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Most careers in the era of cognitive systems have not been invented yet

Jean Paul Jacob (IBM Research Emeritus) alerted me to this NYT article “Automation Alone Isn’t Killing Jobs” …

The author of the NYT article, Tyler Cowen, notes:

“Many other examples of automatable jobs are discussed in “The Second Machine Age,” a book by Erik Brynjolfsson and Andrew McAfee, and in my own book, “Average Is Over.” The upshot is that machines are often filling in for our smarts, not just for our brawn — and this trend is likely to grow.”

I especially agree with this point from the NYT article:

“There are unlimited human wants, so there is always more work to be done. The economic theory of comparative advantage suggests that even unskilled workers can gain from selling their services, thereby liberating the more skilled workers for more productive tasks.”

Ricardo’s Law of Comparative Advantage is very profound  – and greatly appreciated in the global service science community.  The advantages of interaction seems to be mathematically built into the fabric of universe, and certainly society.  Not just productivity (via Ricardo, etc.) but also creativity/innovation is enhanced by diverse perspectives and interaction (MIT’s Thomas Malone in his Science article on collective intelligence, and recent formulations by Pentland at MIT -  http://service-science.info/archives/3486).  This topic will surely be discussed at the National Academies Keck Center in this NSF-sponsored workshop in conjunction with the UIDPresearch priorities for service science.

I especially like the perspective in this blog post by @JosieHolford  http://t.co/zdlS8g875m — don’t ask kids what they want to be when they grow up (that is 20th Century thinking – implicit a job and profession), ask them what problems they want to work with others to solve (21st Century thinking – implicit a team and purposeful effort) – their type of career may not exist yet.

If the transition to the industrial age is any guide, most of the jobs and types of careers in the age of smart machines do not exist yet.  The era of cognitive systems is just beginning to dawn.

 

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.

 

 

Cogs, Cognitive Systems, and Service Systems

“Cogs” can be thought of as a new species of intelligent agents (“smart machines“) that can learn and communicate with us, and know a person and a person’s history very well.  Like our pets, Cogs know us; but unlike pets, Cogs process natural language and do pattern recognition as smart machines – with capabilities that are on a tremendous improvement trajectory, driven in part by Moore’s Law.  Cogs will be one of the key innovations in the era of cognitive computing, or what some have called The Second Machine Age.

The IBM Watson Group is building APIs that will be available in the IBM Cloud (SoftLayer) – and people will be able to build personal Cogs as well as professional Cogs for various applications (each job type or professional will have a GenericCog, that can be customized to know a specific person in a job role).

For example, imagine a ChefCog, it already exists, helping chefs create and prepare amazing new recipes.  If a chef has some unique style, then a ChefCog would adapt and help that person be more creative and productive.  Or like free-style chess, imagine teams of chefs and their ChefCogs all working together to advance the culinary arts.

One can also imagine DoctorCogs, MedicalEducatorCogs, CustomerServiceCogs, and SalesCogs, all helping people be more creative and productive – and like the ChefCog, early versions of all these types of Cogs already exist.

So you may have a Cog that knows you holistically, and then a separate Cog for each of your roles in life (e.g., various service systems – at home, at work, at school, at the hospital, etc.).

Technically, the set of Cognitive Systems includes entities such as people, pets, and Cogs…. but also larger entities, such as companies that know you and communicate with you and can learn about you like Facebook, Google, etc. as well as nations, states, cities that know you as a citizen and provide service offerings customized to your needs.  Any entity that stores information about you and builds up a profile that helps the entity interact with you naturally to co-create value and co-elevate capabilities can be viewed as a type of Cog.

So Cogs are somewhat like a new species, without rights or responsibilities (so not a formal service system entity yet), but definitely with capabilities and constraints.

The relationship: All service system entities are cognitive system entities, but not all cognitive system entities are service system entities.   The set of Cogs is a subset of the set of Cognitive Systems, but until Cogs have rights and responsibilities – they are disjoint from the set of Service Systems.

Social Physics: Selected Quotes

Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin.

“Most people think in relatively static terms…. I think in terms of social physics: growth processes within networks” p. ix

“…research program to develop a rigorous intellectual framework that extends current individual-centric economic and policy thinking by including social interactions. It posits social learning and social pressure as primary forces that drive the evolution of culture and govern much of the hyperconnected world.” p. ix

“But as we know all know, academic papers are, well, academic. So I’ve also helped… creating half a dozen start-up companies…” p. x

Chapter 1: From ideas to Actions

“Where do new ideas come from? How do they get put into action? How can we create social structures that are cooperative, productive, and creative?” p. 1

“Suddenly our society has become a combination of humans and technology that has powers and weaknesses different from any we have ever lived in before.” p. 2

“Adam Smith himself understood that it is our social fabric… In his book, Theory of Moral Sentiments he argued that it was human nature to exchange not only goods but also ideas, assistance, and favors our of sympathy.” p. 3

“The goal of this book is to develop a social physics that extends economic and political thinking by including not only competitive forces byt also exchanges of ideas, information, social pressure, and social status in order to fully explain human behavior.” pp. 3-4.

“Social physics is a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other.  Social physics helps us understand how ideas flow from person to person through the mechanism of social learning and how this flow of ideas ends up shaping the norms, productivity, and creative output of our companies, cities, and society.” p. 4.

“Just as the goal of traditional physics is to understand how the flow of energy translates into changes in motion, social physics seeks to understand how the flow of ideas and information translates into changes in behavior.” p. 5

“The ultimate test of a practical theory, of course, is whether or not it can be used to shape outcomes… create better companies, cities, and social institutions.” p. 7

“The engine that drives social physics is big data… by analyzing patterns of human experience and idea exchange within the digital bread crumbs we all leave behind us as we move through the world…  These data tell the story of everyday life by recording what each of us has chosen to do…. reality mining…” p. 8

“During the past decade, my students and I have developed the ability to build and deploy such living labs, measuring entire social organisms – groups, companies, and whole communities – on a second-by-second basis for up to years at a time.” p. 9

“To accomplish this I have developed legal and software tools to protect the rights and privacy of the people in the labs to insure they are fully informed about what is happening to their data and that they maintain the right to opt out at any time.” p. 9

“… enabling us to build some of the first practical ‘socioscopes.’ These new tools give a view of life in all its complexity…” p. 10

“Figure 1: Qualitative overview of social science observations and experiments, with the horizontal axis showing data collection duration (duration of observation from minutes to years) and vertical axis showing richness of the information collected (measurements per person per minute from one to hundreds).” p 11.

“Just a brief examination of Figure 1 makes it easy to see that these social physics data sets are many orders of magnitude richer than previous social science data sets.” p. 12

“In support of this book, I have placed several of the world’s largest and most detailed living lan data sets onto the Web.” p. 13

“Friends and Family: Roughly eighteen months of data from a small community of young families…” p. 13

“Social Evolution: Nine months of data from a university dormitory…” p. 13

“Reality Mining: Nine months of data from graduate students at two university laboratories…” p. 13

“Badge Data Set: One month within a white-collar workplace…” p. 14

“Data for Development…  These data are now all available from http://www.d4d.orange.com/home.” p. 14

“Idea flow within social networks, and how it can be separated into exploration (finding new ideas/strategies) and engagement (getting everyone to coordinate their behavior).” p. 15

“Social learning, which is how new ideas become habits, and how learning can be accelerated and shaped by social pressure.” p. 15

“Social physics also shares some surface resemblance to other academic domains, such as cognitive sciences…. rather than focus on individual thoughts and emotions, social physics focuses on social learning as the major driver of habits and norms.” p. 16

“The social physics that is emerging brings together branches of economics, sociology, and psychology, along with network, complexity, decision, and ecology sciences and fuses them together using big data.” p. 17

“It shows how we can begin to build a society that is better at avoiding market crashes, ethnic and religious violence, political stalemates, widespread corruption, and dangerous concentration of power.  The first steps are to being setting scientific, reliable policies for growth and innovation, and to institute information and legal architectures for the protection of privacy and public transparency…. This vision of a data-driven society implicitly assumes that the data will not be abused. … I have called this the New Deal on Data …  ” p. 17

“While these changes will help protect citizens from companies, they do little to protect against the government itself.” p. 18

“Language – engagement, exploration, idea, idea flow, information, interaction, social influence, social learning, social network incentive, social norms, social pressure, society, strategy, trust, value.”  p. 19-21

Part One – Social Physics

Chapter 2 – Exploration

“The most consistently creative and insightful people are explorers.  They spend an enormous amount of time seeking out new people and different ideas, without necessarily trying very hard to find the ‘best’ people or ‘best’ ideas.  Instead, they seek out people with ‘different’ views and ‘different’ ideas.” p. 26

“The main work of science, art, or leadership is the same: developing a compelling story about the world and then deciding to test it against reality.” p. 27

“Social Learning – Harvard Business Review article ‘Beyond the Echo Chamber.’” p. 29.

“What Kelly found was that star producers engage in ‘preparatory exploration’; that is, they develop dependable two-way streets to experts ahead of time…” p. 35

“Second, start performers’ networks were also more diverse.” p. 35

“What we found was that individuals who adopted an energetic, engaging interaction style that created more interactive conversations ended up being more important to idea flow in social networks.” p. 36

“This echo chamber overconfidence effect is a source of fads and financial bubbles.” p. 37

“For example, what can be done when the flow of ideas becomes either too sparse and slow or too dense and fast? … As a result of this tuning we were able to increase the profitability of all the social traders by 6 percent, this doubling their profitability.” p. 38

“…created a spin-off company called Athena Wisdom that is now tuning financial and decision-making networks around the world.” p. 39

“Diversity is important… Contrarians are important…” p. 49

“In summary, people act like idea-processing machiens combining individual thinking and social learning from the experiences of others.  Success depends greatly on the quality of your exploration and that, in turn, relies on the diversity and independence of your information and idea sources.” p. 41.

“Utilizing these questions, we can reliably predict what individuals will choose to do and how good their outcomes will be in situations ranging from companies (Part II of this book), to cities (Part III), to entire countries (Part IV).” p 42

Chapter 3 – Idea Flow

“…it is the rates of idea flow – or the barriers to idea flow – that we must understand if we are to work well together.” p. 44

“Idea flow is the spreading of ideas, whether by example or story, through social networks – be it a company, family, or a city…  It facilitates the transfer of habits and customs from person to person and from generation to generation” p. 44

“… some psychologists refer to us as Homo imitans.” p. 45

“By harvesting from the parts of our social networks that touch other streams, that is, by crossing what sociologist Rob Burt called the ‘structural hole’ within the fabric of society, we can create innovations.” p. 45

“The bottom line: In these three example – health habits, political preferences, and consumer consumption – exposure to the behavior of peers, both direct and indirect, predicted idea flow.” p. 45

“Perhaps this is because learning from surrounding example behaviors is much more efficient than learning solely from our own experiences.  Mathematical models of learning in complex environments suggest that the best strategy for learning is to spend 90 percent of our efforts on exploration, i.e., finding and copying others who appear to be doing well.  The remaining 10 percent should be spent on individual experimentation and thinking things through.” p. 54

“Figure 4 (adapted from Kahneman’s Nobel Prize lecture): Humans have two ways of thinking: an older capability based on associations and experience (“fast”) and a new capability based on attentive, rule-based thinking (“slow”).” p. 56

“Psychological studies have shown that the snap judgments of people are more altruistic and cooperative than the decisions made slowly and thoughtfully.” p. 57

“As Nobel Laureate Herb Simon put it, our rational, conscious thinking is the program that invokes the habits of action that take care of all the details of daily life, just as computer programs have subroutines that handle frequently used computations.” p. 58

“Learning and reinforcing the social contract is what enables a group of people to coordinate their actions effectively.” p. 59

Chapter 4 – Engagement

“The ability to work together, though, goes beyond simple idea flow within a community; it also includes striking a bargain between individuals to adopt behaviors that are synchronized and compatible.” p. 63

“Some evolutionary theorists think that this type of ‘social voting’ process could be the most common type of decision making in social animals, in part because it is very good at accounting for the cost-benefit trade-offs of everyone in the group.” p. 63

“Average performers thought teamwork meant doing their part on the team.   Star performers, however, saw things differently: They pushed everyone on the team toward joint ownership of goal setting, group commitments, work activities, schedules, and group accomplishments.” p. 63

“Similarly, business research has shown that this sort of engagement – repeated cooperative interactions among all members of the team – can improve the social welfare of the group, and promotes the trustworthy cooperative behavior conducive for successful business relationships.” p. 64

“What our grandmothers would have known, though, was that nearly all the social influence occurred between close friends who had a face-to-face relationship.” p. 65

“The Facebook voting example suggests that information by itself is a rather weak motivator… that seeing members of our peer groups adopting a new idea provides a very strong motivation to join in and cooperate with others.” p. 65

“But social physics tells us that there is another way: by providing incentives aimed at people’s social networks rather than economic incentives or information packets that are aimed at changing the behavior of individuals.” p. 66

“On average it turned out that the social network incentive scheme worked almost four times more efficiently than a traditional individual-incentive market approach… The number of direct interactions that people had with their buddies was an excellent predictor of how much their behavior would change.  Similarly, the number of times people had direct interactions with each other gave a surprisingly accurate prediction of the trust they expressed in each other.” pp. 68-69

“The social physics approach to getting everyone to cooperate is to use social network incentives rather than to use individual market incentives or to provide additional information… Engagement – repeated cooperative interactions among members of the community – brings movement toward cooperative behavior.” p. 69

“This social network incentive caused electricity consumption to drop 17 percent, twice the best result seen in earlier energy conservation campaigns and more than four times more effective than the typical energy reduction campaign.” p. 72

“… examined the growth and performance of more than one thousand companies’ internal digital social networks.” p. 72

“In other words, engagement build culture.” p. 74

“Social physics tells us that we must include not only economic exchanges, but also exchanges of information, ideas, and the creation of social norms in order to fully explain human behavior.” p. 75

“If the majority of interactions were instead exploitative, then each interaction would serve to destroy trust.” p. 76

“Engagement requires interaction.  Engagement requires cooperation.  Building trust.” p. 77-78

“…idea flows, i.e., the spreading of new behaviors through a social network, may be conceptualized as exploration to harvest new ideas followed by engagement with peers to sift through those ideas and convert the good ideas into habits.” p. 79

“The Mathematics of Social Influence.” pp. 80-84

Part Two – Idea Machines

Chapter 5 – Collective Intelligence

“Groups of people, as well as communities, also have a collective intelligence that is different from the individual intelligence iof each group member.  Moreover, this group intelligence is about as important a factor in predicting group performance as IQ is in predicting individual performance.” p. 87

“The largest factor in predicting group intelligence was the equality of conversational turn taking; groups where a few people dominated the conversation were less collectively intelligent than those those with a more equal distribution of conversational turn taking.  The second most important factor was social intelligence… Women tend to do better at social signals…” p. 88

“What these sociometric data showed was that the patten of idea flow by itself was more important to group performance than all other factors… ” p. 89

“The characteristics typical of the highest performing groups included: 1) a large number of ideas… 2) dense interaction… 3) diversity of ideas… ” p. 89

“Figure 6: (a) an unproductive pattern of interaction, (b) a good pattern of interaction.” p. 89

“One exception to using these patterns of interaction as a guide is performance in times of stress… A second exception is when… emotions are high… ” p. 90

“The sociometric data from these small working groups highlight that teams are operating as idea-processing machines in which the pattern of idea flow is the driving factor in performance.” p. 90

“…spin-off company, Sociometric Solutions… ” p. 92

“Harvard Business Review article ‘The New Science of Building Great Teams’ …” p. 93

“As a result of this simple change, the call center management converted the break structures of all their call centers to this new system and forecast a $15 million per year productivity increase.” p. 95

“Our sociometric badges were deployed in this Chicago-area data-serve sales firm for a period of one month… collecting roughly a billion measurements about who talked to whom, their body language, and even their tone of voice… http://realitycommons.media.mit.edu)” p. 95

“Remember that engagement is defined as idea flow within a work group…” p. 96

“The solution suggested by other social species, such as ape troops and bee colonies, is to alternate between exploration for idea discovery and engagement with others for behavior change.” p. 97

“Figure 7: Exploration and engagement networks. (a) Exploration is when team members interact with other teams. (b) Engagement is when they interact with each other.” p. 98

“Qualitatively, this is what the Bell Stars study of Chapter 2 and 3 found: Star performers became familiar with different perspectives on their work.  Senior management, customers, sales, and manufacturing groups all have different views, and the combination of their ideas with those already in their work group were a major source of useful creative thinking.” p. 99

“In fact, a simple combination of the engagement and exploration measures was able to predict which days were the most creative with 87.5 percent accuracy.” p. 102

“To use Herb Simon’s phrasing, if there is a consensus about an idea, it is then integrated into the team’s store of ‘action habits’ to use for their fast thinking.” p. 102

“Because fast thinking uses associations rather than logic, it can make intuitive leaps more easily by finding creative analogies.” p. 103

Chapter 6 – Shaping Organizations

“This makes the pattern of idea flow the single biggest peformance factor that can be shaped by leadership, and yet today there isn’t a single organization in the world that keeps track of both face-to-face and electronic interaction patterns.  And, as we all know, what isn’t measured can’t be managed.” p. 106

[[the first unwritten law of service science is "whatever is measured can be gamed or corrupted, and will surely lose its value over time, (requiring new dimensions to be created and put into quasi-balance with existing dimensions for the ecology to remain viable and growing)." ]]

“The goal is to increase the social intelligence of both work groups and the entire organization, and so increase their performance.” pp. 106-107

“When we instrument a typical organization in order to visualize interaction patterns, both managers and employees wear our specially designed sociometric badges (see the Reality Mining appendix for more detail).” p. 107

“The most useful visualizations convey the levels of engagement and exploration within the organization…:  We have found that engagement levels predict up to half the variation in group productivity, independent of content, personality, or other factors.   Exploration is how much the members of a group of a group bring in new ideas from the outside; that in turn predicts both innovation and creative output.” p. 107

“Good idea flow is difficult in some kinds of groups, for example, in both widely dispersed and mix-language groups.” p. 108

[[[one reason IBM is so amazing]]]

“Figure 9: The Meeting Mediator system consists of (a) a sociometric badge (left) to record the interaction patterns of groups, and a mobile phone (right) to display them as real-time feeback.”  p. 109

“While the mathematical measure of idea flow between a work group and people outside it is probably the best way to measure exploration, we have found that it is usually adequate to simply count the number of outside interactions.” p. 113

“…came up with what he calls Bayesian truth serum, which is a way of figuring our who has genuinely new bits of information that might make a difference. One might also call this the wise guys solution to the problem of insufficient diversity in idea flow.  In the wise guy method, we look for individuals who can accurately predict how other people will act but whose own behavior is different.  The logic is that if a person can predict othr people’s actions, then they already know the common knowledge. But if their behavior is also different from everyone else’s, then they must know something the others don’t.   The behavior of such wise guys, then, can be counted as an independent bit of information.” p. 115

“In practical applications, I have found that this third method, estimating the amount of social influence between people, is the easiest and works quite well.” p. 116

“The charismatic connectors are not just extroverts of life of the party types.  Rather, they are genuinely interested in everyone and everything… They tend to drive the conversations, asking about what is happening in people’s lives, how their projects are doing, how they are addressing a problem, etc.  …People can teach themselves to be charismatic connectors – they are made, not born.” pp. 117-118

Chapter 7 – Organizational Change

“Because the social sciences, including economics, have had to work with such impoverished data, it has been difficult for scientists to understand the process of change.” p. 120

“Now let us examine the Red Balloon Challenge, a case in which my research team and I were able to use social network incentives to build a worldwide organization and accmplish a difficulty task in only a few hours, beating hundreds of competing teams to win the prize money.  The strategy we took to accomplish this feat was so novel and effective  that our approach was published in the journal Science and later expanded upon in the Proceeedings of the National Academy of Science.” p. 121

“As a result of using this social network incentive strategy, our research team correctly identified the location of all ten balloons in just 8 hours, 52 minutes, and 41 seconds.” p. 124

“Nevertheless, during the last century this sort of hierarchical crowdsourcing has been exactly the model of most corporations.  Workers sit in cubicles doing independent tasks, adn then their outputs are routed to anonymous others for the next stage of processing. ” p. 126

“This connection between engagement, trust, and people’s ability to act cooperatively is perhaps the main point of Robert Putnam’s classic book Bowling Alone, which highlights the relationship between civic engagement and health of society.  We are trading in ideas, good, favors, and information and not simply the competitors that traditional market thinking would make us.  In each area of our lives, we develop a network of trusted relationships and favor those ties over others.” p. 130

“Understanding ourselves this way could have dramatic effect on the character of our society.  Because idea flow creates culture, supports productivity, and enables creativity we should place greater value on professions that enhance idea flow: teachers, nurses, ministers, and policemen, along with doctors and lawyers who work for charities, as public defenders, or for inner city hospitals.” p. 130

“My goal is to imagine what a data-driven city might look like and how we can use big data and social physics to create more productive and creative cities.  And then in the last section, I will discuss what changes need to be made to privacy, management, and government in order to create a brighter, safer future.” p. 131

“Each of these signals has roots in the biology of our nervous system.  Mimicry is believed to be related to cortical mirror neurons, part of a distributed brain structure that seems to be unique to primates and is especially prominent in humans.  For example, mirror neurons react to other people’s actions and provide a direct feedback channel between people.  One result of this is the surprising ability of human newborns to mimic their parents’ facial movements despite their general lack of coordination… Indeed, these signaling patterns are so clear that they are now used commercially to screen for mental health conditions such as depression and to monitor patient engagement during treatment.  For more details see http://cognitocorp, an MIT spin-off company that I cofounded.” p. 134

Part Three – Data-Driven Cities
 
Chapter 8 – Sensing Cities

“But these century-old solutions are increasingly obsolete.  We have cities jammed with traffic, worldwide outbreaks of diseases that are seemingly unstoppable, and political institutions that are deadlocked and unable to act.  In addition, we face the challenges of global warming, uncertain energy, water, and food supplies, and a rising population that will require building one thousand new cities of a million people each in order to stay even.” p. 137-138

“Rather than static systems that are separated by function – water, food, waste, transport, education, energy, and so on – we must consider them as dynamic and holistic systems.  We need networked, self-regulating systems that are driven by need and preferences of the citizens instead of ones focused only on access and distribution.” p. 138

“Right now, the most important generator of city data is a familiar tool: the ubiquitous mobile phone. These devices are, in effect, personal sensing devices that are becoming more powerful and sophisticated with each product iteration. In addition to deriving information on user locations and call patterns, we can map social networks, and even gauge people’s moods by analyzing the digital chatter that has become so pervasive. ” p. 138-139

“Networks will become faster, devices will have more sensors, and techniques for modeling human behavior will become more accurate and detailed.” p. 139

“Many of the sensing and control elements required to build a digital nervous system are already in place.  What is missing, though, are two critical items: The first is social physics, specifically dynamic models of demand and reaction that will make the system function correctly, and the second is the New Deal on Data, an architecture and legal policy that guarantees privacy, stability, and efficient government.” p. 139

“The proliferation of mobile phones makes it possible to leap beyond demographics to directly measure human behavior.” p. 141.

“These data, created by an MIT spin-off company, Sense Networks (which I co-founded), allow us to analyze movement and purchasing behaviors of tens of millions of people in real time.” p. 141

“… the process of social learning and the development of social norms within cities is driven by the observation of peer behavior, that is, by people trying to fit in with their chosen peer groups.” p. 142

“For most people, the primary pattern is the workday, that is, going to work and coming home, usually along the same path day after day.  The second most pronounced pattern is the weekend and days off, often with the characteristic behavior of sleeping in and spending that night out in a location besides the home or work.  Perhaps surprisingly, the places we go and things we do during our free time are almost as regular as our work patterns.  The third pattern, however is a wild card: days spend exploring, usually a shopping trip or an outing.  This last is distinguished by it lack of structure. Together these three patterns typically account for 90 percent of most of out behavior.” p 142-143

“As we will see in the next few sections, these data-driven forecast allow us to prepare for peaks in demand and manage them better.  It also means that we can react better to emergencies or disasters, because we can know who is likely to be where and when.” p. 143

[Why does the Atlanta snow storm (late Jan or early Feb 2014) come to mind?]

“A simple example consists of basically crowdsourcing dangerous conditions. If other cars have just recently gone down the road you are driving on and had emergency breaking events, then you are at significant risk of an accident.  If you are traveling faster than other cars were, then you are in real danger.  Warnings based on this sort of big data could be used to reduce accidents rats dramatically.” p. 144

[New features in cars, even before driverless cars, are intelligent following behavior in traffic jams. This is significant. Productivity boost from freeing up medical, emergency, police, repair shop, insurance company, etc. resources from needless accidents.  An what a disruption to families - accidents need to be avoided.]

“Perhaps the most interesting idea is to use transportation networks to increase the productivity and creativity of cities.  We can use data about people’s habits to structure public transportation networks to increase the productivity and creativity of cities: We can use data about people’s habits to structure transportation to promote more exploration within cities.” p. 145

[Bill Gates once told a group of us at the San Jose Tech Museum that if we could use technology to make it seem like there was suffering next door to us in our neighborhoods, especially the wealthiest and most segregated of us "so-called elite folks,"  it would cause the most dramatic drop in human suffering on the planet in human history, because people are basically good and have empathy for the unnecessary suffering of others.  I recall thinking - "I like this guy Bill Gates" - when he said that.  He also mentioned he was going slightly schizophrenic between "make-lots-of-profit Bill Gates" and "give-away-lots-of-money Bill Gates"- got a good laugh from the crowd, then he added that his wife was helping him remain somewhat sane through the transition.  Shortly afterBill Gates made these comments at San Jose Tech Museum, Warren Buffet aligned his charitable efforts with the Gates Foundation. Nice, there is a lot of good in the universe...  But helping people who behave irrationally, and making peace with that, is explored in the eccentric yet powerful writings of ... Anthony Galambos 'Unto the stars (sit et astra)' - unfortunately I don't think the Tea Party has read this work of a paranoid of idea-theft, philosophically-minded aerospace engineer of the 1960's, though if their leadership did, it might provide some foundation for their hopes for society.  Perhaps Gene Roddenbury read it though.  Oh well, who really knows what influences what in history... not me that's for sure, mere speculation.  I probably read too much any way.  I doubt anyone will read these remarks, so I am safe.]”

“With reported sore-throat and cough symptoms we found that people’s normal patterns of socialization were disrupted, and they began to interact with more and different people (good for the virus, bad for humans [especially their productivity and creativity]).” p. 146

“This idea is underpinning another of my group’s spin-off companies, Ginger.io, that I helped co-found…” p. 147

“Moreover, using financial incentives privileges the rich.  As an example, consider congestion pricing… This is particularly worrisome because exploration results in innovation… There are three types of interventions that are naturally suggested by the social physics perspective. …Social Mobilization: As used in the Red Balloon Challenge… Tuning the Social Network: …To solve the problem of both insufficient diversity and echo chambers… Leveraging social engagement: … Facebook ‘get out the vote’ campaign in 2010 targeted 61 million people…  “  p. 150-152

“The main barriers to achieving these goals are privacy concerns and the fact that we don’t yet have any consensus around the trade-offs between personal and social values.” p. 153

Chapter 9 – City Science

“Urban areas use resources more efficiently and produce more patents and inventions with fewer roads and service per capita than rural areas.” p. 155-156

“Cities are idea machines in the same way that companies are idea machines.” p. 156

“The difference, however, is that social physics conceptualizes cities and companies as idea factories, so the focus is on the flow of ideas rather than the flow of goods.” p. 157

[Yep, Service-Dominant Logic by Vargo and Lusch - service science and social physics are cousins.]

“As the remainder of this chapter will explain, what really matter is the flow of ideas, and not classes or markets.” p. 157

“That is, when we look at all of out interactions we see that people have many social roles (e.g., mother, coworker, citizen, jazz enthusiast, etc.) and each role engages a different set of people, so that the functions of engagement and exploration are combined across all of a person’s social networks.” p. 160

“Figure 16. A typical shopping pattern, with the size of each circle indicating the frequency of places visited…” p. 161

“This suggests that when people have abundant resources, it is their curiosity and social motivations that drive their exploratory behavior and not the desire to find cheaper prices or better product.” p. 164

“That is, they used their extra money to increase their exploration.” p. 164

“In fact, the relationship between the amount of disposable income and amount of exploration is very predictable.” p. 164

“Figure 17: The model of idea flow along social ties accurately predicts GDP per square mile.” p. 166

“Because of the dependence of idea flow on transportation efficiency, the idea flow equations can be turned around and GDP can be used to calculate the average commuting distance.” p. 166-167

[Amos Hawley's "Human Ecology" work comes to mind.]

“Designing Better Cities:  Traditional theories of city growth emphasize markets and classes, suggesting that specialization in industry or new categories of highly trained workers as generative models of city development.  In contrast, the social physics approach provides a plausible and empirically grounded model that does not require the presence of these social structures.  Instead, it relies only on  the fine-grain characteristics of human social interaction: the distribution of social ties, the flow of ideas along those ties, and the means by which those ideas are converted into new behaviors and new social norms by engagement with peer groups.” p. 167

“The failure of most city zoning is that if cities segregate by function, then exactly the wrong change in the structure of social ties occurs: Engagement decreases locally… What we want is the opposite: self-contained towns in which people meet each other regularly and there are many friends of friends.  As famous urban advocate Jane Jacons argued, a healthy city has complete, connected neighborhoods.” p. 168

“The best size for a city can even be calculated: If within each peer group everyone is a friend of a friend, then the math of social physics indicates that we get maximum engagement for populations of up to roughly one hundred thousand people.   This suggests that the best solution is small-to-medium-sized towns in which everyone is within walking distance of town center, the stores, the schools, the clinics.” p. 168

“This is the approach planners in Detroit are trying, by working to create a tiny hot new city inside the decaying sprawl of the original one.” p. 170

“There is no need to appeal to assumptions about social hierarchies, specialization, or other special social constructs in order to explain how GDP, research and development, and crime grow with increasing city population.” p. 170

“… we have seen that today’s digital technology is not as good at spreading new ideas as are face-to-face interactions.” p. 171

“The recommendations about city structures that come from social physics are similar to those of famous urban advocate Jane Jacobs, but what social physics has added is a quantitative, mathematical basis for recommendations.  By understanding cites as idea engines, we can use the equations of social physics to being to tune them for better performance.” p. 172

“Digital Networks Versus Face-To-Face” p. 172

PART FOUR: Data-Driven Society

Chapter 10 – Data-Driven Society

“We have seen that the digital bread crumbs we leave behind provide clues about who we are and what we want.  That makes these personal data immensely valuable, bot for public good, and for private companies.   As European consumer commissioner Meglena Kuneva said recently, ‘Personal data is the new oild of the Internet and the new currency of the digital world.’ This new ability to see the details of every interaction, however, can be used for good or for ill.” p. 177

“A successful data-driven society must be able to guarantee that our data will not be abused – and perhaps especially that government will not abuse the power conferred by acces to such fine-grained data. To achieve the positive possibilities of a data-driven society we require what I have called the New Deal on Data – workable guarantees that the data needed for public goods are readily available while at the same time protecting the citizenry.” p. 178

“These data must not remain the exclusive domain of private companies, because then they are less likely to contribute to the common good.  This, these privagte organizations must be key players in the the New Deal on Data’s framework for privacy and data control.  Likewise, these data should not become the exclusive domain of the government, because this will not serve the public interest of transparency, and we should be suspicious of trusting the government with such power.” p. 179

“… I will discuss what may be the world’s first large-scale digital commons, and explain how a resource such as this can be used to help build a better society.” p. 179

“We need to recognize personal data as a valuable asset of the individual that is given to companies and government in return for service.” p. 180

“In 2007, I first proposed the New Deal on Data to the World Economic Forum.  Since then, this idea has been run through various discussions and eventually helped shape the 2012 Consumer Data Bill of Rights in the United States, along with a matching declaration on Personal Data Protection in the EU.” p. 181

“A system like this has made the interbank money transfer system among the safest systems in the world, but until recently such technology was only for the big guys. … the Institute for Data Driven Design (co-founded by John Clippinger and myself) have helped build openPDS (open Personal Data Store), a consumer version of this type of system, and we are now testing it with a variety of industry and government partners.” p. 182

“… Data Liberation Front (www.dataliberation.org), a group of Google engineers who mission statement says that ‘users should be able to control the data they store in any of Google’s products’ and whose goal is to ‘make it easier to move data in and out.’” p. 184

“Until we have a solid, well-proven, and quantitative theory of social physics, we won’t be able to formulate and test hypotheses in the simple, clear-cut manner that today allows us to reliably design bridges or test new drugs… We need to construct living laboratories…  …”open data city” I have just help launch within the city of Trento in Italy… More details on this living lab can be found at http://www.mobileterrioriallab.eu.” p. 186-187

“Some people react negatively to the phrase social physics, because they feel it implies that people are machines with free will and without the ability to move independently of our role in society.” p. 189

“The fact that most of our attitudes and thoughts are based on integrating experiences of others is the very basis for both culture and society.  It is why we can cooperate and work together toward common goals.” p. 191

“To accomplish this change we need a language and logic that everyone can understand and that has proven to be more useful thank the old language of markets and classes.  I believe the language of social physics – exploration, engagement, social learning, and measurement of idea flows – has the potential to serve this role.” p. 192

Chapter 11 – Design for Harmony

“Competition versus cooperation.  … In fact, the main source of competition in society may not be among individuals but rather among cooperating groups of peers.” p. 194-195

“Figure 18: (a) a classical market, (b) an exchange network.  An exchange network is a market where trade options are limited to connections within the social network.  Trust and personalized service is much more likely to develop with an exchange network.” p. 197

“Natural Law: Exchanges, Not Markets – Modern society is based on the idea that markets can distribute resources efficiently and on the assumption that humans are relentless competitors.  But as we have seen, this is simply not a good description of how our society lives and functions.” p. 199

“In other words, many early societies operated much more like an exchange network than a market.” p. 200

“The central reason that exchange networks are better than markets is trust.” p. 200

“In markets, one must usually rely on having access to an accurate reputation mechanism that rates all the participants, or to an outside referee to enforce the rules.” p. 201

“Because we are not just economic creatures, our models must include a broader range of human motivations, such as curiosity, trust, and social pressure.” p. 203

“I believe there are three design criteria for our emerging hypernetworked societies: social efficiency, operational efficiency, and resilience.” p. 203

“Such examples give hope that we can build human-machine systems that very quickly configure both economic and social incentives to assemble entire systems, products, and services on the fly.  We need to think more broadly, however, than simply how to rebuild damaged systems.  We also need to think about the resilience of the entire social design.” p. 210

“Consequently, to survive systemic risks we need to have a diverse set of systems rather than one so-called best system.” p. 210

“These results and others like them are available at http://www.d4d.orange.com/home.” p. 214

“All around the world governments and universities are beginning to take a look at how cities are organized and governed, motivated by rapid increase in city populations and the number of new cities that are being created.” p. 215

“… as codirector of MIT Media Lab’s City Science initiative (see http://cities.media.mit.edu) I am now working with a variety of cities to improve idea flow.” p. 215

Appendix 1 – Reality Mining

“In recent years, the social sciences have been undergoing a digital revolution, heralded by the emerging field of computational social science.  In our 2009 Science paper, David Lazer and I, together with more than a dozen endorsing colleagues, describe the potential of computational social science to increase our knowledge of individuals, groups, and societies by use of data with an unprecedented breadth, depth, and scale.” p. 217

“Figure 19: A standard design for a sociometric badge, courtesy of Sociometric Solutions, Inc.” p. 220

“Social media activity, credit card activity, and other sorts of individual information can also be recorded. It is available for Android mobile phones at httP://www.funf.org.” p. 224

Appendix 2 – OpendPDS

“Personal data – digital information about users’ locations, calls, Web searchers, and preferences – have been called the oil of the new economy and what I have seen reinforces this comparison.” p. 225

“Owning a personal data store (PDS) would allow the user to view and understand how the data collected might be used, as well as to control the flow of data and to manage fine-grained data access.” p. 227

Appendix 3 – Fast, Slow, and Free Will

Psychologist Daniel Kahneman and artficial intelligence pioneer Herb Simon, both Nobel Prize winners, each embraced a model of a human with two ways of thinking.  In Kahneman’s formulation, one way of thinking is fast, automatic, and largely unconscious mode, and the second way of thinking is a slow, rule-based, and largely conscious mode.  A thumb-nail sketch of gfast thinking is that it drives habits and intuitions, largely by using associations among personal experiences and experiences learned by observing others.  In contrast, the slow mode of thinking uses reasoning, combining beliefs in order to reach new conclusions.” p. 235

“The best capsule summary is that habits and gut instinct are based on fast thinking which uses engagement with others to integrate their experiences with our own, and thus form our habits of action.  Exploration and guiding our attention to help figure things out seem to be the core functions of slow thinking, which is supported by observation of events, context, and correlation that are learned both personal perception and language.  Understanding that humans have two ways of thinking that work quite differently transform many of the classic disputes in philosophy, anthropology, and sociology. … emphasize how the structure of society shapes the behavior of the individual…  …emphasize free will and how individual cognitive processes shape individual behavior. …it tells us that both sides of the free will versus social context debate are right, but neither is right about all human behavior all of the time.” p. 239

Appendix 4 – Math

The concept of influence is extraordinarily important in the natural sciences.  The basic idea of influence is that an outcome in one entity can cause an outcome in another.   Flip over the first domino, and the second one will fall.  If we understand exactly how two dominos interact – how one domino influences another – and we know the initial state of the dominos and how they are situated relative to one another, then we can predict the outcome of the whole system.” p. 241-242

“An entity’s state is affected by its network neighbors’ states and changes accordingly.  Each entity in the network has specifically defined strength of influence over every other entity and equivalently, each relationship can be weighted according to their strength.” p. 243

“The state of each entity is not directly observable.   As in a hidden Markov model (HHM), however, each entity emits a signal… ” p. 245

“The number of parameters grows quadratically with respect to the number of entities C and the latent space size S.  This largely relieves the requirement for large training sets and reduces the changes of model overfitting, making the influence model scalable to larger social systems.” p. 247

fyi… A very mechanistic view that I have seen before in Gilberts work “Human Competence: Engineering Worthy Performance”
http://www.amazon.com/Human-Competence-Engineering-Worthy-Performance/dp/0787996157

However, big data makes it fresh…  more data makes it quite interesting… still mechanistic, but interesting…

-Jim

Dr. James (“Jim”) C. Spohrer
Director, IBM University Programs (IBM UP) and Cognitive Systems Institute
IBM Research – Almaden, 650 Harry Road, San Jose, CA 95120 USA
spohrer@us.ibm.com 408-927-1928 (o) 408-829-3112 (c)
Innovation Champion (http://www.service-science.info/archives/2233)

CFP: HICSS 48 Track “Decision Analytics, Mobile Services & Service Science”

Dear Colleagues,

Hello! I am serving as a co-chair of the “Decision Analytics, Mobile Services and Service Science” track of the upcoming 48th Hawaii International Conference on System Sciences (HICSS) (http://www.hicss.hawaii.edu/hicss_48/apahome48.htm). My co-chair, Christer Carlsson, and I are writing to internationally renowned scholars such as you with expertise in various areas of analytics, mobile systems and service science in hopes that you will consider submitting a paper to our track. We have been receiving a great deal of interest, and your contributions would help us improve the quality of the sessions. The deadline for submitting papers to HICSS-48 is June 15, 2014 (less than 4 months). Please consider submitting your work if it is related to any of the specific topics listed and/or if you feel it addresses visions of the future of this track. We expect a range of concepts, tools, methods, philosophies and theories to be discussed. We thank you, in advance, for your valuable contribution to HICSS-48. Please let us know if you have any questions or need additional information. We look forward to receiving your submission!

Best Regards,
Haluk Demirkan – haluk@uw.edu
Christer Carlsson – christer.carlsson@abo.fi

HICSS-48 CALL FOR PAPERS

January 5-8, 2015 – Grand Hyatt, Kauai

Additional detail may be found on HICSS primary web site:
http://www.hicss.hawaii.edu/hicss_48/apahome48.htm

The Decision Analytics, Mobile Services and Service Science Track

(http://www.hicss.hawaii.edu/HICSS_48/Tracks/DecisionAnalytics.htm) focuses on emerging managerial and organizational decision-making and innovation strategies, processes, tools, technologies, services and solutions in the Digital Age. This track has four interrelated themes. Analytics focuses on decision making processes, models, tools and technologies. Mobile Services work with the development and delivery of data, information and services with mobile technology platforms. Challenges and issues of emerging service systems, and service-orientation and -transformation of strategies, pro-cesses, organizations, systems and technologies are covered in Service Science. In this track, we also discuss innovative approaches of decision making for/with Critical and Emerging Solutions in a number of high-impact areas.

This track includes the following 17 mini-tracks:

1.            Big Data Analytics: Concepts, Methods, Techniques & Applications
2.            Data, Text & Web Mining for Business Analytics
3.            Decision Making in Production Processes
4.            Decision Support For Sustainability
5.            ICT Enabled Services
6.            Intelligent Decision Support for Logistics & Supply Chain Management
7.            Interactive Visual Decision Analytics
8.            Mobile Value Services
9.            Multi-Criteria Decision Analysis & Support Systems
10.          Network Decision Support Systems
11.          New Economic Models of the Digital Economy
12.          Open Data Services
13.          Service Analytics
14.          Service Science
15.          Smart Service Systems: Analytics, Cognition & Innovation
16.          Soft Computing
17.          The Internet Of Things & Big Data Analytics

IMPORTANT DEADLINES
- June 15 – Submit full manuscripts for review. The review is double-blind; therefore this submission must be without author names.
- Receive acceptance notification by August 15.
- Revise your manuscript to add author names. If required, make other changes.
- Submit Final Paper for Publication by September 15.

Haluk Demirkan (haluk@uw.edu)
- Associate Professor of Service Science, Information Systems & Supply Chain Management, Milgard School of Business, University of Washington – Tacoma
- Founder & Executive Director of Center for Information Based Management Focused on Analytics & Service Innovation
- Co-Founder & Board of Director, International Society of Service Innovation Professionals (www.issip.org)
- Track Chair for Analytics, Mobile & Service Science at HICSS (www.hicss.hawaii.edu/)

Cognitive Systems Institute

The Cognitive Systems Institute is being established to explore answers to two questions with both technology and pubic policy dimensions:

How can cognitive systems be used to improve the productivity and creativity of:

(1) individual researchers and their teams?
(2) research universities and their regions?

More specifically, the Cognitive Systems Institute works with cognitive systems researchers (1) to understand and improve their productivity and creativity, and (2) thereby understand and transform research university-driven development (learning, discovery, and engagement) in smart regions.  A public policy purpose for improved cognitive systems and social networks is to augment individual and collective intelligence to benefit business and society, and improve quality-of-life in the “nested, networked service systems” that we all belong to, depend on, and co-create as we live, work, learn, and play.

The Cognitive Systems Institute is being established as a global, virtual community-of-interest by IBM first and foremost to understand and collaboratively work to improve the productivity and creativity of multi-disciplinary cognitive systems researchers.   For example, how can improved access to data (including test and training data sets, corpora of literature, patents, start-up proposals, etc.), tools (both open and proprietary), grand challenge problems and cognitive sport tournaments, and other shared resources and events improve the productivity and creativity of cognitive systems researchers and problem-solving professions?

Second, the Cognitive Systems Institute will foster the creation of diverse point-of-views (POVs) documents on how cognitive systems will likely transform education systems and sustainable regional development,  lifelong learning, skills and employment.   For example,  public policy related to personal data, quantified-self, learning, discovery, and sustainable development.  Cognitive systems will augment individual intelligence and augment collective intelligence of organizations and institutions (“smart service systems”).

Please contact (spohrer@us.ibm.com) if you are a cognitive systems researcher interested in learning more about the Cognitive Systems Institute.

Goal 1:  Cognitive Systems will improve the productivity and creativity of Cognitive Systems Researchers, as well as other problem-solving professionals.  What are the most relevant articles, blog posts, books, etc. related to improving Cognitive Systems Researchers productivity and creativity?

For example:

Vattam, S., Wiltgen, B., Helms, M., Goel, A. K., & Yen, J. (2011). DANE: fostering creativity in and through biologically inspired design. In Design Creativity 2010 (pp. 115-122). Springer London.

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 248-255). IEEE.

Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Van Alstyne, M. (2009). Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915), 721.

Goal 2: In addition to augmenting individual intelligence, cognitive systems in conjunction with social networks will increasingly augment collective intelligence of families, businesses,  and cities.  What are the most relevant articles, blog posts, books, etc. related to the transformational impact of cognitive systems on regional educational systems and regional economic development?

We are also planning a “Handbook of Cognitive Systems” that will provide a snap-shot on these two questions from multiple disciplinary, sectoral, and cultural perspectives.   Researchers will be invited to contribute chapters to the handbook from diverse areas of research, including Artificial Intelligence, Cognitive Science, Machine Learning, Pattern Recognition, Planning, Robotics, Computer Vision, Neural Networks, Energy Efficient New Computing Architectures, Big Data Social Physics, Quantified Self, Regional Economic Development, Transformation of Education Systems, Transformation of Professional Systems and Associations, Urban Science, Data Science, Service Science, Public Policy, etc.

Please contact me for more information:

Dr. James (“Jim”) C. Spohrer
Director, IBM Global University Programs (IBM UP) and Cognitive Systems Institute
650 Harry Road, IBM Research – Almaden, San Jose, CA 95120 USA
spohrer@us.ibm.com 408-927-1928 (o) 408-829-3112 (c)

 

Cognitive Systems: Science, Management, Engineering, Design & Arts, Public Policy

POV – Cognitive Systems: Science, Management, Engineering, Design & Arts, Public Policy

The fields of Artificial Intelligence and Cognitive Science are arguably two of the newer academic disciplines, barely older than Computer Science which is still a few decades short of its centennial.

As academics and practitioners of these young fields convene several themes will surely emerge:

First, the progress to date, false steps, and history of many sub-fields will need to be reviewed.
- natural language processing, machine learning, pattern recognition, planning, robotics, knowledge representation and reasoning, expert systems, knowledge engineering environments

Second, the grand challenges for the coming decades, and what breakthroughs as well as incremental progress is likely in store.
- task performance benchmarking (both human and super-human), human and organizational augmentation, bootstrapping and compression of all knowledge, rapidly rebuilding societal systems

Third, reflections on the methodologies for making progress, strengthening the scientific foundation and opportunities for a deeper integration
- logics and mathematical foundations, data sets and corpora, new hardware and software architectures, new areas of application, new and changed professions

Last, but not least, the broader societal implications of this work will likely be highlighted.
- public policy, equity, accelerated change, security and defense, humanities and arts, ongoing transformation of industries

I have begun assembling a few pointers to augmented intelligence grand challenges.  I suspect there is a shift underway .  The discipline of cognitive systems is shifting from a focus on individuals to collectives, or social networks of smart entities.   Therefore, the study of cognitive systems will likely progress from notions of “artificial individual intelligences” to “augmented collective intelligences” as one of the major transformations in the coming decade.  Large investments will be driven by nations and businesses with the goal of upgrading their employees and citizens collective IQ as part of building innovation capacity for smart service systems.

 

 

Improving Improvement in Education

Lately, I have been thinking about the dynamics of a truly smart education system in the age of cognitive systems and social networks, trying to formulate what a ”Moore’s Law For Education” might be like.

So I was delighted when I received an email from my colleague Bill Daul, about a keynote speech by Prof. Louis Gomez (UCLA) and Howard Rheingold (author and educator) entitled “Improving Improvement in Education.”

Here is a short extract, but I urge you to read the whole piece and follow all the links to video etc.

Howard Rheingold wrote:

When I spoke to Gomez for the video that accompanies this post, we talked about the way networked improvement communities are rooted in Engelbart’s thinking from decades ago. An improvement community, in Engelbart’s framework, is not just one that seeks to improve its performance, but one that also seeks to learn how to improve improvement methods. Engelbart made an important distinction between the day-to-day work of an organization, the effort to improve the organization’s performance, and the ongoing conversation about improving improvement:

A Activity: the organization’s day-to-day core business activity, such as product development, customer support, R&D, manufacturing, marketing, sales, accounting, legal, etc. Examples: Aerospace — producing planes; Congress — passing legislation; Medicine — researching a cure for disease; Education — teaching and mentoring students; Associations — advancing a field or discipline.

B Activity: Improving how A work is done, such as improving product cycle time and quality. Examples: improving how A Activities foster customer relations or team building, deliver quality products and services, deliver corporate IT services, manage their people and budgets. Could be an individual learning about new techniques (reading, conferences, networking), or an initiative, innovation team or improvement community engaging with A Activity and other key stakeholders to implement new/improved capability within one or more A activities.

C Activity: Improving how B work is done, such as improving improvement cycle time and quality. Examples: improving effectiveness of B Activity teams in how they foster relations with their A Activity customers, collaborate to identify needs and opportunities, research, innovate, and implement available solutions, incorporate input, feedback, and lessons learned, run pilot projects, etc. Could be a B Activity individual learning about new techniques for innovation teams (reading, conferences, networking), or an initiative, innovation team or improvement community engaging with B Activity and other key stakeholders to implement new/improved capability for one or more B activities.

These activities are ongoing in any healthy organization. However, the current means of improving how we work are not adequate for the scale and rate of change we face today. Most organizations need to acquire much more effective ways of identifying and assimilating dramatic improvements on a continuing basis. … This is all C activity work.

Systematically improving the way we think about improving education is a splendid idea, many will agree. But what, exactly, might it mean in practice? Gomez made a start at addressing this question in “Schooling as a Knowledge Profession,” an Education Week article co-authored with Jal D. Mehta and Anthony S. Bryk.

You can read the rest of what Howard Rheingold wrote here.

Improving improvement is the key to making smart service systems across all sectors a key grand challenge for the future.

Call For Papers – ISES Global Conference on Service Excellence 2014

From: Institute of Service Excellence <ise@smu.edu.sg>

Date: Mon, Jan 27, 2014 at 7:03 PM

Subject: Call For Papers – ISES Global Conference on Service Excellence 2014

To: stephen.kwan@sjsu.edu

 

Dear Prof Kwan,

 

Greetings and happy new year from the Institute of Service Excellence at Singapore Management University (ISES)!

 

Later this year, we will again be hosting the ISES Global Conference for Service Excellence 2014 (IGCSE 2014) and we would like to invite you to submit and present your research papers at the academic proceedings.

 

Held in Singapore, Southeast Asia’s hub for business and tourism, IGCSE will give you many opportunities to professionally network with business leaders, renowned academics, researchers, and thought leaders that share your passion in service research.

 

We are hopeful that your research may dovetail with some of the themes at this year’s conference, including:

 

- Globalisation of service companies

- Customer behaviour, culture, and service marketing

- New technology, service challenges, and opportunities

- Service operations, quality, and productivity

- Organisational behaviour and human resource management in the service industry

- Government policy and roles in service development

- Service innovation

- Skills for the future of service

 

Please be aware that your papers should be submitted to us no later than 31 March 2014.

If accepted, we would also request your availability to present the research at IGCSE 2014 in Singapore on 22 July 2014.

 

 

For more information on submission guidelines, click here

Please also share this with colleagues whom you feel may have relevant research.

 

If you have any questions, feel free to contact myself, or my colleague Ms. Xu Bin (+65 6808 5409 / binxu@smu.edu.sg), if you have any questions. We are looking forward to hearing from you.

 

Yours Sincerely,

 

Ms Caroline Lim

Director,

Institute of Service Excellence at

Singapore Management University

Dr Marcus Lee

Academic Director,

Institute of Service Excellence at

Singapore Management University

 

©Copyright 2013 by Singapore Management University. All Rights Reserved.

 

Some trends and future scenarios

A.      What are the most important competence areas or science fields at the moment ? In the future ?

Since innovation happens at the boundaries, and they are all important, it is important to look for sciences that are “well spaced” and fundamental, as well as integrative.

Service science is integrative so is important.

Physics, Chemistry, Biology, Computer Science & Brain Science, Service Science – are “well spaced” …..

Within Physics and Chemistry – besides the obvious nanotechnology – material science in general is very important, especially bio-friendly processes for purifying materials, as in recycling.

B.      What industries are growing fast at the moment in Silicon Valley? Where do the venture capitalists of Silicon Valley invest the most at the moment?  What industries are struggling to find investors at the moment?

Platform technologies are hot – social media, learning (MOOCs).

Robotics is strong.

Disruptions of smart phones are also strong – Google Glass and Contact Lens, Apple Watch and Wrist Bracelets, Microsoft & Corning Smart Glass-Coated Surfaces, perhaps Facebook, Twitter , Lenovo and other pro-sumer companies or startups will come up with something truly disruptive and surprising.

C.      What kind of challenges companies are facing at the moment? What is preventing them from growing or developing further and faster? (Law, taxations, shortage of resources, know how, money, cyber safety…)

Cybersecurity and the balance of privacy protection and security are big on-going challenges, because technology solutions are only a part of the answer.

Businesses have mastered many disciplines, run-transform-innovate and successful acquisitions, but they are still struggling to master true collaborative innovation, open innovation, and dynamic capabilities, and essential aspects of service-dominant logic and service science.

Regional economic development is very inefficient today and businesses trying to break into that are finding it slow going.

Two key disciplines (1) balancing improve weakest link with improve strongest link policies, and (2) rapid rebuilding from scratch policies – have not been mastered, and require regulatory change.

Education is being disrupted, but the transformation of academia, industry, government is a co-evolution towards more team and project-based work, and the measures are too oriented towards individual performance and not team performance. Need a balance – T-shapes.

D.      What kind of new clusters are about to born (like connected homes and cars, e-learning solutions etc)

The major clusters that are about to form are going to form on each continent – and are (1) manufacturing as a local recycling service, (2) AirB&B for more smart regional economic development.    Each industry sector will be disrupted by this:  (a) transportation – driveless trucks and then car regions, (b) water – local recycling of water, (c) manufacturing – as a local recycling service with new business models, new materials, robotics, 3D printers, etc., (d) hydrogen economy – based on artificial leaf for homes, roads, cars, (e) ICT disrupted by cognitive systems – boosting individual productivity 10x, (f) building construction and recycling at scale for recycling whole sections of cities, with spill over to space systems (g) finance shifts more to crowdfunding and virtual currencies, (h) healthcare goes robotic surgery, 3D printing of organs, and homes with sensors for self-service health, and robotics for elderly, (i) education becomes team based sport, and the lines between education and entrepreneurship and community service blur, (j) governments and regional economic development merge and become rational by adopting a balanced improve weakest-link and strongest-link set of policies – nested, network service system governance becomes explicitly managed.

E.       What will the main 3 trends be in the 2015 from the consumers perspective?

The word consumer will be replaced by pro-sumer, or something that is a better descriptor related to people as resource-integrators.   Better full-life cycle management of people’s lives will be an increasing focus as big data analytics comes to career and health and recreation planning across a life space.  Quantified self.  Much smarter smart phones with more sensors.   Cloud integration with virtual currencies based on activities.

F.        What industries will bring the biggest returns to investors in the next  5-10 years’ time?

Energy, finance, ICT, home improvement, community improvement, regional economic development.  Space systems.

My instincts are that the evolution of regional economic development is about to take off, and it will require future scenarios created by local populations as fuel… when people are preoccupied  by the basic challenges of life in most regions of the world, it is hard from them (except perhaps children) to think much about the future.  However, it is the hopes and dreams about the future, individually and collectively, as well as a focus on grand challenges that is the fuel for future scenario generation.  Is there an app for that?