If your New Year’s resolution was to stay more current with news and trends in the innovation space but you’ve been slacking off, we’ve got you covered. CIMS Innovation Management Report Editor Mike Wolff carefully curates a list of the best in innovation lit for each issue of the newsletter. Here’s an excerpt from his reviews in the January/February 2018 issue.
Troublemakers: Silicon Valley’s Coming of Age. Leslie Berlin; Simon & Schuster, New York, NY; Nov. 2017; 512 pages.
Berlin is Project Historian for the Silicon Valley Archives at Stanford University. Her book tells the story of those years when Silicon Valley “came of age” as Intel’s Robert Noyce and other semiconductor industry pioneers “passed the baton to younger up-and-comers developing innovations that would one day occupy the center of our lives.”
Berlin writes, “Between 1969 and 1976, the narrow peninsula south of San Francisco was the site of the most significant and diverse burst of technological innovation of the past 150 years. In the space of thirty-five miles and seven years, innovators developed the microprocessor, the personal computer, and recombinant DNA.” She also notes that “the years covered in this book saw the launch of five major industries: video games, personal computing, biotechnology, modern venture capital, and advanced semiconductor logic.”
While Berlin devotes considerable ink to well-known innovators such as Steve Jobs, Regis McKenna and Larry Ellison, she also profiles Mike Markkula, Apple president and major shareholder; Robert Taylor, who led the ARPAnet forerunner to the Internet; Sandra Kurtzig, the first woman to take a technology company public; Genentech co-founder Bob Swanson; Al Alcorn, the Atari engineer behind the first successful video game; Fawn Alvarez, who rose to the executive suite from a factory assembly line; and Niels Reimers, the Stanford University administrator who changed how faculty research could reach the marketplace.
Berlin also applauds the diversity of both the area and the tech industry, noting that “the constant refresh of immigrants to the valley” has sustained its recent waves of innovation. She cites as an example the fact that 37 percent of the population is foreign-born and two-thirds of the science and engineering bachelor-degree holders working in the Valley were born in another country.
As she writes, “The next great ‘Silicon Valley idea’ can pull its energy from, or export its energy to, anywhere in the world.”
A Mind at Play: How Claude Shannon Invented the Information Age. Jimmy Soni and Rob Goodman; Simon & Schuster, New York, NY, 2017; 366 pages.
In 1985, a thin, white-haired man was rushed onto the stage at an information theory symposium in Brighton, England, and introduced as “one of the greatest scientific minds of our time.” Inhibited by the thunderous applause, he did little more than take three balls from his pocket and juggle them. After recounting the incident, authors Jimmy Soni and Rob Goodman write that when the symposium chairman was asked to comment on this “performance,” he replied, “It was as if Newton had showed up at a physics conference.”
Shannon was 69 years old when he attended the Brighton symposium, 48 years after completing “A Symbolic Analysis of Relay and Switching Circuits,” which some called the most important Master’s thesis ever written, and 37 years after publishing his ground-breaking “A Mathematical Theory of Communication.”
Soni and Goodman relate how Shannon’s thesis “had shown, ‘down to the metal,’ how engineers and programmers to come might one day wire logic into a machine.” This leap, as bestselling author Walter Isaacson (Steve Jobs, The Innovators and Einstein) put it, “became the basic concept underlying all digital computers.”
Shannon’s 1948 Information Theory paper, retrospectively dubbed “The Magna Carta of the Information Age” by Scientific American, “stands as one of the defining moments in the history of information theory,” Soni and Goodman write. As the authors put it, the paper would “in less than a decade turn into a kind of international phenomenon—one that Shannon himself would, ironically and futilely, try to rein in.”
And indeed, after age 32 and the height of his fame, he turned to experimenting in artificial intelligence by building a robotic mouse, followed by a computer chess player, a wearable device for gaining advantage at roulette, and other intelligent devices.
Isaacson gives the book his seal of approval, calling it “a long overdue, insightful, and humane portrait of this eccentric and towering genius.
“Session with Authors of A Mind at Play.” Jimmy Soni and Rob Goodman; quora.com, July 17, 2017. https://wwhttps://www.quora.com/session/Claude-Shannon-mathematician/1w.quora.com/session/Claude-Shannon-mathematician/1
For those who don’t have time to read a “A Mind at Play” or are just hungry for more stories and insights about Claude Shannon and his work, authors Jimmy Soni and Rob Goodman, read this Quora post. It features detailed answers that Soni and Goodman gave while participating in an online Q&A session, addressing questions such as:
- Why is Claude Shannon so under-appreciated compared to other great thinkers of his day like Turing and Einstein?
- What were Claude Shannon’s most important contributions to math and technology?
- Why was Bell Labs (where Shannon arrived as an intern in 1937) so important to innovation and creativity in the 20th century?
- How did Claude Shannon incorporate ‘play’ into his work?
- What would Claude Shannon think of the Internet and computational technologies generally as they exist in 2017?
- To this last question, Jimmy Soni responds: “Suffice it to say, Shannon would be amazed by where the world is now—but, perhaps more importantly, he’d be keen to ask, ‘What’s next?’ And then, we imagine, he’d roll up his sleeves and figure out the answer.”
“Reshaping Business With Artificial Intelligence: Closing the Gap Between Ambition and Action.” S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves: MIT Sloan Management Review and The Boston Consulting Group, September 2017; http://sloanreview.mit.edu/AI2017
This report from an MIT Sloan Management Review research initiative in collaboration with The Boston Consulting Group aims “to present a realistic baseline that allows companies to compare their AI ambitions and efforts.”
The research is based on a global survey conducted in the spring of 2017 of more than 3,000 executives, managers and analysts across 21 industries in 112 countries and in-depth interviews with more than 30 technology experts and executives. More than two-thirds of the respondents were from outside the United States.
Data from the research revealed a large gap between ambition and execution at most companies. As the authors report, “Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85 percent believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39 percent of all companies have an AI strategy in place.”
In addition to discussing the disconnect between goals and execution, the report examines several data-related misconceptions surfaced by the research. One such misunderstanding is that sophisticated AI algorithms alone can provide valuable business solutions without sufficient data.
The report cautions that the need to train AI algorithms with appropriate data “has wide-ranging implications for the traditional make-versus-buy decision that companies typically face with new technology investments.”
However, “AI requires more than data mastery.” The report discusses the “many managerial challenges” facing companies in introducing AI into their organizations. Ensuring customer trust is one. Another is the health check:
“Unlike other digital initiatives, an AI health check involves an assessment of the skills necessary to properly execute the training of AI, from first nurturing the system to become intelligent all the way to continuing to learn after deployment. This is both new and decisive — and a capability most companies need to build themselves.”
The report concludes that most companies have no AI plan, “and those that do have one have been slower to move have some catching up to do. Those that continue to fall behind may find the playing field tilted evermore steeply against them.”
“How To Regulate Artificial Intelligence.” Oren Etzioni; The New York Times, Sept. 2, 2017, p. A19.
Oren Etzioni, the chief executive of the Allen Institute for Artificial Intelligence, proposes three rules for preventing AI harm. Inspired by the “three laws of robotics” that science writer Isaac Azimov introduced in 1942, they are: 1) A private, corporate or government AI system must be subject to the full gamut of laws that apply to its human operator; 2) Such system must clearly disclose that it is not human; 3) And such system cannot retain or disclose confidential information without explicit approval from the source of that information.
“Are Ideas Getting Harder to Find?” Nicholas Bloom et al; National Bureau of Economic Research Working Paper No. 23782; Sept. 2017; http://www.nber.org/papers/w23782.
The “robust finding” these Stanford University and MIT researchers discuss “is that research productivity is falling sharply everywhere we look. Taking the U.S. aggregate number as representative, research productivity falls in half every 13 years—ideas are getting harder and harder to find. Put differently, just to sustain constant growth in GDP per person, the U.S. must double the amount of research effort searching for new ideas every 13 years to offset the increased difficulty of finding new ideas.”
Calling Moore’s Law the best example of this finding, they also report similar rates of decline in agricultural productivity (corn, soybeans, cotton, and wheat) and medical innovations. “Averaging across firms, research productivity declines at a rate of around 10 percent per year,” the authors note.
The report addresses the conceptual framework and aggregate evidence on research productivity; the framework in the context of growth theory; applications to Moore’s Law, agricultural yields, medical technologies, and Compustat firms; and implications of the findings for the growth models economists use.
“Removing the Roadblocks to Corporate Innovation – When Theory Meets Practice”; Steve Blank; https://steveblank.com/2017/09/19/corporate-innovation-theory-versus-practice/
Asserting that “innovation theory and innovation in practice are radically different,” Steve Blank prescribes “some simple tools to get your company’s innovation pipeline through the obstacles it will encounter.” Blank, who developed the National Science Foundation Innovation Corps (I-Corps) curriculum, blogs regularly about innovation—especially the lean version—at steveblank.com.
In his Sept. 19, 2017 post, Blank relates the real-life experience he and Pete Newell had working with “Karl,” the Chief Innovation Officer of a multi-billion-dollar company he dubs Spacely Industries.
It seems that Karl had 14 innovation teams working an I-Corps/Lean LaunchPad program to validate product/market fit. One day he called Blank in frustration to report that despite a supportive CEO, “we’re 15 months into the program and the teams still run into continual roadblocks and immovable obstacles in every part of the company.” Moreover, the CEO was ready to bring in a big consulting firm to redo all of the company’s business processes.
After pondering Karl’s dilemma, they decided that, “while the company had playbooks for execution, what was missing were specific processes for innovation. We agreed that the goal was not to change any of the existing execution processes, procedures, incentives, or metrics but rather to work with all the organizations to write new ones for innovation projects.”
Blank and Newell then suggested a six-month experimental “Get to Yes” program that would begin with Karl obtaining a single-page memo from his CEO to his direct reports. Blank provides the text of the suggested memo, followed by another memo to all department heads, a subsequent “Get to Yes Request Action Form,” and a form for escalating any disagreements to departmental appeals boards.
“The big idea is that Spacely was going to create innovation by design, not by exception, and they were going to do it by co-opting the existing execution machinery.” The time for a process resolution: two weeks!
Blank concludes with lessons learned, one of the most important being this: “Explicit, top-down support for innovation only takes a single-page memo.”
“Digital Economics.” Avi Goldfarb and Catherine Tucker; National Bureau of Economic Research Working Paper 23684, August 2017; http://www.nber.org/papers/w23684
Avi Goldfarb (University of Toronto) and Catherine Tucker (MIT Sloan School of Business) examine how digital technology changes economic activity, specifically how it reduces the distinct economic costs associated with searching for information, replication of bits, transportation of information, tracking, and verification of individuals’ identities and reputations.
Their review discusses each of the cost changes associated with digitization. They emphasize the key research questions that have driven the area and how they have evolved, relating them to policy. Goldfarb and Tucker conclude with digitization’s consequences for countries, regions, firms, and individuals.
One example they cite from the literature: Adoption and usage of digital technology does enhance productivity—although only for some firms. “Various factors enhance or mitigate this relationship, including organizational change, skills, geography, regulation, firm size and age, and the potential for spillovers and/or network externalities.”