his knowledge and experience into both a textbook and a reference text, his intention was to provide a complete and consistent methodology for science and engineering students, professional scientists and engineers, project managers, and administrators for the design, conduct, and management of R&D projects. The question facing Peter, however, was how to present this complex material in a reasonably linear fashion, while avoiding the sterility of an IF-THEN-ELSE cookbook approach and a dry and overly pedantic presentation. It is a bit of a mystery to me exactly how he managed to do this, but in fact he did. The first edition of this book was published by Academic Press in 2001.

The primary focus of the book is on the Scientific method, the basic and unchanging framework for assuring that research and development tasks are carefully organized and orderly processes. At first readers will first learn about the categories and types of knowledge arranged in a useful taxonomy, with well-defined and useful exercises to harden the concepts. In the working world, professionals can also use the book as a reference for answering questions that arise during their daily R&D activities. Project managers will learn how to design a project task tree and a milestone chart in evolving stages, using pilot tasks to explore new ideas before scarce resources are committed to formal investigations. Administrators will learn that every project needs a management reserve, an appropriate tool set, and the allocation of adequate time and money to acquire new tools and maintain the project environment.

The book offers a wealth of examples that lead the readers to the underlying reasons for failures and successes of their projects. Carefully constructed summaries, tables, figures, and extensive sets of useful Tips and practical Exercises give the reader brief answers about how to avoid common pitfalls, such as chaotic design processes, unconscious bias, poor design of user interaction modes, and the inappropriate application of canned statistics packages. Provocative and amusing illustrations and pictures catch the reader's eye, add spice to the experience, and serve as convenient bookmarks and reminders for special problems and messages. Peter not only provides clear explanations, but also puts the information in its historical and cultural context, making it part of something larger, an essential perspective in the modern multidisciplinary world.

The central focus of the book is the Scientific Method, a modernization of the classical process that proceeds from a detailed analysis of the problem along with precise statements of the associated knowledge, to a clear formulation of the project objectives, to the synthesis of solutions with well-defined goals and hypotheses, to the design and conduct of rigorously controlled experiments, to the generation of reproducible conclusions and reliable products, emphasizing that all research results must be validated by formal peer review within the scientific community, and all industrial development projects must ultimately prove the worth of their products to their customers.

In this second edition, Peter has updated the book to keep the concepts and examples fresh and relevant after 20 years. I hope it will continue to convince universities to include courses in research methods as required components of their curricula, as well as persuade R&D departments in industry to offer in-house courses and seminars that present the rigorous methods for knowledge acquisition and the Scientific Method, with Peter's book at the heart of their efforts. Read, enjoy, and learn!

Preface

Peter Bock, Washington, DC

Innovation is the creation of something brand new and of significant value. It is triggered by a spark of inspiration that lights the fuse of creative possibility. But the path from inspiration to innovation can be very challenging, and all too often the fuse sputters and goes out; possibility does not imply certainty by any means.

We often see the moment of inspiration in others. Someone unexpectedly stops talking mid-sentence, the head comes up and tilts slightly, the brow furrows, and the eyes narrow. This announces the fusion of thousands of thoughts into one grand idea, the aggregation of many seemingly unrelated cognitive tidbits into a single revelation. Sometimes the spark is an accident of fate or an unpredictable consequence of serendipity; sometimes it is the result of conscious intellectual effort; and sometimes it just seems to come out of nowhere.

But even though the duration of the inspiration can be very short, the time it takes to turn this inspiration into innovation can be very long, fraught with false leads and mind-numbing recursion that all too often lead to a dead end for a variety of intractable obstacles. That is probably what Edison meant when he described the process of innovation as 1% inspiration and 99% perspiration.

Although this book directly addresses the challenges of transforming hypotheses or goals into meaningful conclusions or successful products, it does not attempt to characterize any special backgrounds and lifestyles of people who can successfully transform their inspirations into innovations. But clearly, that would be very worthwhile knowledge for evaluating potential staff members for R&D projects in any field that places a high value on the ability to innovate!

To acquire such knowledge we could mount a huge research project to build a list of all the major innovations throughout history with the objective of understanding the backgrounds and lifestyles of the associated innovators. However, just listing the innovations would provide very little insight into the minds of the innovators, whose identities may not even be known. Who, for example, invented the wheel, or bronze, or coffee, or scissors, or oil colors, or vellum, or the arch?

So instead of trying to list all the significant innovations, suppose we list all the people throughout history who have been posthumously acknowledged as significant innovators, regardless of the field of their innovation. After all, it is the backgrounds and lifestyles of innovators we want to study, not the innovations themselves. Once we have constructed that list, we can research the lives of each of these significant innovators to extract the features of their backgrounds and lifestyles in an effort to identify a small number of combinations of features that, taken together, could identify other significant innovators with some reasonable confidence.

That seems like a sound approach ... providing you have the time and resources to extract sufficient information about their cognitive abilities and backgrounds to cluster these features into a small set of meaningful and statistically valid classes. However, there will probably be many thousands of significant innovators on the list. Even a large team of researchers would not be able to summarize this information and extract meaningful features at an affordable cost and in a reasonable amount of time.

Thus we need to winnow down the number of innovators on the list by listing only those who have achieved outstanding records of innovation. Suppose, for example, we include only those significant innovators who are recognized for at least three significant innovations in their lifetimes, thus earning the title of prolific significant innovators. Applying this condition seems fair: it is probably a safe assumption that the most remarkable innovators would probably be those who have done so repeatedly. Clearly, Leonardo da Vinci would make the list, but Sir John Harington, whose only significant innovation was the flush toilet in the late 16th century, would not.

Unfortunately, imposing this additional condition only reduces the number of qualified innovators to perhaps a few thousand, still requiring an immense research effort. Thus we must apply an additional condition to the master list to eliminate all but a small number of the especially promising candidates. As it turns out, a single additional condition will do the trick: include only those prolific significant innovators who are recognized for innovations in at least three different application domains. I postulate this condition under the assumption that broad experience and involvement with the world and its ways is critical for successfully meeting the challenges of significant innovation repeatedly. Again, Leonardo would make the list, but James Watson, who deciphered the structure of the DNA molecule, would not. Upon applying this third condition, our final list is limited to diverse prolific significant innovators. I have dubbed these talented individuals super innovators.

How many super innovators can we expect to be on this final list? Over the final months of writing this book, I gave quite a bit of thought to this question. To my amazement I was only able to identify about twenty individuals whom I could confidently classify as super innovators. To honor them, I have placed full-page photographs or portraits of ten of these super innovators at various places in the book. They are in no special order or context. Innovation can happen anytime and anywhere.

I have not done any detailed research into the backgrounds of these ten super innovators. To those of you who find this task interesting, I invite you to extract some meaningful quantitative features from their backgrounds and lifestyles, and then cluster these feature vectors in an effort to identify any significant classes of commonality. Even though the sample is small, perhaps a reasonable model of a super innovator will emerge from this analysis.

If you want to increase the sample size, you can gradually relax the conditions to allow other innovators to be included in the sample. Be careful to let a consistent set of factual conditions drive the process (e.g., the number of siblings, the birth order of the candidate, or the age at death of the candidate). Pease note that using qualitative conditions based on your opinions, (e.g., the usefulness or the economic value of an innovation) will simply produce a self-fulfilling prophecy.

There's much more about these challenges and related issues in the book. Enjoy!

Acknowledgments

First and foremost, my loving wife, Donna, who is my best friend and my most treasured critic. In addition, my dear friend and talented artist Bettina Scheibe, who created even more amazing illustrations for this edition and added a myriad of subtle touches of color to so many of her delicate and zany drawings, all of which breathe indispensable life and fun into the book; my colleague and close friend Dr. Carsten Oertel, whose expertise in the current R&D world was indispensable for bringing this book up-to-date; Dr. Fridolin Piwonka, my colleague and friend of many years, who has graciously authored the Foreword for both editions; and finally Brian Guerin, John Leonard, and Paul Prasad Chandramohan at Elsevier, who patiently waded through so many of my unconventional proposals to liven up the book's style and appearance.

About the Author

Bock received his undergraduate degree in Physics in 1962. After finishing his graduate studies, in 1965 he was invited to join the NASA Apollo Program to direct the development of the Earth orbital simulation software for the Apollo remote sensing missions.

Following the first successful manned Apollo Lunar landing in 1969, Bock was invited to join the faculty of the Department of Computer Science at The George Washington University in Washington DC, to establish a new graduate curriculum in Artificial Intelligence. Over the next 20 years he added courses in cognitive science, neurophysiology, statistics and adaptive statistical learning to the computer science core for the graduate AI curriculum to expand the biological knowledge and sharpen the empirical perspective of the students in AI.

During a two-year stay as a visiting professor at the University of Ulm in Germany, Bock and his graduate students developed the software application ALISA (Adaptive Learning for Image and Signal Analysis) with generous funding from the large German corporation Robert Bosch GmbH. For the next 20 years, research funding from both industry and government enabled the support of many doctoral students who are now successfully employed in academia or industry around the world. Bock has published more than 100 scholarly papers, several books and book chapters, and presented many invited lectures in government, academia, industry, NGOs, and public events.

Bock retired from George Washington University in 2011, but still directs several doctoral students as well as his own research in AI, focusing primarily on natural language processing, high-dimensional clustering, and the development of artificial neural networks that acquire and apply their knowledge using adaptive statistical learning. In 2012 he was invited to present a TED Talk, which can be viewed at www.youtube.com/watch?v=CpNfy7AUPl4. His long-term research objective has remained unchanged for the last 50 years: the construction of an artificially intelligent being (already named Mada) whose intellectual and emotional capabilities are on a par with human beings.

In addition to his technical background, Bock is broadly educated in the humanities and social sciences, with special interests in theatre, music, the history of technology and culture, and neonatal developmental psychology. He lives with his wife in downtown Washington DC in the midst of a jumble of computers and musical instruments.

About the Illustrator

Bettina Scheibe is an artist specializing in production design and scenic painting. Born in 1976 in Germany, before settling down in her hometown at the age of thirteen, she had moved eleven times with her family, influencing her to become the traveller she is now.

Since childhood Bettina has been drawing ceaselessly, always striving to capture the essence of a person or the everyday moments in life. When her grandfather willed his painting tools and materials to her, she discovered that painting was her main calling.

Bettina graduated in 2000 with a degree in Performance Design from the Liverpool Institute for Performing Arts in England.

Since then Bettina balances creating her personal art in the solitary space of her studio with collaborating with other artists. She now works as a stage designer for theatre and as a production designer and scenic painter for film and TV. Woven in among these activities was the creation of the illustrations for both editions of this book.

Her permanent base is Berlin (Germany), traveling often to join creative projects in Europe and beyond.

www.bettinascheibe.com

Part I

Introduction

Introduction

Thomas Alva Edison (1847 - 1931)

Chapter 1

Research and Development

The professor turned off the projector and turned to his students at the end of the first class in R&D Methods, Some evening this week I would like each one of you to conduct an experiment. Find a moment to spend a few minutes alone. Treat yourself to a glass of wine or beer or whatever helps you relax. Put some rhythmic music on the stereo, classical or jazz or rock, whatever you enjoy. Plug in a set of earphones and turn off the loudspeakers. Light a candle and place it on a table about a meter in front of a comfortable chair. Sit in the chair, put on the earphones, close your eyes, listen to the music, and relax for a few minutes. Then, open your eyes and see if the flickering of the candle flame keeps time to the music. Please be ready to report your observations in class next week.

The next week in class, the students were all abuzz. Gee, Professor. It’s crazy, but you were right! The candle flame keeps time to the music! Not all the time, but it happened pretty often. We saw it! they chorused. Yeah, but how could that happen? one demanded. I was wearing earphones, and there was no sound coming from the speakers. There was no acoustical connection between the sound and the flame! Most of them, however, said, Nonsense! There was wasn’t any correlation between the flickering of the candle and the music rhythms. That’s impossible. But many of them saw it at least some of the time. Arguments and theories abounded.

The professor let them rant and rave for a few minutes. Then, when everyone finally quieted down, he spoke up. How many of you observed the candle flame keep time to the music? About 8 of the 20 students raised their hands. And how many of you observed no apparent correlation? The hands of the other 12 students shot up. How many of you carefully followed the experiment protocol I gave you? All 20 hands came up. And how many of you are being factual about your observations to the best of your ability? Again, 20 hands. So, may we conclude, asked the professor, that 8 of the candles kept time to the music, and 12 did not?

The room was silent while the students reacted to this proposed conclusion. That’s seems unlikely, said one student. And why didn’t it happen with all of us?

Well, said the professor, let’s look at the conditions that governed this experiment. First, each of you used a different candle. Maybe some kinds of candles work, and others don’t. Second, each of you was in a different room with different lighting conditions. Maybe the candle needs to be in a particular lighting condition to react to the music. Eyebrows went up on that one. Third, each of you probably chose a different kind of music: Mozart, Al Jarreau, The Stones, whatever. Maybe different candles prefer different kinds of music. Audible groans could be heard in the classroom. Fourth....

Maybe it wasn’t the candle, interrupted one of the students. Maybe it was me.

Please explain, Jack, the professor prompted.

Well, I know I saw the candle flickering in time to the music, but maybe my eyes were flickering, not the candle. Or something like that. Some others in the class chuckled. Or maybe I just imagined it was flickering, but it really wasn’t.

I didn’t imagine it. I saw it, insisted Bill, another student.

Well, Jack responded, how could you know whether it was real or imagined. It would seem real either way.

"I don’t know what you’re talking about, dude. Maybe you imagined it. I know what I saw," countered Bill, his eyes narrowing.

It was time for the professor to interrupt this exchange, which sounded like something straight out of the Middle Ages, when the means for settling intellectual disputes were often ugly. Let’s get back to the original question. How come the candles kept time to the music?

I think the question is wrong, ventured Jack.

Oh, said the professor. So, you think your esteemed professor has posed an ill-posed question, eh, Jack? The students laughed nervously, and Bill swung around in his chair to shake his finger at Jack.

Yes, sir_.no offense intended, said Jack

None taken. So, how should the question be posed?

Maybe... How come some of us saw the candle keep time to the music?

That’s the same as the question the professor asked, dude, said Bill.

"No. He asked why the candle kept time. I asked why we saw the candle keep time."

Psycho-babble, said Bill. If I saw it happen, it happened, grumbled Bill.

I am absolutely convinced that you saw the candle flickering, Bill, said the professor. I have no doubt that all of you have reported your observations faithfully. However, Jack’s rewording of the question may be significant. You must understand that the brain is a massive computer that intercedes itself between your sensors — your eyes and ears — and your conscious thoughts. A lot is going on beneath the surface that we are not aware of, and as the image of the candle makes its way from your eyes through your brain to your conscious thoughts, a lot can happen. A lot of processing takes place. All of us know firsthand how computers can screw up because their programs have logic bugs in them. Brains are no different.

Are you telling us that we have bugs in our brains, professor? asked Bill.

Perhaps ‘bats in your belfry’ might be a more appropriate expression, said the professor with a grin. The class, including Bill, laughed good-naturedly.

So that’s the answer? asked another student. It was all in our imagination?

No, said the professor. We don’t know that, Jane. It simply means that, as good researchers, we have to consider that possibility. We have to understand that the solution method that all of you applied was not even close to being objective, was certainly subject to your biases and not very reproducible. That’s bad science.

Well, if we were biased during this experiment, where did the bias come from? Jane asked. Were we born with it? Did it come from our upbringing? Should we blame our parents?

No, the professor laughed. "It’s much simpler in this case, Jane. You can blame me. I instilled this bias in you. Intentionally, Mea culpa"

When did you do that?

When I first described the experiment protocol last week. If you recall, I told what you were to look for. I told you to watch and see if the candle flickered in time to the music. And guess what? You saw it in your conscious mind! Like good students, you dutifully saw what your professor told you to see. That’s called a self-fulfilling prophecy.

Now, wait a minute, sir, said Bill. I wasn’t influenced by your remark. I know that objectivity is important for a good scientist.

With all due respect, Bill, by definition you cannot know for certain what goes on in your subconscious or unconscious mind. I am pleased that you consider objectivity a critical criterion for the acquisition of knowledge. But like all the rest of us, you are strongly influenced by unconscious and subconscious motivations. This is not speculation on my part. Many carefully devised experiments have confirmed the impact of unconscious biases on the behavior of humans and many other intelligent animals.

Then, how should you have described the experiment protocol to avoid biasing us with an expectation? Jane asked.

Why don’t you give it a try, Jane, suggested the professor.

Well, let’s see. Put some music on the stereo, light the candle, sit down in the chair, and so forth, and then watch the candle ... uh ... to see if... uh .... Jane’s voice faltered as she searched for the right way to phrase it. How can you word the protocol for the task so that you don’t give the goal away, but still make sure the subject knows what to do?

Tricky, isn’t it? said the professor, eyebrows arched.

If you can’t tell the subject to watch the candle, how is he supposed to know what to do? Bill mused, finally realizing that the problem was more complicated than he had first thought.

To begin with, the person watching the candle is not the subject, responded the professor. "The focus of the experiment, which is called the task unit, is the candle, not the person watching the candle and listening to the music. So, the person is the simply a smart sensor, whose function is to acquire the data necessary to decide if there is any correlation between the rhythm of the music and the flickering of the candle.

One possible solution to this problem is to use an electronic sensor and get the human out of the system entirely. The development of a vast array of mechanical and electronic sensors over the last few centuries has allowed us to eliminate much of the bias resulting from the subjective interpretations of humans. That has been one of the most important contributions to research in science and engineering since the Scientific Revolution of the 17th and 18th centuries.

What kind of sensor would you use to replace the human eyes and ears? asked one of the students.

A video camera and a microphone might work. After acquiring the data, we would have to find a way to measure the periodicity of the flickering flame in the video images and the beat of the music on the audio track. If we can do that, then computing a standard measure of association, such as the correlation, would tell us whether we can validate the research hypothesis that the flame is at least partly synchronized with the music or not.

I’m studying electrical engineering, professor, and I think measuring the periodicity of the flame’s flicker and the music’s beat would be very difficult, said

Jane. Isolating the principal periodic components of the music, the beat of the music, is not straightforward. That's pretty high-level information.

You know, when it comes right down to it, this seems to be rather difficult experiment, said Jack.

The professor nodded. The design and conduct of rigorous R&D projects in science and engineering is often very challenging.

That's what this book is all about.

1.1 The Emergence of Research

Around the turn of the first millennium, Europe began to break free from the dreary stagnation of the Dark Ages. In an effort to reestablish the wealth and stature of a weak Europe, the Roman Catholic Church and the rulers of the nations in the Holy Roman Empire financed and organized a sequence of Crusades, whose knights and armies traveled to Palestine to try to wrest control of Jerusalem away from the grip of Islam, which had gained enormous strength and power over the centuries following the death of their prophet Muhammad in 632. The crusaders were accompanied by civilian traders in search of wealth and commercial opportunities, and they brought back tales of exotic lands, cultures ... and new cultural and intellectual knowledge.

As a result, ship captains were ranging farther and farther from the coasts of Europe to transport the crusaders and traders to their distant destinations in the Mediterranean. When the mariners returned to Europe, while waiting for the next voyage, they spent their wages in the taverns of along the coast, where a growing number of them began to voice their suspicions that the Earth was not flat, but round (spherical). It was the only way they could explain how their navigational beacons --the sun, the moon, and the stars --- behaved as they did, with great regularity, but differently in the south than in the north, and in the summer than in the winter. They perceived the heavenly bodies not as angels of their god, as required by religious doctrine, but as other worlds hanging in the sky. This insight even inspired some of the braver European mariners to speculate about the possibility of circumnavigating the world as early as 1200 AD to demonstrate the validity of this revolutionary hypothesis.

However, most of these mariners were smart enough to choose their listeners carefully and keep their voices down. It was heresy to talk of such things, often under penalty of death in one of several ghastly ways. In addition, some young and daring monks in the Catholic church, who enjoyed the rare privilege of being literate and having access to the few remnants of classical Greek and Roman literature, heard the mariners’ heretical stories and began to visualize the world in a new way. It was just possible, they thought, that not everything was under the direct and immediate control of their god, but functioned more-or-less automatically according to a set of laws, which was, of course, devised and even occasionally modified by their deity. At great personal risk, they whispered among themselves about the mechanisms and phenomena of the universe.

Of course, some very conservative priests eventually got wind of this heresy and decided to crack down on it. But the disease had already spread quite far; some of the high-ranking priests themselves were already infected with this new universal perspective. It was, therefore, not politically practical to employ the normal solution to such problems by simply burning the heretics at the stake (though many were in the beginning). The old-timers also understood full well the power of martyrdom. After all, their prophet had been a martyr to their faith, and that had led to the vast and powerful empire they commanded. Much was at stake.

The priests conferred and discussed and argued and finally decided that the church must fend off the poisonous ideas entertained by these dangerously misled monks and priests, not by destroying them, but by isolating them and keeping a close eye on them. So the church stripped the conspirators of most of their clerical powers and expelled them from the monasteries, but allowed them to live and work in buildings right next to the church grounds, where they could be easily monitored while they babbled on about the universe and universal laws of nature.

Thus, as such things often happen, these buildings soon became known as universities. Eventually within such new institutions this new search for fundamental cause-effect relationships was appropriately dubbed research. The oldest university in Europe is probably the University of Bologna in Italy, which was founded at the end of the 11th century.

Over the following centuries, as knowledge accumulated in the European universities and their libraries, a trickle of practical applications began to emerge: the Gothic arch, the printing press, medicines and surgical procedures, the escapement, the cannon, handheld firearms, and so forth. Much of this kind of development was based on new knowledge that had been brought back from the Levant during the crusades. By the beginning of the 19th century, emerging industries were investing enthusiastically in the development of practical industrial applications based on the validated results and conclusions of scientific research in Europe, and the Industrial Revolution spun into full swing.

1.2 The Emergence of R&D

Today the industries and governments of the industrialized nations spend an average of about 5% of their annual budgets on research and development in science and engineering, and most major universities expect their science and engineering faculty to spend at least one-third of their time on research activities (funded, hopefully, by industry or government). Many more companies have an R&D department with its own budget, clearly separated from the production activities of the company. R&D has become the mother of product development and improvement, which is, of course, exactly what it should be.

It is very difficult to trace business problems back to possible origins in R&D departments. They are seldom blamed directly for slumping sales, but are always the vaunted heroes of the success. In today’s high-tech R&D departments, scientists and engineers enjoy a remarkable freedom from oversight and planning. On the contrary, management coddles their R&D thoroughbreds and waits with bated breath for the next killer app. It is not surprising that this encourages a lot of hype from management and sales. As a result, when R&D fails to deliver (e.g., the burgeoning development of the AI car that drives you to and from work every day), it’s the marketing people who called it wrong and get the pink slips.

The professional staff of high-tech R&D departments are often highly intelligent individuals, but have little knowledge of the exigencies of economic survival on the battlefield of business. Likewise, their arcane alchemies are little understood by managers and executives farther up the chain of command, and it is in the best interest of the R&D personnel to maintain and protect this technological mystique. Their intellectual prowess puts them in demand in the job market, and this insulates them from the strict oversight and control routinely imposed on other departments, such as marketing and sales. The fact that modern-day scientists and engineers are the memetic descendants of the priests and monks of the Middle Ages is quite evident.

Despite these strict and disciplined beginnings, today’s R&D activities are often agonizingly disorganized and free-wheeling. Top management often feels compelled to give their new young R&D engineers and scientists their head to exploit their ideas and energy in an open and environment that is free of oppressive rules and regulations. But just like young high-strung temperamental steeds aware of the luxurious prairies beyond the corral, many tend to rear up and gallop off into the night every few months to feast on greener grasses and explore the boundless world. After all, it’s a buyer’s market for these young geeks these days, and it is expected to stay that way for the foreseeable future.

Table 1.1 presents a list of some of the common problems that result from this undependable chaos, compiled by several high-level industrial executives. Some of the problems will strike you as hauntingly familiar.

Table 1.1

If you are hoping this book will help you solve some of these problems, that is exactly the intention. But there will be a price for this solution that both management and technical personnel must be willing to pay. CEOs are going to have to be willing to invest in retraining their technical staff to erase the chaotic habits of current R&D methods and become comfortable with the methodology proposed in this book. Unfortunately, however, investors despise spending money on such non-productive efforts. They will do anything to invest as little money as possible in research, let alone the amorphous task of changing the way R&D scientists and engineers go about their work. Old habits are hard to break.

The acronym R&D has become so hackneyed that many professionals have lost sight of the important differences between research and development. * So, before we go any farther, we need to establish some definitions for these two critical terms.

Definition 1.1

Research is a process that acquires knowledge.

Definition 1.2

Development is a process that applies knowledge to create new devices or effects.

Research seeks understandings development seeks utility. For this reason, industrial development engineers often consider research an effete and bookish activity, a high-risk luxury that no one can afford. On the other side of the coin, many science and engineering researchers (especially in academia) consider development a process that applies the knowledge gained by successful research by applying cut-and- dried skills that are unworthy of the research mindset.

Both attitudes are self-serving and myopic. The fact is, without research, nothing would get developed, and without development, no research could be funded. The two processes are as symbiotic as Tweedledee and Tweedledum. And often just as chaotic.

Research, whose objective is to leach new knowledge out of the matrix of the universe, is often an insular process. A few individuals can work together on a research team to accomplish its objectives using what is often called incremental innovation, but large centralized research teams are unusual and generally unworkable. The focus of a research team must be very tight, and progress is often painfully slow. Ambitious research topics, like finding a cure for cancer, require enormous investments of time and money that are seldom conducted in one lab. Instead, a number of small teams work independently in many different places, coming together now and then to share their results and conclusions through the well-established mechanisms of publication and peer review via professional journals and academic conferences.

Sometimes an effective research team can actually consist of a single scientist, working with little or no funding, occasionally joining his/her colleagues at professional gatherings to exchange knowledge. The activities of Albert Einstein, Gregor Mendel, Marie Curie, and James Watson are good examples of such largely insular research activities. James Watson, who was awarded the Nobel Prize for his discovery of the double helix of the DNA molecule and the placement of the four molecules on this complex chain in a systematic way is an excellent example of that.

In fact in 2014 Watson sold his Nobel Prize medal because he needed the money and wanted to contribute substantially to the institutions that funded his education and career. Ironically, Albert Einstein was awarded the Nobel Prize for Physics in 1921 for his discovery and explanation of the photoelectric effect, which was regarded as completely theoretical with no practical application. Einstein did not care about this. He had been successful in his endeavor. And that remained a fact until after his death in 1955. Since then, all that has changed. In 2016 investments in solar power was about 150 billion dollars in the United States, generating 74 gigawatts of electricity worldwide, in 2020 more than 10% of all electricity used in the United States comes from solar power. Research and theory have led successfully to fact and development. And a torrent of improvements in performance is expected over the coming years. This is where 'innovation becomes important in the development process.

Development, in contrast to research, is usually undertaken as a team activity that requires high efficiency, extensive funding, and careful oversight to be cost- effective (which often invites frequent inappropriate micro-management). The complete development staff, of course, may be distributed into small working groups, but constant communication and coordination among them is critical to ensure that the axle designed by one group fits the wheels designed by another group.

The enormous development project for the invasion of Normandy in June of 1944, called Operation Overlord, required the coordination of thousands of teams and hundreds of thousands of people. More than 80,000 pages of planning documents were generated for this project, the largest military battle in history.

The Apollo Project that put the first humans on the Moon was the largest peacetime development project in history at a cost of about 20 billion dollars, three lives, and 100,000 person-years of professional effort expended over 9 years. Another dramatic example of an extraordinary industrial development project was the design, construction, and testing of the Boeing 777 passenger aircraft over a three-year period in the early 1990s. At a cost of four billion dollars, this development project set new standards in engineering design and management, which were then applied to the design and construction of the International Space Station. An excellent set of five video episodes describing the management and technological challenges encountered during the Boeing 777 development project can be viewed on YouTube in five episodes. Watching them is time well spent for engineers and scientists in all fields!

R&D activities take place in many different fields, not just science and engineering. Table 1.2 gives some examples of the typical names that might be given to the corresponding roles of research and development professionals in a number of fields.

Table 1.2

Some may disagree with some of the suggested equivalencies, but the point should be clear: R&D is not limited to the hard sciences and engineering.