Recipes and Smart Service Systems with Cogs

How can a food truck become an example of a smart service system, known for creative new recipes?

Before explaining, consider recipes just a bit…

Recipes are new combinations and configurations of existing ingredients.  Some recipes are appealing, and stick around.

In his book “The Coming Prosperity: How Entrepreneurs Are Transforming The Global Economy” Phil Auerswald extends the notion of recipes in several interesting ways, including viewing entrepreneurs as people who bring new innovations to the world by seeking out new combinations and configurations of resources.

In “Technology: What It Is And How It Evolves,” Brian Arthur describes the evolution of technologies as new combinations and configurations of existing technologies, with occasional scientific discoveries expanding the list of ingredients.

Resource integrators are fundamental, and described in the Service-Dominat Logic literature of Vargo and Lusch.

In the emerging service science literature, built on the foundations of the S-D Logic worldview, service systems are defined as dynamic configurations of resources (people, technology, organizations, and information) interconnected by value propositions, for the purpose of value co-creation and capability co-elevation.  Service science is the study of the evolving ecology of nested, networked service systems entities, complex systems with capabilities, constraints, rights, and responsibilities.

Smart service systems are an emerging focus area for the National Science Foundation, and a first set of proposals are now under review.

As the era of cognitive computing and smart machines dawns, the people inside service systems will benefit from Cogs.  By boosting the productivity and creativity of people in roles in service systems, Cogs will lead to smarter service systems.

So how can a food truck become an example of a smart service system?

Nick Lavars explains it this way:

IBM has put its cognitive computing system in control of the menu at a food truck feeding attendees at this week’s SXSW festival and the appointment has resulted in some particularly imaginative dishes.

For its “Cognitive Cooking” project, IBM has enlisted the services of four prominent chefs to work with Watson, who is consulting a database of tens of thousands of recipes and ingredient combinations to conceive dishes that the regular foodie has probably never thought of.

“If you were to look inside the system that is running in the IBM cloud you would see a system that is trained on 35,000 different recipes, as if it was digesting a giant cookbook,” said Steve Abrams, Director of Watson Life at IBM. “From reading that cookbook it has learnt an awful lot about different ingredients that are often used in different cuisines, and the ingredients that are often paired together.”

So where is the world of Cogs and Smart Service Systems headed?  To get a glimpse of the answer, Ramez Naam has begun to articulate clearly some of the key issues.   The notion of “learning” that Naam begins to illuminate is a step in the right direction, especially when he remarks:

And, indeed, should Intel, or Google, or some other organization succeed in building a smarter-than-human AI, it won’t immediately be smarter than the entire set of humans and computers that built it, particularly when you consider all the contributors to the hardware it runs on, the advances in photolighography techniques and metallurgy required to get there, and so on. Those efforts have taken tens of thousands of minds, if not hundreds of thousands. The first smarter-than-human AI won’t come close to equaling them. And so, the first smarter-than-human mind won’t take over the world. But it may find itself with good job offers to join one of those organizations.

A grand challenge research problem in cognitive systems research and service science is to minimize the amount of energy, material , and time required to rebuild more and more capable systems (cognitive systems and service systems).   This could mean finding the optimal set of training data to boot-up a Cog that can assist a human in some particular job role (“how many recipes does a Cog need to read and understand before it can help a chef innovate?”).  Or it could mean finding the optimal population of Cogs to help a new Cog train up rapidly.   Or it could mean, how rapidly can one rebuild the entire societal infrastructure, and ecology of service system entities.  These are new fundamental problems in accelerated learning rates, for individuals in isolation (just data stream inputs), for individuals in a society of other entities, and for entire societal boot-ups.

In a few weeks a group of us will convene in Washington DC to develop a proposed research agenda for service science and innovation, with an eye towards how technology advances will enable smart service systems of the future.  Empowering the people in service systems to be more productive and creative with Cogs will surely be one of many topics for discussion.

In thinking about the jobs of the future and jobs in the age of smart machines, it is important to think about jobs where there is no end in sight of work to be done.   Scientists, artists, ethicists, entrepreneurs, explorers, coaches, and chefs are seven jobs that immediately spring to my mind.  Surely, there are many more.

 

 

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