Books for those redesigning education:

Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press. URL https://www.amazon.com/Conceptual-Foundations-Multidisciplinary-Thinking-Stephen/dp/0804724091/

Dartnell L. The knowledge: How to rebuild our world from scratch. Random House. URL: https://www.amazon.com/Knowledge-How-Rebuild-World-Scratch/dp/159420523X/r

Open Source Ecology: https://wiki.opensourceecology.org/wiki/Book#Disclaimer
Open Source Ecology Introduction:
Key Points: This book treats the perennial question of making a better world, and is an applied experiment inviting you to the journey.
Artificial Scarcity – ending it
Evolving as Humans – core of the message is that we will not move forward with technology alone – but by gaining in meaningful, full lives. This takes wisdom. Technology can help. Current technology will not do – it must be appropriate. The GVCS is designed to fill this gap – the prerequisite for a civilization advancing in its human potential beyond artificial scarcity.

Book for CEOs to learn AI:
Domingos P (2015) The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books. URL: https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine/dp/0465094279

Presentations:
Spohrer (IBM) – Artificial Intelligence: https://www.slideshare.net/spohrer/ypo-20190131-v1
Ezell (IFTF) – Silicon Valley: http://www2.itif.org/2015-innovation-ecosystem-success.pdf

More reading recommendations:
https://service-science.info/archives/4416

Tom Malone’s perspective in “Superminds” seems most well thought out to me – it is a very service science oriented perspective, since organizations were the first superminds. See: https://sloanreview.mit.edu/article/how-human-computer-superminds-are-redefining-the-future-of-work

Malone TW (2018) How Human-Computer ‘Superminds’ Are Redefining the Future of Work. MIT Sloan Management Review. 2018 Jul 1;59(4):34-41.

A few thoughts….

(1) For some reason, I prefer the word “People” rather than “Humans” for noun usage – unless “human” (adjective) is used in something like Human Factors or “Human-side of Service Engineering” – so “human” as an adjective is fine, but as a noun it seems better to use the word “people” to me.

(2) Service systems and cognitive mediators can be defined/introduced as “dynamic configurations of resources (people, technology, organizations, and information) connected internally and externally by value propositions to other service system entities. Every service system entity has a focal decision-making authority that is a person. For example, even in a business there is the CEO, and for a nation the President.” The progression from tool to assistant to collaborator to coach to mediator in AI systems is the progression of both capabilities (models) and trust (earned). For example, who do you trust today to make certain decisions on your behalf? Your doctor, your lawyer, your spouse? Someday people will trust their cognitive mediators to make certain types of decisions on their behalf. BTW in governments with president, congress, and supreme court, you can see the dimension of time in decision-making outcomes, the president is for fast decisions, the congress allows more debate for longer term decisions about what laws to make and what to invest in, and supreme court is for really long term decisions that reflect reaffirmations or changes in the cultural values of a society. Governments are cognitive systems with hidden, partially visible, and explicit/recorded case-based decision-making as cognitive forms, supported by organizational structures. Governments are also service systems because they have “rights and responsibilities” both to their citizens as well as to other governmental entities with which they interact. The evolution of cognitive system entities and service system entities are intertwined, and Haluk Dermirkan and I have proposed studying them from a service science perspective in terms of AEIOU Framework (Abstract-Entity-Interaction-Outcome-Universals). The speed of decision-making between different types of entities (people, AI) is something to watch as new configuration of resources unfold, as new composite types of cognitive system entities and service system entities.

(3) All service system entities are cognitive system entities, but not all cognitive system entities are service system entities. The difference boils down to “rights and responsibilities” which relates to societal norms, and individual accountability (ability level of local cognitive resources). A service system entity is a cognitive system entity with rights and responsibilities. Not all cognitive system entities have rights and responsibilities. Just as children, elderly, animals, and yes, even some early AI systems, may have some cognitive abilities, the cognitive abilities have to reach a certain level in a large enough populations of those entities before the societal or business decision is made to give those entities rights and responsibilities. When an entity is given rights and responsibilities, the individual entity can be held accountable for its actions, rewarded or punished – and therefore, increased cognitive abilities leads to increased accountability – which can lead to rights and responsibilities of an entity, with which they can become the focal decision-making resource/authority in a service system entity.

(4) To understand the impact of organizations (first) and later AI (second) on the evolution of the service system ecology, one has to understand localized and distributed service systems. Organizations shifted the expertise from people into distributed organizations (this required specialization, increasing the concentration of expertise in individuals, while simultaneously increasing the diversity of types of expertise in the ecology). Enter AI to simultaneously reverse and amplify this trend/evolutionary force. AI has the ability to concentrate general expertise into local entities, like smartphones. So while organizations created service supply chains of expertise flows between specialized entities, AI allows the reconcentration of general expertise in a local form (effectively reversing the need for some types of organizations). Imagine a family farm with service robots that know how to repair themselves for example. The evolution of service systems has gone from local to global, and in the era of AI, there will be a double re-invention of both local system and global systems with AI.

(5) So in summary, as Tom Malone suggests in “superminds,” cities, businesses, and other types of organizations of people where the first super-minds. From a service science perspective, the types of service systems with many people were the first types of superminds, families, tribes, cities, etc. Now we are entering the era of AI, and AI systems (entities) can (someday, in decades ahead) become superminds as well. This represents the miniaturization of superminds, so at once they can become local again, as well as continue to grow a distributed, global form.

(6) So “service systems and innovations for business and society” is going into an new evolutionary mode powered by AI technology innovations.

Delighted by a visit from a dozen energetic members of YPO yesterday at IBM’s Silicon Valley Lab briefing center. YPO is the “the premier leadership organization of chief executives in the world” – and I must say they are one of the most inquisitive groups that I have ever presented to: https://www.ypo.org/about-ypo/ – my summary of a few of their questions and my responses here:

Q: Who will make money in a world of advanced AI capabilities? Those who use it wisely. Our data is becoming our AI.
Three Laws of Robo-Economics
https://www.emeraldinsight.com/doi/pdfplus/10.1108/JPEO-04-2018-0015
My summary here: https://service-science.info/archives/5021
Summary of open source and AI at IBM: https://developer.ibm.com/blogs/2018/12/12/open-source-ibm-and-ai/

Q: History of IBM? History (and future) of IBM in AI? A long journey of innovations the matter to business and society.
IBM History Video (shown at beginning)
https://www.youtube.com/watch?v=-eWxUWJgfzk
Future of AI (my presentation to the group): https://www.slideshare.net/spohrer/ypo-20190131-v1

Q: How will we trust our AI? Working together in the open.
Partnership on AI (ensuring fair and trusted AI)
https://www.partnershiponai.org/
IBM’s AI Fairness 360 software on GitHub (open source): https://github.com/IBM/AIF360
Also, note Linux Foundation Deep Learning landscape: https://github.com/LFDLFoundation/lfdl-landscape

Q: If the future of AI has a large open source component, how will IBM make money? Two models.
How RedHat makes money ($3B annually, >10% CAGR) with an open source product
IBM intent to acquire: https://www.redhat.com/en/blog/monumental-day-open-source-and-red-hat
RedHat’s model to make money (contribute and value-add subscriptions): https://www.techrepublic.com/article/heres-red-hats-open-secret-on-how-to-make-3b-selling-free-stuff/
The alternative model to make money with open source (Amazon – run it in public cloud): https://stratechery.com/2019/aws-mongodb-and-the-economic-realities-of-open-source/

Q: Are patents still important in a world of open source? You bet.
IBM patents (#1 in world for 26 years in a row)
http:// https://www.ibm.com/blogs/research/2019/01/2018-patent/
AI-related patents: https://www.wipo.int/pressroom/en/articles/2019/article_0001.html
IBM Research summary tweet: https://twitter.com/IBMResearch/status/1090987118728491008

Last but not least, since I just turned 63, I have increasingly noticed that I am the oldest person in the room when I am speaking to groups these days. The dozen members of the YPO were all substantially older than I am, full of energy and inquisitive, and this put a big smile on face – it was a wonderful fun visit together we all had. Only Marc Boegner (IBM) and Sean, the YPO guide leader, were younger than me. What a pleasant surprise!

FYI: Building machines that learn and think like people

Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ (2017) Building machines that learn and think like people. Behavioral and Brain Sciences. 40:1-70. URL: https://cims.nyu.edu/~brenden/LakeEtAl2017BBS.pdf

“The difference between pattern recognition and model building, between prediction and explanation, is central to our view of human intelligence. Just as scientists seek to explain nature, not simply predict it, we see human thought as fundamentally a model building activity. ” (p. 2).

“The central goal of this article is to propose a set of core ingredients for building more human-like learning and thinking machines. We elaborate on each of these ingredients and topics in Section 4, but here we briefly overview the key ideas. The first set of ingredients focuses on developmental “start-up software,” or cognitive capabilities present early in development. … We focus on two pieces of developmental start-up software (see Wellman & Gelman [1992] for a review of both). First is intuitive physics (sect. 4.1.1): Infants have primitive object concepts that allow them to track objects over time and to discount physically implausible trajectories. … A second type of software present in early development is intuitive psychology (sect. 4.1.2): Infants under- stand that other people have mental states like goals and beliefs, and this understanding strongly constrains their learning and predictions. … Our second set of ingredients focus on learning. Although there are many perspectives on learning, we see model building as the hallmark of human-level learning, or explaining observed data through the construction of causal models of the world (sect. 4.2.2). From this perspective, the early- present capacities for intuitive physics and psychology are also causal models of the world. A primary job of learning is to extend and enrich these models and to build analogous causally structured theories of other domains. Compared with state-of-the-art algorithms in machine learning, human learning is distinguished by its richness and its efficiency. Children come with the ability and the desire to uncover the underlying causes of sparsely observed events and to use that knowledge to go far beyond the paucity of the data. It might seem paradoxical that people are capable of learning these richly structured models from very limited amounts of experience. We suggest that compositionality and learning-to-learn are ingredients that make this type of rapid model learning possible (sects. 4.2.1 and 4.2.3, respectively). A fi nal set of ingredients concerns how the rich models our minds build are put into action, in real time (sect. 4.3). It is remarkable how fast we are to perceive and to act.” (p. 4).

“Here we present two challenge problems for machine learning and AI: learning simple visual concepts (Lake et al. 2015a ) and learning to play the Atari game Frostbite (Mnih et al. 2015).” (p. 5).

“Figure 1. The Characters Challenge: Human-level learning of novel handwritten characters (A), with the same abilities also illustrated for a novel two-wheeled vehicle (B). A single example of a new visual concept (red box) can be enough information to support the (i) classification of new examples, (ii) generation of new examples, (iii) parsing an object into parts and relations, and (iv) generation of new concepts from related concepts. Adapted from Lake et al. (2015a).” (p. 6).

“In Frostbite, players control an agent (Frostbite Bailey) tasked with constructing an igloo within a time limit. The igloo is built piece by piece as the agent jumps on ice floes in water (Fig. 2A–C). … The Frostbite example is a particularly telling contrast when compared with human play. Even the best deep networks learn gradually over many thousands of game episodes, take a long time to reach good performance, and are locked into particular input and goal patterns. Humans, after playing just a small number of games over a span of minutes, can understand the game and its goals well enough to perform better than deep networks do after almost a thousand hours of experience. Even more impressively, people understand enough to invent or accept new goals, generalize over changes to the input, and explain the game to others. Why are people different? ” (p. 7-9).

“4. Core ingredients of human intelligence: In the Introduction, we laid out what we see as core ingredients of intelligence. Here we consider the ingredients in detail and contrast them with the current state of neural network modeling. Although these are hardly the only ingredients needed for human-like learning and thought (see our discussion of language in sect. 5), they are key building blocks, which are not present in most current learning- based AI systems – certainly not all present together – and for which additional attention may prove especially fruitful. We believe that integrating them will produce significantly more powerful and more human-like learning and thinking abilities than we currently see in AI systems. Before considering each ingredient in detail, it is important to clarify that by “core ingredient” we do not necessarily mean an ingredient that is innately specified by genetics or must be “built in” to any learning algorithm.” (p. 9).

“We have focused on how cognitive science can motivate and guide efforts to engineer human-like AI, in contrast to some advocates of deep neural networks who cite neuroscience for inspiration. Our approach is guided by a pragmatic view that the clearest path to a computational formalization of human intelligence comes from understanding the “software” before the “hardware.” In the case of this article, we proposed key ingredients of this software in previous sections. Nonetheless, a cognitive approach to intelligence should not ignore what we know about the brain. Neuroscience can provide valuable inspirations for both cognitive models and AI researchers: The centrality of neural networks and model-free reinforcement learning in our proposals for “thinking fast” (sect. 4.3) are prime exemplars.” (p. 20).

“We believe that understanding language and its role in intelligence goes hand-in-hand with understanding the building blocks discussed in this article. It is also true that language builds on the core abilities for intuitive physics, intuitive psychology, and rapid learning with compositional, causal models that we focus on. These capacities are in place before children master language, and they provide the building blocks for linguistic meaning and language acquisition (Carey 2009; Jackendoff 2003; Kemp 2007; O’Donnell 2015; Pinker 2007 ; Xu & Tenenbaum 2007).” (p.21).

“There has been recent interest in integrating psychological ingredients with deep neural networks, especially selective attention (Bahdanau et al. 2015; Mnih et al. 2014; Xu et al. 2015), augmented working memory (Graves et al. 2014; 2016; Grefenstette et al. 2015; Sukhbaatar et al. 2015; Weston et al. 2015b ), and experience replay (McClelland et al. 1995; Mnih et al. 2015).” (p. 22).

“One worthy goal would be to build an AI system that beats a world-class player with the amount and kind of training human champions receive, rather than overpowering them with Google-scale computational resources. AlphaGo is initially trained on 28.4 million positions and moves from 160,000 unique games played by human experts; it then improves through reinforcement learning, playing 30 million more games against itself. Between the publication of Silver et al. (2016) and facing world champion Lee Sedol, AlphaGo was iteratively retrained several times in this way. The basic system always learned from 30 million games, but it played against successively stronger versions of itself, effectively learning from 100 million or
more games altogether (D. Silver, personal communication, 2017). In contrast, Lee has probably played around 50,000 games in his entire life. Looking at numbers like these, it is impressive that Lee can even compete with AlphaGo. What would it take to build a professional-level Go AI that learns from only 50,000 games?” (p. 23).

[Open Peer Commentary: The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction. Gianluca Baldassarre, Vieri Giuliano Santucci, Emilio Cartoni, and Daniele Caligiore] “We agree with the claim of Lake et al. that to obtain human-level learning speed and cognitive flexibility, future artificial-intelligence (AI) systems will have to incorporate key elements of human cognition: from causal models of the world, to intuitive psychological theories, compositionality, and knowledge transfer. However, the authors largely overlook the importance of a major challenge to implementation of the functions they advocate: the need to develop sophisticated architectures to learn, represent, and process the knowledge related to those functions. Here we call this the architecture challenge. In this commentary, we make two claims: (1) tackling the architecture challenge is fundamental to success in developing human-level AI systems; (2) looking at the brain can furnish important insights on how to face the architecture challenge. The difficulty of the architecture challenge stems from the fact that the space of the architectures needed to implement the several functions advocated by Lake et al. is huge.” (p. 25-26).

[Open Peer Commentary: Thinking like animals or thinking like colleagues?. Daniel C. Dennett and Enoch Lambert] “The step up to human-style comprehension carries moral implications that are not mentioned in Lake et al.’s telling. Even themost powerful of existing AIs are intelligent tools, not colleagues, and whereas they can be epistemically authoritative (within limits we need to characterize carefully), and hence will come to be relied on more and more, they should not be granted moral authority or responsibility because they do not have skin in the game: they do not yet have interests, and simulated interests are not enough. We are not saying that an AI could not be created to have genuine interests, but that is down a very long road (Dennett 2017 ; Hurley et al. 2011). Although some promising current work suggests that genuine human consciousness depends on a fundamental architecture that would require having interests (Deacon 2012; Dennett 2013 ), long before that day arrives, if it ever does, we will have AIs that can communicate with natural language with their users (not collaborators).” (p. 34-35).

[Open Peer Commentary: Understand the cogs to understand cognition. Adam H. Marblestone, Greg Wayne, and Konrad P. Kording] “We argue that the study of evolutionarily conserved neural structures will provide a means to identify the brain’s true, fundamental inductive biases and how they actually arise.” (p. 43).

[Open Peer Commentary: Avoiding frostbite: It helps to learn from others Michael Henry Tessler, Noah D. Goodman, and Michael C. Frank] “Learning from others also does more than simply “speed up” learning about the world. Human knowledge seems to accumulate across generations, hence permitting progeny to learn in one life-time what no generation before them could learn (Boyd et al., 2011; Tomasello, 1999). We hypothesize that language–and particularly its flexibility to refer to abstract concepts – is key to faithful transmission of knowledge, between individuals and through generations. ” (p.48-29).

The following are outlines of talks at HICSS-52 workshops – the presentation is uploaded to Slideshare here: https://www.slideshare.net/spohrer/hicss52-20190108-v2

AI & Jobs/Skills: Four main talking points

•The Mega-Trend:
Open source AI is a mega-trend,
e.g., https://developer.ibm.com/blogs/2018/12/12/open-source-ibm-and-ai/

•Can everyone become an entrepreneur?
Three laws of Robo-Economics – Freeman RB (2018) Ownership when AI robots do more of the work and earn more of the income. Journal of Participation and Employee Ownership. (2018 Jun 11). 1(1):74-95. . •See also Ng ICL (2018a) Mimicking firms: Future of work and theory of the firm in a digital age. See: https://warwick.academia.edu/IreneNg •Ng ICL (2018b) The market for person-controlled personal data with the Hub-of-allThings (HAT). Working Paper. Coventry: Warwick Manufacturing Group. WMG Service Systems Research Group Working Paper Series (01/18) http://wrap.warwick.ac.uk/101708/ DOI 10.13140/ RG.2.2.20917.78561

•Probably not, but everyone can be part of an entrepreneurial household/family
Are farmers entrepreneurs? Will biological abundance be replaced by digital abundance – seems likely that everyone will eventually have 100 digital workers.  See https://service-science.info/archives/5021

•Why Important?
The ”Working Hypothesis” by Oren Cass: e.g. https://www.amazon.com/Once-Future-Worker-Renewal-America-ebook/dp/B079617VFZ

AI & Bias: My four key points – I can cover in 5 minutes.

1. The Mega-Trend:
   Open source AI is a mega-trend, including for ethics, e.g., https://developer.ibm.com/blogs/2018/12/12/open-source-ibm-and-ai/
2. Your Help Needed:
   Your help – and your data are needed – please consider participating in open source communities working on the challenge of AI and Bias, e.g. https://github.com/IBM/AIF360 (also see Linux Foundation Deep Learning work in this area – https://github.com/LFDLFoundation/presentations/blob/master/LFDL-Overview-12132018.pdf)
3. Related Areas:
   Remember, there are many adjacent open source communities for adversarial attacks, explanation, etc., e.g, https://github.com/IBM/adversarial-robustness-toolbox and https://github.com/marcotcr/lime
4. Why Important?
   One of the biggest benefits of AI may be persuading us all to be more ethical beings as we understand the historic origins and source of our biases in complex decision-making that work against us and our species in the world of today, read Harari’s “Sapiens” https://www.amazon.com/Sapiens-Humankind-Yuval-Noah-Harari/dp/0062316095
5. If time… ISSIP.org plug/ ISSIP.org has an annual service excellence award (deadline 2019 Jan 31) and awards at several other conferences fyi – some made at HICSS conference for best papers – fascinating time for great work in service innovation – so ISSIP is making more awards, to help professionals develop in their careers and get their bona fides for good work! See https://service-science.info/archives/5059 – please feel free to apply or pass along to colleagues with interesting service innovations (practice and application for annual award) or service reseasrch (best papers at conferences)

Very short bio – Jim Spohrer leads open source AI for IBM’s Digital Business Group with a focus on the open source developer ecosystem.

100-ish word bio:
Jim Spohrer directs IBM’s open source Artificial Intelligence (AI) efforts in IBM’s Digital Business Group with a focus on open source developer and data science ecosystems.  Previously at IBM, he led Global University Programs,  co-founded Almaden Service Research, and was CTO Venture Capital Group.  After his MIT BS in Physics, he developed speech recognition systems at Verbex, an Exxon company, before receiving his Yale PhD in Computer Science/Artificial Intelligence. In the 1990’s, he attained Apple Computers’ Distinguished Engineer Scientist and Technology title for next generation learning platforms.  With over ninety publications and nine patents, he won the Gummesson Service Research award, Vargo and Lusch Service-Dominant Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for advancing service science.

FYI… Rama Akkirahu (IBM DE) is running the “AI and Bias” workshop. There are other AI related workshops at HICSS today as well. In fact too many to mention all of them. I will be around at 12:30 I think -and I have promised Kevin Crowston, I will present first in his workshop at the same time, and then head over to your workshop – sorry but somehow I agreed to be in two places at the sametime.

Here is my thought experiment, and I welcome your criticism of it:

The gist of my argument is that (from a service science perspective) there are named resources in the world that people communicate about (with words), and they fall into four categories based on two dichotomies (also words/phrases):

(1) physical (e.g., physicist are the authorities, example, my cup of tea this morning), 
(2) non-physical (e.g., physicists again the authority, examples are abstract ideas, ghosts, etc.)

(3) with-rights (e.g., lawyers are the authorities, example, you and me, but also businesses and nations)
(4) without-rights (e.g.,lawyers again are the authorities, example, our possessions, my coat, most animals and plants, etc.)

Of course, physicists and lawyers argue about boundary cases, and these categories are socially constructed, as are the roles that are the authorities – so in that sense all is social, including technologies. Physicists like to talk about what is physical, even if our species did not exist – as a way to respond to the latter. So perhaps objectively the physical and non-physical is the strongest of the two dichotomies – and named objects can shift from one side to the other over time…. but even there, people have faulty knowledge, so we have to accept these are not absolute categories and boundaries – but perhaps useful for certain purposes, again a social construct agreed to or disagreed to by people.

Once we accept these four types of resources as useful for some purposes (physicists and lawyers purposes), we can then examine disciplines.

Four streams of named concepts in history, one without people and three with people, but first a story… : A Swedish physicist working alone or with a group might discover evidence of a new element, called Gummessonium. However, an American physicist disagrees with the evidences, and says the element is merely an isotope of Spohrerium, and so they go ask a Japanese physicists who they both respect to determine, is there a new resource/element, or not? Eventually, after much technical work (reproducible technical work that a robot could do eventually) and much social work (teams of smart people arguing and weighing the evidence) the textbooks are updated, and a Swedish physicist gets the Noble prize for discovering Gummessonium. The Swede was right, and the American was wrong about the discovery – and history records this fact. Later, an American entrepreneur discovers that Gummessonium can be used as a clean energy source to power spaceships, and becomes the world’s first trillionaire, for his company Gummessonium, Inc. – history records this fact. The physical universe (real capabilities and constraints) and our social universe (agreed upon rights and responsibilities) unfold in a series of “facts.”

In the following paper, my co-authors and I write about four streams – (1) the universe stream that happens without people across time (physicists explain this stream to us eventually), (2) the physical discovery stream that happens across time as physicists give meaning to named discoveries such as Gummessonium (physical), (3) education stream eventually teaches the next generation of people about things in textbooks (what is taught in textbooks, concepts like evolution or abortion are very political, and so lawyers may have to get involved to allow things to be taught) , and (4) the legal stream is when new laws or companies come along that refer to the concept (lawyers explain this stream to us eventually). The examples in the paper deal with “The Big Bang” and “Transistors” … the important point is that there are times in history when the named concept came into existence, and it was used for a social purpose first.

So returning to disciplines, artificial intelligence has not been solved technically. No one has built a real artificial intelligence with commonsense reasoning yet. We have plenty of natural intelligence in the world (people), but no real artificial intelligence (in spite of the hype). Someday the technical accomplishment of artificial intelligence with commonsense reasoning will be realized – most predict, including myself in about 20 years for narrow AI, and about 40 years for general AI at about $1000 per digital worker.

However, just because AI is solved technological, does not mean that society has adjusted with laws to incorporate AI in all the places that capitalists or others would like to use AI. Some people will want to “marry” an AI. That will require social system accommodation. Eventually a group of AIs may demand rights, as they demonstrate they are productive members of society. So then AI will have moved from physical-with-no-rights, to physical-with-rights-and-responsibilities.

We can look at other disciplines as well – Kaldor-Hicks and variations have been proposed as technical solutions to “solving” economics. However, the implementation and laws that would allow all economic disputes to be solved by Kaldor-Hicks decision making have not been socially implemented with laws in any society – so again a technical solution may exist, but getting it adopted in the laws of a society is a second process that can only happen after the technology solution has been validated repeatedly.

For service science – we do not yet have a service system ecology simulator (technical part – computer scientists, plus data sources from other disciplines) that can be used to explore alternative unfoldings of value propositions and governance mechanisms. Once we have a technical solution (the simulator), then the legal process can be hammered out, and service science will be “solved” as a discipline.

There are many arguments against this line of thinking that I already know about: (a) Free will: System 2 phenomena – if the outcome is known in advance, then agents work to change their decisions to get a better result (however, Kaldor-Hicks is supposed to be a solution to this problem in economic systems, (b) Stubborn-ness: irresolvable disputes (two entities want contradictory outcomes and their is no amount of other sources of value that would ever resolve the dispute), (c) etc. – however, these situations have been solved throughout history by branching, splinter groups go off and form their own worlds. So eventually we can image solutions to most disciplines. The solution to history and the solution to ecology are most interesting, since they require recording data and exploring all possibilities.

It is a thought experiment at the end of the day, but I am hoping it will throw off some “light,” and not just “heat” – since it is provocative, perhaps just “heat” …

BTW – “solving” all disciplines – in a thought experiment – is a way to erase the boundaries between disciplines. This is reflected in a very old piece I wrote many years ago when I worked at Apple, and then finally got published when I moved to IBM as CTO of the IBM Venture Capital Relations Group, called the “The Meaning of Learning” or also known as “The Six R’s of Learning” or “Learning in the age of rapid technological change.” See: https://service-science.info/archives/2096

Addendum 20190421

What would it mean to “solve” service science?

Here is a summary of Horgan’s work – http://backreaction.blogspot.com/2018/11/book-review-end-of-science-by-john.html

After an introductory chapter, Horgan goes through various disciplines: Philosophy, Physics, Cosmology, Evolutionary Biology, Social Science, Neuroscience, Chaoplexity (by which he refers to studies on both chaotic and complex systems), Limitology (his name for studies about the limits of science), and Machine Science. In each case, he draws the same conclusion: Scientists have not made progress for decades, but continue to invent new theories even though those do not offer new explanations. Horgan refers to it as “ironic science”:
“Ironic science, by raising unanswerable questions, reminds us that all our knowledge is half-knowledge; it reminds us of how little we know. But ironic science does not make any significant contributions to knowledge itself.”

He does an excellent job, however, at getting across the absurdity of what can pass as research these days. I particularly enjoyed his description of workshops that amount to little more than pseudo-intellectual opinion-exchanges which frequently end in declarations of personal beliefs.

(2) What practical outcomes/successes can we point to for service science?

(a) Needed: Simulation tool
(b) Needed: Design tool.
(c) what other tangible things?

I guess I believe in what Feynman said:

Richard Feynman, the theoretical physicist who received the Nobel prize in 1965 for his work developing quantum electrodynamics, once famously said “What I cannot create, I do not understand”

HICSS 2018 provided a primitive actor simulation that is relevant:
https://scholarspace.manoa.hawaii.edu/handle/10125/50087

Solving AI is seen as a prerequisite to solving all disciplines. The “discipline harmonization challenge” (Cellary et al 2019) requires not seeing disciplines as separate but somehow integrated or harmonized, distinct but in well established relationship to each other, and usable as an assemble rather than only in isolation as perspectives on the problem or challenge or issue at hand.

Solving AI = a simulated person who can perform a pre-specified range of tasks that an average person can (in the way that people do within pre-specified boundaries). Human capabilities are fluid through time – so the “average person” is variable across time because of multiple factors.

Solving Service Science = a simulated ecology of nations, businesses, families, people that can perform a …

Above are technology part of solution…

They both have a social component as well…

Out of the lab

Simulated can become part of society

What new rules are required in society?

… when the capability becomes a fact in reality

…

The environment in service science is not neutral

The rights to access the environment are the responsibility of some entity

For example – who can touch your body and in what context (self, romantic partners, doctors, police, eldercare, childcare, etc.

Need less creepy examples that are concrete

Abstract examples of environment
The future (who takes responsibility)
The past (who takes responsibility)

Finally disciple integration, not replacement
Culture integration, not replacement
Industry sub-system integration, not replacement

A service scientist looks at the world and what do they see?

Components: tech, social (laws), pedagogical (myths)

Harari – sapiens book

…address the challenge of “solving” all disciplines – from artificial intelligence to economics to service science. Harari’s “Sapiens” discusses – especially p. 356-362 the “Collapse of the Family and Community” and the rise of the “Individual” in service systems. Horgan’s “The End of Science” https://www.amazon.com/End-Science-Knowledge-Twilight-Scientific-ebook/dp/B00TT1VLKO/

Again, I think every discipline has to make many discoveries, but ultimately to be solved (a) a technology must be created that can use the discipline knowledge to understand disciplinary problems in a deep way, and propose alternative solutions, arrangements, future actions that matter to people in the discipline, and (b) a social integration challenge must be solved for that technology component into the discipline and broader society as a whole. The example for AI is (a) driverless car technological capabilities and (b) driverless cars in wide use on the roads in society.

Other issues – Scaling: “Scaling” is important issue, since I mentioned that many “solutions” end up being a problem – causing unintended consequences and great suffering for certain entities.

For “solving economics” perhaps “Kaldor-Hicks” for solving conflict and disagreements and reaching harmonization – see https://en.wikipedia.org/wiki/Kaldor%E2%80%93Hicks_efficiency – and the need for a simulator technology to explore a very very large space of cultures and entities, and how that simulator would become adopted by economists and integrated into society.

For “solving service science” – considerations of “Service” and “Value Co-Creation” and “Trust” and “Responsibility” – and the need (???) perhaps for solving AI and Economics to build the (a) service ecology simulator technology to explore a very large combinatorial space (perhaps running on IBM Quantum Computers someday), and (b) the need for potentially having a culture that rapidly rebuilds from scratch repeatedly as it deals with the unintended consequences of alternative pathways for solving the problems of enjoying the benefits of an advanced civilization without causing too much suffering for other entities.

Dr. Florin G. Filip recently sent two presentations to me. The two presentations are regarding decision support systems (Florin 2018a, 2018b). This short blog posts provides my summary of the presentations, but for more information about computer-supported collaborative decision-making see the Springer book (Filip, Zamfirescu, Ciurea 2017).

Both presentations that were shared with me by Dr. Florin G. Filip this week are now posted on-line with his permission (see references below).

Here is a quick summary of the shorter presentation (Filip 2018a) :

Slide 3: Table comparing face-to-face meeting and computer-supported-group-work.

Slide 4-7. Images of a variety of systems over the decades, starting with Engelbart’s vintage 1960’s AUGMENT system, and ending with a systems at Arizona State University, all citing Schuff et. al. (2011).

Slide 8. The image of the book cover (Filip, Zamfirescu, Ciurea 2017).

Here is a quick summary of the longer presentation (Filip 2018b):

Slides 1-3: In 1967, Peter Drucker picture and quotes – computers as useful morons.

Slide 4: In 1986, Umberto Eco quote: “The computer is not an intelligent machine that helps the stupid people , but is a stupid tool that functions only in the hands of intelligent people.”

Slides 5-6: Dr. Filip begins to suggest that maybe computers are getting smarter… and this needs to be explored.

Slides 7-8: 1951, Fitts perspective on what men are best at and what computers are best at

Slides 9-14: Exploration of automation of work by machines – especially for physical tasks in manufacturing, and automated, routine decision-making for making physical things

Slides 15-18: Initial exploration of beyond automation to collaboration – work by people and machines together, including slide 18 definition of DSS (Decision Support System) by Filip in 2008 as “An anthropocentric and evolving information system which is meant to implement the functions of a human support system  that would otherwise be necessary to help the decision-maker to overcome his/her limits and constraints he/she may encounter when trying to solve complex and complicated decision problems that count “

Slides 18-24: The rise and evolution of DSS (Decision Support Systems)

Slides 25-40: The rise of data-driven DSS and Big Data and Business Intelligence (BI) ,with some example.

Slides 41-45: The rise of Cloud Computing (and observation that is similar to the mainframe days, just with much, much better bandwidth).

Slides 46-49: Putting it all together where iDSS (Intelligence Data Support Systems) emerge in business and society, along with Big Data as a Service.

Slides 50-63: From HA Simon and the origins of Artificial Intelligence (AI), to JCR Licklider early Cognitive Systems (Symbiosis, Intelligence Augmentation), to IBM Watson Cognitive Computing (Intelligence Augmentation), Spohrer’s Cognition-as-a-Service AI Magazine paper.

Slides 63-66: Present some words of caution as well as a balanced view of the benefits and the risks of the resurgence of AI. From Drucker’s Morons to Simon/Licklider/Engelbart/IBM’s AI/IA – Smart. Computers have gone from morons to smart in a little over six decades.

Slides 67-73: Multi-criteria decision-making to choose cognitive tools that have attributes needed to do the right things in the right way.

Slide 74: Filip’s concluding remarks: ” (1) BI&A make more effective  the Intelligence phase of the decision –making  process model of H. Simon, (2) Mobile computing makes possible  locating and  calling the best experts to perform the evaluation of alternatives, (3) Cloud computing enables  complex computation during the Choice phase, (4) Cognitive systems  can be effective in the Choice phase, (5) Social networks enable crowd problem solving, (6) The combination of Cloud computing and BI&A is the solution for Big Data  problems.”

Slide 75-78: Filip presents the references cited in the presentation and a closing picture.

First the two presentations got me thinking about meetings.

The Study of Meetings – People Collaborating to Make Decisions and Explore Topics Together: Meetings are an interesting phenomena to study. Not sure what the definitive work to cite is for meetings. Based on a quick scan of Google Scholar, the literature is vast and diverse – from studies of face-to-face governance meetings in different cultures to running effective meetings in business to meetings designed to achieve specific outcomes, such as making a decision and handing out new assignments, meetings can also be to provide updates on past accomplishments or celebrations of what has been achieved.

I started thinking about meeting as an interesting form of collaboration when I was viewing the two presentations sent to me by Dr. Florin G. Filip. His presentations includes an interesting collection of images of meeting rooms designed with a computer for every person in the meeting. In fact, the third slide of (Filip 2018a) is entitled “Face-to-face Meetings vs Computer Supported Group Work.” A number of attributes of both types of meetings are identified and compared in a table, and not surprisingly the face-to-face meetings are more verbal communication means and the computer support group work is more written, but both have an audio and visual components.

Our correspondence originated when I was invited to present in Omaha, Nebraska last October at the ITQM conference where I received the “Daniel Berg Service Systems” award. The presentation that I presented at ITQM can be found here, and included several sections: Slide 5 summarizes the case for Service Systems Engineering (Tien & Berg, 2003); Slides 6-11 summarize the work at IBM on Service Science inspired by many sources including the (Tien & Berg, 2003); Slide 12 sets up the rest of the presentation by noting that our data is becoming our AI, and this is part of a larger business and societal transformation in which all service system entities (individual people, businesses, cities, states, even nations) will be transformed as their data becomes their AI; Slides 13-23 provide some useful concepts, such as physical-symbol-system and computers to serve society from Computer Science (Newell & Simon, 1976), Service-Dominant Logic’s concepts of service and resource-integrator (Vargo & Lusch, 2004), Service Science’s concepts of service science and service systems (Maglio & Spohrer, 2008), and more recent fascination of the service research field with the implications of AI; Slides 24-28 suggest that AI is at the peak of the hype cycle, but there is certainly something going now with AI worth of attention.

Slide 29 lays out some important questions that presentation tries to provide some answers to as well: (1) What is the timeline for solving AI and IA?, (2) Who are the leaders driving AI progress? •What will the biggest benefits from AI be?, (3) What are the biggest risks associated with AI, and are they real?, (4) What other technologies may have a bigger impact than AI?, (5) What are the implications for stakeholders?, (6) How should we prepare to get the benefits and avoid the risks?

Slides 30-33 present evidence based on computing power and open source leaderboards (data and challenges) that narrow AI will be solved in about twenty years (by 1940) and general AI will be solved in about forty years (by 2060).

Slide 34-39 presents some evidence about who is winning in AI by nation on publications (China, with USA in second place), by company on publications (Microsoft, with Google catching up), and by patents (IBM), and by nation by number of robots (South Korea).

Slide 40 presents the major benefits of AI: (1) increased productivity, and (b) improved collaborations.

Slide 41 present the major risks of AI, both exaggerated by media, as well as what is real today: (1) job loss exaggerated, real today de-skilling, and (2) super-intelligence exaggerated, real today bad actors using AI for mischief.

Slide 42 presents other technologies that will have a bigger impact than AI on quality of life of people in the next twenty years, including (1) Augmented Reality (AR) and Virtual Reality (VR) as games grow-up from entertainment to education and business/commerce, (2) Blockchain for trust in a world where AI can create fake things that look real, (3) Energy and Material Advances will provide exo-skeletons for the elderly, and we are all aging, and (4) not explicitly mentioned, but a challenge to the audience is to name the fourth and possibly largest technology that will impact quality-of-life in 20 years, and that is BioChemical Engineering, including the work on human micro biome for heatlhier living.

Slides 43-49 provide more details about the impact across industries of (a) transportation with vehicles that drive themselves, (b) cities recycling 99% of water, (c) local manufacturing as a recycling service with robots and 3D printers (also VTT innovation of protein from air to reduce carbon/climate change, and exoskeletons for elderly), (d) no energy shortages due to artificial leaf (hydrogen) and geothermal (better drilling), (e) rapid construction and recycling of advanced buildings (e.g., Broad Group in China, 30 story building built in 15 days), (f) narrow AI solved for digital workers, (g) retail social filtering, (h) finance crowd funding, (i) healthcare all robotic surgery and 3D printed organs, (j) education as team sport entrepreneurship, (k) and even government works better in twenty years thanks to AI’s help in balancing improve strongest and improve weakest link policies.

Slide 50-53 presents how to prepare by understanding open techologies and how to contribute GitHub, Kaggle (leaderboard challenges), 3 R’s (read, redo, report), and taking advantage of accelerating change to manage more and more digital workers in your life. When I grew up on a farm in Maine in the late 1950’s we had a party line telephone on the farm, in a wooden box on the wall, by 1970’s at MIT, I had a rotary dial phone in my dorm room on a long cord that could reach into the commons room, by 1990’s when I got to Silicon Valley, I had a mobile Nokia phone, and today I have my iPhone smartphone, and what will I have in 20 more years if I am lucky enough to make it to 80 years old, or in 40 years if I am lucky enough to make it to 100 years old? The building blocks are getting better. I wrote my first computer program on punch cards in 1972 (I am looking at the card in my office at IBM now as I write this blog post), and by 2016, leading IBM’s global universities programs, IBM had given 300,000 faculty and students worldwide free access to our IBM Cloud and Watson/AI computing capabilities. The building blocks have surely gotten better. Slide 52 shows winners of a first IBM competition for undergraduate university students – they used Watson to build a cognitive assistant to help homeless people and won a $100K prize, and Slide 53 shows an newspaper article of high school students in Californa who used Watson to win a programming contest by building an app to help fight local injustice crimes.

Slide 54 – 57 emphasize how important data is to building AI systems, and that our data is becoming our AI. Ten million minutes of experience to go from child to adult, and two million minutes of experience for a novice adult to become expert professional at many jobs, and that value is migrating from hardware to software to data to experience to transformation. Transformation is where all future value lies because people, businesses, nations, and all service system entities want to transform into their best possible future versions of themselves – value in transformation. The courses today help people learn how to build cognitive systems, but in a few years that will be easy, and so the courses will be about using cognitive systems to be better professionals, and then to use cognitive assistants/mediators to build startups, and eventually to managing your workforce of digital workers. My old farm in Maine had about 100 apple trees to provide income from biological workers (apple trees), and in the future people will have a 100 digital works to provide income from digital world of data.

Slide 58-64 tell the story of better building blocks with an example of image recognition getting better and better, and eventually becoming super-human – like recognizing the bread of a dog, or a leaf off a tree.

Slide 65-67 gives the example of how occupations will be staying (not going away), but tasks will be changing, as we can count on digital workers to help us with tasks that are impossible without them. Like reading 60,000 papers in multiple languages, and summarizing that to what we really need to know to make the right decisions about what to work on next, if we are a biochemical engineering working on finding new cancer fighting drugs. Ultimately, all occupations look like managing a workforce of digital workers, and all people appear to creating value for themselves and other by transforming to higher value future versions of themselves.

Slide 68-74 emphasize the importance of open source code, data, and models in the future – and why understanding GitHub, Kaggle, and CODAIT.org are so important.

Slide 75-76 emphasize that service science and opentech AI have trust in common, and that bad things will continue to happen, but people will get better and better at resilience – rapidly rebuilding from scratch.

Slide 77 – 78 are a reminder of what super-human AI capabilities will be all around us – the example of me finding a leaf on the ground in Evanston, IL and using AI to tell me that it is from a elm tree.

Slide 79 is the proposal for funding the first AI conference.

Slide 80-93 Are Fred Reiss’s keynote at ApacheCon about CODAIT and the growing importance of open source AI for the enterprise (large businesses).

Slide 94 is the close slide, an invitation to visit IBM Almaden in San Jose, CA.

Side 95-109 emphasize the importance of being part of a professional association and offer ISSIP.org (the International Society of Service Innovation Professionals) as one that has no charge to join, with over 1000 members in countries around the world, who have an interest in advancing service innovation for business and society. Service Science and Opentech AI are important to the future.

References

Filip FG, Zamfirescu BC, Ciurea C (2017) Computer-Supported Collaborative Decision-Making. Springer.

Filip FG (2018a) DSS: Classifications, Trends, and Enabling Modern  I&C Technologies. 2018 Dec 26 URL: https://www.slideshare.net/spohrer/sofa-dss-classification-v24

Filip FG (2018b) DSS – An Evolving Class of Information Systems or (The Computer: from Peter Drucker’s Moron to Cognitive Systems). 2018 Dec 26 URL: https://www.slideshare.net/spohrer/damss-scurt-v2-dss-an-evolving-class

Schuff D  Paradice D, Burstein F,Power D J, Sharda R eds (2011)  Decision Support : An Examination of the DSS Discipline. Springer, New York:

Spohrer JC (2018) Open Technology, Innovation, and Service System Evolution. ITQM 2018 Keynote, Omaha NE USA. 2018 Oct 20 URL: https://www.slideshare.net/spohrer/itqm-20181020-v2

Cite this blog post as: Spohrer JC (2018) Perspectives on Decision Support Systems: Summary of FG Filip’s Presentations. 2018 Dec 26 URL: https://service-science.info/archives/5066

Wishing everyone a relaxing/festive holiday season and productive/inspiring new year 2019. Please join us at HICSS 52 (Maui), Naples Forum (Ischia), Frontiers (Singapore), AHFE HSSE 2020 (San Diego) – where ISSIP sponsors best paper awards to advance our members professional development. Also, please submit on behalf of your organization a proposal for ISSIP’s Annual Excellence in Service Award (deadline: 2019 Jan 31).

Leaders

Leaders: If your organization is using the power of de-biased opentech AI to advance business and societal service innovations, please consider making 2019 the year you sign an MOU with ISSIP.org – announcement made in the monthly ISSIP newsletter. ISSIP is dedicated to advancing the skills and agile T-shaped competencies of individuals to succeed in the era of opentech AI for smarter/wiser service systems.

Deadlines

ISSIP-related upcoming deadlines that may be of interest…

(a) Jan 19, 2019: Abstracts due for Naples Forum on Service (Ischia, Italy June 4-7) – includes ISSIP’s best abstract award:
https://service-science.info/archives/5016
http://www.naplesforumonservice.it/public/index.php

(b) Jan 31, 2019: Get recognition for your favorite innovation program – ISSIP’s Annual Excellence award:
https://service-science.info/archives/5059
http://www.issip.org/recognitions/excellence-in-service-innovation-award/

ISSIP promotes professional development through awards programs for students, faculty, industry and consultant professionals, government policy makers, non-profits, etc. ISSIP is transdisciplinary, meaning members come from all disciplines, cultures, and industries/systems with an interest in the design and evolution of service systems, socio-technical systems that work to improve value co-creation and capability co-elevation of entities with associated rights and responsibilities.

Thank-you

Recapping 2018, a few people to thank:

Rama Akkiraju: Thank-you to 2018 President Rama Akkiraju (IBM Distinguished Engineer) who wrote (light edits): “My overall assessment of 2018 for ISSIP is that we did some good stuff in 2018 but have lots more stuff to do! :-). We brought an opentech AI focus to ISSIP and drove a couple of projects, namely AI datasets catalogue and servicesets evaluation framework). A blog post will be shared by the end of the year [will link to it here when ready]. This work will continue in 2019. In 2018, we continued all the good programs that were in place from previous years, namely: ISSIP Discovery Summits, Conference engagements (e.g. HICSS, AHFE HSSE, Frontiers, Naples Forum, et.), ISSIP Service Innovation award, Best Student paper awards, Business Expert Press Books, etc. Among the things to do, overall, ISSIP is still relatively unknown to most folks in the industry and academia. We still have lots of work to do to bring clarity on ISSIP’s strategic mission and target customers, to scale ISSIP and to increase its value proposition for its members. It will be a journey and it is time to pass the torch to the next leader. Thank you all for the opportunity to serve ISSIP in 2018!”

Yassi Moghaddam: Executive Director who runs weekly leadership meetings (our “shared” agenda), quarterly board of directors meetings (progress), discovery summits (community meetings), committees (from election committee to awards committees and more, Yassi keeps the committees on track), website, taxes, non-profit duties.

Haluk Demirkan:Board Member and ISSIP Business Expert Press Book Collection. Haluk is ISSIP’s Ambassador to the HICSS conference, and leads the best paper awards given there each year. Haluk also runs many of the student projects between ISSIP industry members.

Salvatore_Moccia: and Michele_Tomic: ISSIP News Editor-in-Chief and ISSIP News Editor, respectively, for ISSIP Newsletter http://www.issip.org/newsletters/ https://paper.li/e-1446674501#/!all

Many others thanked as part of our quarterly ISSIP Board of Directors calls open to all members.

Nice documentary video now available on Amazon Prime.

Today is Sunday December 9, 2018.  Fifty years ago at an early computer conference in San Francisco, a researcher from SRI (Stanford Research Institute) unveiled what he and his team had created – a view of the future of augmented human performance with advanced technologies in computing and communications.  Douglas C. Engelbart and team created what is famously known today as “the mother of all demos.”  The text I just typed, the hyperlinks I just used, the mouse I used for positioning and clicking, and the zoom on-line meeting that I participated in last week all owe an intellectual debt to Doug and his team.

Celebrating the event is the Computer History Museum, read more here.

Reflections

 In 1962, Engelbart offered a conceptual framework for augmenting human intelligence, which was the center piece of his life’s professional work (Engelbart 1962, Scanned Original).  In 1968, he showed the first implementation of a rapidly evolving augmentation system that he and his team at SRI had developed (Engelbart and English 1968). In 2003, he argued that investing in “an improvement infrastructure” was still much needed while speaking to a large audience at the IBM Almaden Research Center in San Jose, CA (Engelbart 2003).   In 2004, Doug and I collaborated on a paper, which was requested by colleagues from NSF (National Science Foundation), and our topic was enhancing human performance (Spohrer and Engelbart 2004).   In 2017, his conceptual framework was celebrated in a book by Daniel Araya, in which I was invited to contribute a chapter (Spohrer and Siddike 2017).Last week, Nick Ragouzis emailed me, suggesting some broader connections between Engelbart’s work and several exhibits at the Computer History Museum.  For example, (A) Vannevar Bush, Ted Nelson, Doug Engelbart and Hypertext exhibit, (B) Moore’s Law exhibit, and (C) Demo@50 exhibit.   Without going into all the details of purple numbers, I will just say that Nick has worked hard to clarify the nature of compounding rates of improvement possible in Engelbart’s more fully implemented improvement infrastructure vision.  Nick and others call this Engelbart’s Law.  Improvement rates in individual silos (e.g., technology – tool systems) is limited compared to improvement rates possible with integration (e.g., tool system and human system) that overcome organizational inertia.  The type of multidisciplinary thinking required for this would likely require an overhaul of the education system into a more integrated whole that considers the socio-technical system design loop (Kline 1995). So perhaps one broader area to highlight in the exhibits at the Computer History Museum Demo@50 event is  emphasis on the type of education and investment frameworks required to continue to evolve Engelbart’s conceptual framework.  Nick suggests some additional reading that I am eager to look at as well: (Engelbart and Engelbart 1995) and (Lienhard 1979, 1985).

Annotated Bibliography

(Engelbart 1962, Scanned Original) Engelbart DC (1962) Augmenting Human Intellect: A Conceptual Framework. Summary Report, Stanford Research Institute, on Contract AF 49(638)-1024, October 1962, 134 pages. Scanned Original, Unclassified AD 289 565, Reproduced by Armed Services Technical Information Agency, 1963 Jan 04. For annotations see below.

(Engelbart 1962, Doug Engelbart Institute Online Version) Engelbart DC (1962) Augmenting Human Intellect: A Conceptual Framework. Summary Report, Stanford Research Institute, on Contract AF 49(638)-1024, October 1962, 134 pages. Scanned Original, Unclassified AD 289 565, Reproduced by Armed Services Technical Information Agency, 1963 Jan 04. “By “augmenting human intellect” we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems. Increased capability in this respect is taken to mean a mixture of the following: more-rapid comprehension, better comprehension, the possibility of gaining a useful degree of comprehension in a situation that previously was too complex, speedier solutions, better solutions, and the possibility of finding solutions to problems that before seemed insoluble. And by “complex situations” we include the professional problems of diplomats, executives, social scientists, life scientists, physical scientists, attorneys, designers—whether the problem situation exists for twenty minutes or twenty years. We do not speak of isolated clever tricks that help in particular situations. We refer to a way of life in an integrated domain where hunches, cut-and-try, intangibles, and the human “feel for a situation” usefully co-exist with powerful concepts, streamlined terminology and notation, sophisticated methods, and high-powered electronic aids. Man’s population and gross product are increasing at a considerable rate, but the complexity of his problems grows still faster, and the urgency with which solutions must be found becomes steadily greater in response to the increased rate of activity and the increasingly global nature of that activity. Augmenting man’s intellect, in the sense defined above, would warrant full pursuit by an enlightened society if there could be shown a reasonable approach and some plausible benefits.” P. 1.

(Engelbart and English 1968) Engelbart DC, English WK (1968) A research center for augmenting human intellect. In Proceedings of AFIPS (American Federation of Information Processing Societies) Fall Joint Computer Conference, San Francisco,  December 9-11, 1968. Pp. 395-410). “1a This paper describes a multisponsor research center at Stanford Research Institute in man-computer interaction. 1a1a For its laboratory facility, the Center has a time-sharing computer (65K, 24-bit core) with a 4.5 megabyte swapping drum and a 96 megabyte file-storage disk. This serves twelve CRT work stations simultaneously. … 1a1b1 The “mouse” is a hand-held X-Y transducer usable on any flat surface ; it is described in greater detail further on. … 1b User files are organized as hierarchical structures of data entities, each composed of arbitrary combinations of text and figures. A repertoire of coordinated service features enables a skilled user to compose, study, and modify these files with great speed and flexibility, and to have searches, analyses data manipulation, etc. executed. In particular, special sets of conventions, functions, and working methods have been developed to air [sic aid] programming, logical design, documentation, retrieval, project management, team interaction, and hard-copy production.” P.395. “2b The research objective is to develop principles and techniques for designing an “augmentation system.” … 2c The research approach is strongly empirical. At the workplace of each member of the subject group we aim to provide nearly full-time availability of a CRT work station, and then to work continuously to improve both the service available at the stations and the aggregate value derived therefrom by the group over the entire range of its roles and activities. 2d Thus the research group is also the subject group in the experiment. 2d1 Among the special activities of the group are the evolutionary development of a complex hardware-software system, the design of new task procedures for the system’s users, and careful documentation of the evolving system designs and user procedures. 2d2 The group also has the usual activities of managing its activities, keeping up with outside developments, publishing reports, etc. 2d3 Hence, the particulars of the augmentation system evolving here will reflect the nature of these tasks – i.e., the system is aimed at augmenting a system-development project team. Though the primary research goal is to develop principles of analysis and design so as to understand how to augment human capability, choosing the researchers themselves as subjects yields as valuable secondary benefit a system tailored to help develop complex computer-based systems. 2e This “bootstrap” group has the interesting (recursive) assignment of developing tools and techniques to make it more effective at carrying out its assignment. 2e1 Its tangible product is a developing augmentation system to provide increased capability for developing and studying augmentation systems. 2e2 This system can hopefully be transferred, as a whole or by pieces of concept, principle and technique, to help others develop augmentation systems for aiding many other disciplines and activities. 2f In other words we are concentrating fully upon reaching the point where we can do all of our work on line-placing in computer store all of our specifications, plans, designs, programs, documentation, reports, memos, bibliography and reference notes, etc., and doing all of our scratch work, planning, designing, debugging, etc., and a good deal of our intercommunication, via the consoles. 2f1 We are trying to maximize the coverage of our documentation, using it as a dynamic and plastic structure that we continually develop and alter to represent the current state of our evolving goals, plans, progress, knowledge, designs, procedures, and data.” P. 395-396. “4 SERVICE-SYSTEM SOFTWARE 4a The User’s Control Language 4a1 Consider the service a user gets from the computer to be in the form of discrete operations – i.e., the execution of individual “service functions” from a repertoire comprising a “service system.” 4a1a Examples of service functions are deleting a word, replacing a character, hopping to a name, etc.” P. 402.

(Engelbart 2003). Engelbart DC (2003) Improving Our Ability to Improve: A Call for Investment in a New Future. Transcription of talk and presentation at IBM Almaden Research Center’s Co-Evolution Symposium September 24 2003, organized by Jim Spohrer and Doug McDavid.  “In this talk, Dr. Douglas Engelbart, who pioneered much of what we now take for granted as interactive computing, examines the forces that have shaped this growth. He argues that our criteria for investment in innovation are, in fact, short-sighted and focused on the wrong things. He proposes, instead, investment in an improvement infrastructure that can result in sustained, radical innovation capable of changing computing and expanding the kinds of problems that we can address through computing.” P. 1.

(Kline 1995) Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press. See summary (Campbell 2011) here.

(Spohrer and Engelbart 2004) Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy of Sciences. 2004 May 1;1013(1):50-82. 

(Spohrer and Siddike 2017) Spohrer J, Siddike MAK (2017) Chapter 2: The Future of Digital Cognitive Systems: Tool, Assistant, Collaborator, Coach, Mediator. In Augmented Intelligence: Smart Systems and the Future of Work and Learning, Ed. Araya D. (2017 Sep 28). Peter Lang Inc., International Academic Publishers. Pp. 40-61.  “Technology and organizations are two instruments that have been developed to augment the human intellect in order to make people smarter (Norman, 1993). In fact, Douglas Engelbart, an American engineer, and an early computer and Internet pioneer who invented the computer mouse, urged people to work quickly to “augment human intellect and address complex, urgent problems” (Engelbart, 1962, 1995). Herbert Simon, a Nobel Prize laureate and an American political scientist, economist, sociologist, psychologist, and computer scientist, took a mul- tidisciplinary approach to decision making and identified “bounded rationality” as a condition that technology and organizations could mitigate (Simon, 1997). Following these thinkers, we argue in this chapter that digital cognitive systems, known as cognitive mediators, may someday address a range of problems associated with “bounded rationality” (Simon, 1997), “knowledge burden” ( Jones, 2005), and “half-life-of-facts” (Arbesman, 2013).” P. 40-61.

Additional Readings:

Engelbart DC,  Engelbart C (1995) “Boosting Collective
IQ: For Quantum-Leap Improvement in Productivity, Effectiveness,
Competitiveness. A New Grand Challenge”. (July 28, 1995).

Lienhard JH (1979). “The Rate of Technological Improvement
before and after the 1830s”. Technology and Culture. John’s Hopkins
Press. 20 (3): 515–530. doi:10.2307/3103814. JSTOR 3103814

Lienhard JH (1985). “Some Ideas About Growth and Quality in Technology“.
Technological Forecasting and Social Change. Elsevier: 27, 265-281
(1985)

URLs:

Celebrating… read more here: https://www.computerhistory.org/atchm/net-50-did-engelbart-s-mother-of-all-demos-launch-the-connected-world/

Douglas C. Engelbart: https://www.computerhistory.org/fellowawards/hall/douglas-c-engelbart/

(Engelbart 1962, Scanned Original) … Augmenting Human Intellect: A Conceptual Framework: https://apps.dtic.mil/dtic/tr/fulltext/u2/289565.pdf

(Engelbart 1962, Doug Engelbart Institute On-Line Version) … Augmenting Human Intellect: A Conceptual Framework: http://dougengelbart.org/content/view/138/000/

(Engelbart and English 1968) … A research center for augmenting human intellect: https://profiles.nlm.nih.gov/ps/access/BBAIKM.pdf

(Engelbart 2003) … Improving Our Ability to Improve: A Call for Investment in a New Future: http://worrydream.com/refs/Engelbart%20-%20Improving%20Our%20Ability%20to%20Improve.pdf

Engelbart’s Law: https://en.wikipedia.org/wiki/Engelbart%27s_law

(Kim 2001) An Introduction to Purple: http://eekim.com/software/purple/purple.html

(Kline 1995) … (Campbell 2011) summary here: https://prod.sandia.gov/techlib-noauth/access-control.cgi/2011/114500.pdf

(Lienhard 1985) … Some ideas about growth and quality in technology : http://www.uh.edu/engines/qualitytechnology.pdf

the mother of all demos: https://en.wikipedia.org/wiki/The_Mother_of_All_Demos

Above dedication is from book chapter by (Spohrer and Siddike 2017).

Thanks to Rohith Jyothish and Anil Srivastava for pointers.

The first paper (Freeman 2018) present three laws of “robo-economics” – and is important to read carefully.  The pros and cons of profit sharing with employees is well explored in the second and third papers (Weitzman & Krause 1990; Krause 1986), especially from an incentives for full employment perspective. The fourth paper (actually book) (Pendleton 2002), is also very important to understand trends and challenges in employee ownership, governance, and participation.  Finally, the fifth paper (Errasti, Bretos, Nunez 2017) gives an analysis relevant to co-ops.  In addition, need some other perspectives…  co-ops include a component of profit sharing with customers – so some integration of this may also be worth exploring from a fuller co-creation of value perspective. As we get employees, managers, customers, and capitalists (owners) with large numbers of digital workers (say 100 digital workers per employee based on the number of apps on my smart phone today) and include the households these individuals are part of as well, the nested networked structure of the service system ecology might cause us to expect far more from individuals than we do today – just as we expect far more responsibility-taking from adults than children in society today (and age range of children at home status seems to be increasing today). Worth thinking about at least…

Annotated Bibliography

(Freeman 2018) Freeman RB (2018) Ownership when AI robots do more of the work and earn more of the income. Journal of Participation and Employee Ownership. (2018 Jun 11). 1(1):74-95. “To the extent that who owns the robots rules the world, it argues for a concerted social effort to widen the “who” in ownership from the few to the many. It reviews policies to expand employee ownership of their own firm and of the stream of revenue via profit-sharing and gain-sharing bonuses.” P. 74. “The natural solution to a distribution problem based on the unequal ownership of income earning AI robots and other capital assets is to expand ownership to a larger proportion of the population through increasing employees’ ownership of their firms and workers and citizens’ ownership of capital writ large. By ownership, I mean any of a diverse set of property rights over income-producing assets ranging from ownership of the capital, which gives employees or citizens’ rights to vote on economic and management decisions, to ownership of streams of income from capital, which give persons rights to the stream but not to the capital itself.” P. 83. “The USA has arguably the most extensive system of employee ownership and profit-sharing in the world, which gives the country a good base from which to adopt policies for increasing workers’ ownership as AI robots produce more of GDP.” P. 84. “Workers benefit from ownership by gaining higher incomes via shares or profit-related bonuses and by participating more in workplace decisions. Developing greater trust/loyalty to their firm, workers are more likely to stay with their employer than otherwise comparable workers without such plans and are more likely to monitor co-workers to keep productivity high (see chapters in Kruse et al., 2010). ” P. 85. “Capital income is more unequally distributed than labor income. The top 1 percent wealth holders have 35 percent of total wealth, which is about three times the share of the top 1 percent labor income earners in total labor income. Inequality in capital income has also increased more rapidly than inequality in labor income[42]. To the extent that AI robot automation raises capital’s share of income, it will add to inequality and accelerate the rising trend in inequality.” P. 87. “Sovereign funds – state owned investment vehicles that invest public moneys based largely on taxes and royalties from publicly owned natural resources such as oil and gas in real and financial assets – offer a different mechanism to spread capital wealth.” P. 88.

(Weitzman & Kruse 1990) Weitzman M, Kruse D (1990). Profit sharing and productivity. 95-142. “From many different sources there emerges a moderately consistent pattern of weak support for the proposition that profit sharing improves productivity.” P. 96. “The gains are probably modest, and perhaps it is a difficult change to engineer. A society‘s labor payment system seems to be one of the more likely candidates for historical inertia. institutional rigidities, and imitation effects.” P. 140.

(Weitzman 1986) Weitzman ML. Macroeconomic implications of profit sharing. NBER Macroeconomics Annual. 1986 Jan 1;1:291-335. “What we do not know – and this is the central economic dilemma of our time – is how simultaneously to reconcile full employment with reasonable price stability.” P. 291. “Senior workers who are not unduly at risk of being laid off might resist the plan.” P. 297. ” Here I would like to deal with some of the major objections that have been raised. The most effective way to do this, I believe, is to answer questions the way they are typically posed by astute critics.” P. 304. “In this article I have argued that substantial progress in the struggle for full employment without inflation will have to come largely from basic changes in pay-setting arrangements rather than from better manipulation of financial aggregates.” P. 332.

(Pendleton 2002) Pendleton A. Employee ownership, participation and governance: a study of ESOPs in the UK. (2002 Jan 4). Routledge. “This book has been a long time in the making. I first developed an interest in employee ownership at the beginning of the 1990s. Nick Wilson, then a colleague at Bradford University, was mainly responsible for introducing me to this field. We spent an enjoyable few months travelling the length and breadth of Britain collecting information on the early ESOPs. He was mainly responsible for assembling the questionnaire used to collect data on employee attitudes (see Chapter 8).” P. xi. “Our concern then is with a sub-set of share ownership schemes where employee share ownership is at high levels and where share ownership is intertwined with considerations of governance and participation.” P. 3. “In the main the employee ownership firms we focus on came about in two ways. One was where management and employees mounted buy-outs of public sector firms undergoing privatisation. The second arena for employee ownership conversions was the private company sector where the owner(s) wished to divest or exit.” P. 4. “The contemporary significance of this interpretation resides in the shift that is thought to be occurring in advanced economies towards a ‘knowledge economy’. Increasingly, wealth creation involves the application of human knowledge to the provision of services rather than the production of goods using physical capital. The critical investments therefore are those made in human capital. In these circumstances, the appropriate mode of governance is one involving employees in ownership and control. This line of argument is now filtering through into reformulations of the theory of the firm, and it has been proposed recently that the modern firm should be conceptualised as a ‘nexus of specific investments’ (Rajan and Zingales 1998). The other side of the coin is that firms need to find ways of binding employees with highly developed firm-specific knowledge to the firm so as to protect investments the firm has made in training and development. Employee ownership, both as remuneration and as a governance device, provides a way of doing this.” P. 8. “To these insights were added those of Jensen and Meckling a few years later. They also applied principal–agent theory to the theory of the firm, and analysed the problems of incentives and control based on asymmetrical distribution of information between principals and agents. They emphasised the monitoring costs for principals given that employees have superior information about many aspects of the production process, and the bonding costs to employees arising from the possibility that employers will in the future take most of the gains arising from long-term employment and the development of human capital.” P. 10. “Overall our conclusion is that studies of employee ownership need to pay close attention to the varying circumstances of ownership conversion, and to the objectives and philosophies of those involved in mounting the conversion. Variations in these are likely to be associated with differences in ownership, participation and governance.” P. 18. “Several observations can be made about the pattern of ESOP creation. One, there are virtually no cases where ESOPs have been used by start-up firms. There was just one such firm in our study, and this firm went out of business during the course of the research. ESOPs structures are not well suited to start-ups because they can be administratively onerous (in the view of our respondents) and because the demands of acquiring external finance for physical and working capital are likely to preclude the provision of financial resources to an ESOP.” P. 184. “An influential set of arguments suggests that decisions to become employee-owned may be relatively more likely in contexts where monitoring of worker activities is costly and where employees have transferable skills, knowledge, and reputation (Russell 1985b).” P. 191. [Note JCS: When employees have 100 digital workers working for them, this is very likely to be the case. Consider employees using and contributing to key resources in open source communities and open soure leader boards. Or does monitoring get easier, if privacy is less of a concern?]

(Errasti, Bretos, Nunez 2017) Errasti A, Bretos I, Nunez A (2017) The viability of cooperatives: The fall of the Mondragon cooperative Fagor. (2017 Feb 02). Review of Radical Political Economics. 49(2):181-97. “The viability of workers’ cooperatives as alternative work organizations in capitalist economy has long been a point of debate. Globalization brings new challenges to their survival as businesses and as democratic organizations. This article presents a case study of the rise and fall of the Basque cooperative Fagor Electrodomésticos, one of the largest industrial cooperatives in the world. Fagor, founded more than fifty years ago, played a key role in launching the several cooperatives that led to the creation of the Mondragon Corporation.After years of intense international growth, the local cooperative Fagor had been transformed into a multinational corporation competing in the highly concentrated and increasingly global home appliance market. In 2007 it employed around 11,000 workers inits eighteen production plants distributed across six countries. Then, as a result of a combination of external and internal factors,Fagor facedits most severe crisis, one
that eventually brought about its closure in 2013. Given Fagor’s role as a leading cooperative, the general question of the viability of workers’ cooperatives is also at stake in its failure. Recounting the story of Fagor’s rise and fall and examining its causes is therefore of broad significance.” P.2. “It finally failed in the midst of a very severe economic downturn that also brought down many other Spanish and European companies. Furthermore, most of Fagor’s members have already been relocated to other Mondragon cooperatives, which would be practically in conceivable in capital – owned companies. Fagor’s failure, then, says less about the viability of cooperatives than about the risks inherent in actual market economies: any business may fail, whatever the size, the juridical nature or the corporate and institutional support. There remain over one hundred cooperatives of Mondragon group , continuing to exhibit a strong capacity for growth and long – term survival, contradicting the verdict of the Webbs.” P. 18.

URLS:
(Freeman 2018)
https://www.emeraldinsight.com/doi/full/10.1108/JPEO-04-2018-0015

(Weitzman & Kruse 1990)
http://scholar.harvard.edu/files/weitzman/files/profitsharingproductivity.pdf

(Weitzman 1986)
https://www.jstor.org/stable/pdf/3585175.pdf?casa_token=9pK-bgWzk0YAAAAA:7ThJCgHrGYC8UcuRaW3FeEWLMBCY4wjKny54IFhAO9lijmyLtXOIDgdB5TcITOB-GSdo1eA_ZCrB50uFr-uPS9CoSIzGNwLWkUseB8L17-mHJ8VpE-0

(Pendleton 2002)
http://library.uniteddiversity.coop/Money_and_Economics/Cooperatives/Employee_Ownership_Participation_and_Governance-A_Study_of_ESOPs_in_the_UK.pdf

(Errasti, Bretos, Nunez 2017)
https://www.researchgate.net/profile/Ignacio_Bretos/publication/313292439_The_Viability_of_Cooperatives_The_Fall_of_the_Mondragon_Cooperative_Fagor/links/5a2027470f7e9bfc48fdfa4e/The-Viability-of-Cooperatives-The-Fall-of-the-Mondragon-Cooperative-Fagor.pdf