Tag Archive: crowd

Welcome to the Machine

“I kind of felt powerless… I do have extensive experience in terms of playing the game of Go, but there was never a case as this as such that I felt this amount of pressure.” (Lee Sedol, after playing against AlphaGo in March, 2016)

McAffee and Brynjolfsson (2017) describe phase two of the second machine age as the time “when science fiction technologies – the stuff of movies, books, and the controlled environments of elite research labs – started to appear in the real world”: winning at Go, diagnosing disease, interacting with people, engaging in creative work. The authors envision three great trends that are reshaping the business world:



including AI, boosted by:

      1. Moore’s law
      2. Cloud computing has opened relatively inexpensive computing power required to execute a machine learning project.
      3. An endless supply of data (and GPU‘s to process it). Machine learning systems need to be exposed to many examples in order to perform and improve in their tasks.
Human Mind
According to Kahneman and Egan (2011):

  • System 1: Evolutionary ancient, fast, automatic, intuitive
  • System 2: Evolutionary recent, slow, conscious, and a lot of work

“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.” [Kahneman, D. and Egan, P. (2011) Thinking, fast and slow (Vol. 1), New York, Farrar, Straus and Giroux.]

that people use to access a product or service, like Uber or AirBnB, but don’t actually produce anything or provide the service to the end customer.
Products and Services
For example, the big gains of electrification (one of the most disruptive technologies ever) came not from simple substitution of steam engines, but from the redesign of the production process itself. The process lens typically reveals many tasks that can be eliminated, or as Hammer and Champy (1993) put it, obliterated. According to Grant (2010) a firm increases attention to process innovation as it seeks to reduce costs and improve product reliability. The tendency over time for product life cycles has become compressed (p. 275).
The Crowd
e.g. GE’s FirstBuild, a “co-creation community that is changing the way products come to market”.
Organisational Capabilities
Prahalad and Hamel (1990) coined the term “core competences” to distiguish those capabilities fundamental to a firm’s strategy and performance. They also criticised U.S. companies for emphasizing product management over competence management.

Now where does all this leave us? As Haidt (2006) argues, “judgment and justification are two separate processes” of the mind. Judging, performed by System 1, happens almost instantaneously. It is then justified in rational and plausible arguments delivered by System 2:

“This finding, that people will readily fabricate reasons to explain their own behavior, is called ‘confabulation’. Confabulation is so frequent in work with split-brain patients and other people suffering brain damage that Gazzinga refers to the language centers on the left side of the brain as the interpreter module, whose job is to give a running commentary on whatever the self is doing, even though the interpreter module has no access to the real causes or motives of the self’s behavior. For example, if the word ‘walk’ is flashed to the right hemisphere, the patient might stand up and walk away. When asked why he is getting up, he might say, ‘I’m going to get a Coke’. The interpreter module is good at making up explanations, but not at knowing that it has done so.”
[Haidt, J. (2006) The happiness hypothesis: Finding modern truth in ancient wisdom, Basic Books]

At Microsoft, the acronym HiPPO (“Highest-Paid Person’s Opinion”) was created to summarise the dominant decision-making style at most companies.It illustrates the example given above of System 1 and 2 at work. HiPPOs too often destroy value. In a decades-long assessment Tetlock (1984) found that “humanity barely bests chimp” at predicting possible outcomes of politics, economics, and international affair. Today, machine learning – the science of building systems that can detect patterns and formulate winning strategies after shown many examples – is starting to accomplish interesting results. The “science fiction stuff” is just starting now…