Long Term Information Technology Forecasting

Yogi Berra once said, “Predictions can be tricky, especially when you’re talking about the future.” And looking forward is certainly more perilous than using our 20-20 hindsight. However, the future of rapidly converging technologies is not so complex and uncertain that a few reasonable speculations can’t be discerned.

The truth about the biggest scientific breakthroughs is that they often come when a scientist takes a leap of imagination out of what is probable into what just might be possible. Scientists seek to understand their surrounds through three remarkable human characteristics: discovery, invention, and creativity.

Discovery is about with finding something that is already there - like finding a gold deposit. Invention is an ingenious product of a culmination of many contributing ideas, like the invention of the telephone. On the other hand, creativity is the product of a single mind like a play by Shakespeare. Actually, there is a great deal more to the scientific process, but 'seeing the big picture' requires an ability to understand the relationship between relationships.

Forecasting scientific breakthroughs requires a look into the prospects of science principles, technologies, and the economic incentives to identify areas of strategic opportunity.

Lessons can be taken from past efforts. In a recent review of a 40 year-old forecasting study, Richard E. Albright commented on the one hundred technical innovations identified as being considered very likely to be developed in the last third of the twentieth century. While fewer than 50% of the predicted innovations were considered “good and timely,” Albright found that the accuracy rate for the areas of computers and communications rose to about 80%.

Further, Albright concluded that “we should look for sustained and continuing trends in underlying technologies, where increasing capabilities enable more complex applications and declining costs drive a positive innovation loop, lowering the cost of innovation and enabling wider learning and contributions from more people, thus sustaining the technology trends.”

The growth of a new technological capability typically follows an S-shaped curve which follows three stages. First a slow initial growth allows the new technology to prove its superiority over previous technology. Once this is demonstrated, rapid growth follows. Finally, growth is limited by technological or socioeconomic competition which leads to an asymptotical leveling off.

The S-shaped curve illustrates the progress of many inventions such as electrical appliances. Many of the early analog signal processing devices developed a paradigm shift which took nearly 50 years to come to practical fruition as the adoption and utilization of independently powered analog machines followed an S-shaped curve. Today, the growth of the digital competitors is following a similar pattern.

In Connections: Patterns of Discovery the patterns of discovery are presented that produced Moore’s Law and the book explores the question, “What is the software equivalent of Moore’s Law?”

The patterns challenge the reader to think of the consequences of extrapolating trends, such as, how Moore's Law could reach machine intelligence, or retrench in the face of physical limitations.
From this perspective, the book draws the ‘big picture’ for the Information Revolution’s innovations in chips, devices, software, and networks. One goal of science is ubiquitous intelligence (UI) where everyone is connected to devices with access to Artificial Intelligence (AI) - offering what Google founder Larry Page calls ‘perfect search.’

REFERENCE:

H. Peter Alesso and Craig F. Smith, Connections: Patterns of Discovery John WIley & Sons Inc., 2008.