This NYT article is worth a read:
http://www.nytimes.com/2014/12/16/upshot/as-robots-grow-smarter-american-workers-struggle-to-keep-up.html

Fears being fueled by Bostrom’s book “Superintelligence” as well as Elon Musk and Stephen Hawking’s remarks.
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat
http://www.huffingtonpost.com/2014/05/05/stephen-hawking-artificial-intelligence_n_5267481.html

Example, so-so attempt at rebuttal (a bit tongue in check in part, but with some more serious undertones)
http://www.wired.com/2014/12/armageddon-is-not-the-ai-problem/

Attempts to dispel the threat are hard because:
species and civilizations come and go, for many reasons
every technology can be used for good and bad
this time may be truly different, after the threshold where technology re-builds itself, on exponential change curves

The arguments to address the fear, so far have been one or combination of:
the threat is still far off, and short term benefits outweigh long-term risks – we can manage this
we can probably design friendly AIs – we can manage this (Bostrom’s plea/resolution at end of his book)
we will co-evolve with the new tools – augmented intelligence (“Advanced Chess” example of teams and tools)
we will merge with the new tools (Kurzweil – creepy augmented intelligence)
something else is more likely to kill us off first, and we need AI to work on those complex urgent problems

It is easy to get involved in the Cognitive Systems Institute Group.

Some resources and background to share with faculty who are looking for alignment opportunities:

General resources to share with faculty can be found at this website, and discussions on LinkedIn group:
Cognitive Systems Institute Group website: http://www.cognitive-science.info
Cognitive Systems Institute Group LinkedIn discussions: https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452

More specifically regarding awards, which are more competitive, and go to the most aligned projects, our global university programs team is making small IBM awards to faculty who are submitting larger proposals for government/foundation funding that are aligned with IBM.

Criteria include:

(1) Focus: Here is what is being ramped up.   Awards to faculty working on building cognitive assistants for some occupation as part of a smart service systems, or the cognitive computing componentry underlying those cognitive assistants (i.e., focus)
See – http://cognitive-science.info/award-recipients/

(2) Leverage: Here is what the review board is weighting heavily, besides good people doing good work in good places, the dominant criterion is leverage. Restricting to faculty who are pursuing significant follow-on funding from government or foundation funding agencies (i.e., leverage).
See – http://cognitive-science.info/about-grants/where-to-apply/

(3) Ecosystem Outcomes:  Potential for increase in startups built on the Watson Bluemix platform, and university cognitive computing componentry that is accessible as part of Bluemix.  For example, see these cognitive computing componentry (Watson Services on Bluemix):
See – https://ace.ng.bluemix.net/#/solutions/solution=watson

(4) Research Outcomes: Similar to ecosystem outcomes (components on Bluemix), but also includes co-publications with IBM Researchers regarding progress on cognitive research grand challenge.
See – http://cognitive-science.info/research-challenges/debater/
http://cognitive-science.info/research-challenges/
https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452
http://cognitive-science.info/

In sum, we are most interested in universities that are developing cognitive computing componentry and cognitive assistants that solve cognitive research grand challenges, grow the Watson Services on Bluemix ecosystem, leverage government/foundation funding sources, and can be seeded with small IBM awards in our focus area.  We are interested in other win-win’s as well, but this is our focus for university cognitive awards.

From Steve Kwan, this update:

As some of you already know, Managing Service Quality (MSQ) will be retitled, becoming the Journal of Service Theory and Practice (JSTP) from the next volume (2015). This is now reflected in some of the journal content, including the website portal via which manuscripts are submitted. Please continue to submit your papers intended for MSQ through ScholarOne as before, although you will now be doing so via the JSTP page. You will be automatically directed to this page. If not, please use the following URL: http://mc.manuscriptcentral.com/jostp

We will provide you with more information regarding the change in the coming weeks. We will also fully explain the rationale for the change of title in the first issue of the next volume.

Thank you for your continued support for the journal.

Chatura Ranaweera and Marianna Sigala
Co-Editors, Journal of Service Theory and Practice JSTP (formerly MSQ)

For the upcoming Service Science PIC workshop at Almaden includes the full breadth of service science related themes:

Service Design & Industry Solutions
Service Delivery & IT as a Service (Cloud, Mobile, Social, Security)
Service Analytics & Big Data/Internet of Things (Web Services)
Smart Service Systems & Cognitive Computing
Service Science Foundations & Mathematical/Simulation Modeling of Service Systems/Sociotechnical Systems
Service Ecosystems, Platforms, Business Models, and Governance Systems
Human-Side of Service Systems Engineering and User/Customer Experience
Professional Associations for Service Researchers/Practitioners/Etc (ISSIP, INFORMS, IEEE, ACM, AIS, AMA, etc.)

Significant groups of faculty, students, and practitioners in professional associations do research on all the above topics.

INFORMS Service Science Journal reflects these diverse themes through the study of service systems.

NSF funding of Smart Service Systems reflects these themes as well.

The next NSF workshop will be at MIT and focus on smarter service systems and transdisciplinary research.

The last NSF workshop on service science focused on the research agenda for service innovation.

The umbrella professional association the links together other professional associations for service researchers is ISSIP = International Society of Service Innovation Professionals, and Jeff Welser (VP, IBM Research – Almaden) will be ISSIP President in 2015.  Charlie Bess (HP Fellow) is ISSIP President 2014.   Ammar Rayes (Cisco DE) was ISSIP Founding President in 2012-2013.

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The 10th Annual Meeting of the

Service Management and Science Forum

June 11-13, 2015

Bentley University
Waltham, MA

Theme

Co-creating the Customer Service Experience with High Tech and High Touch

Meeting Announcement:

The Service Management and Science Forum is a truly transdisciplinary meeting involving academics and practitioners from all disciplines and organizations that focus on service delivery processes and the service systems that support them. The conference has attracted a number of established researchers across operations, marketing, information technology, design, engineering, and human resource management from domestic and international higher education institutions and businesses.

In today’s highly competitive environment, there is a growing emphasis to providing customers with a truly memorable experience as a way to increase both customer satisfaction and long term customer loyalty. The customer service experience resulting from the interaction with the service provider requires a combination of high tech and high touch, which depends on the type of service being provided and therefore is generally heterogeneous by its nature.  For instance, experience at Disneyland theme park is largely different from experience at Apple or Microsoft stores. Technology and service design have pushed customer experience management towards a new era.

Information for Contributors:

Individuals from academia, business and government are invited to submit refereed research papers, non-refereed research abstracts, and proposals for workshops, panels, and symposia. All submissions should have a clear focus on enhancing the customer’s experience and are encouraged to be transdisciplinary in nature; that is, they should involve more than a single traditional discipline.

Submission Deadlines:

The submission deadline for refereed research papers is February 15, 2015. The submission deadline for non-refereed research abstracts and proposals is March 15, 2015.

Additional details about the 2015 Forum will be forthcoming. In the interim, please mark the dates on your calendar and for more information please contact:
Program Chair:

David Xin Ding
University of Houston
Houston, TX 77004
Tel: 1-713-743-4095
Email:  xding@Central.UH.EDU

General Chair:
Mark M. Davis
Bentley University,
Waltham, MA 02154
Tel: 1-781-891-2739
Email: mdavis@bentley.edu

The Cognitive Systems Institute is a virtual institute to support global university, government, industry, and foundation collaborations in the area of next generation cognitive systems.   The vision is to augment and scale human expertise with cognitive assistants for all occupations in smart service systems.

A key question: How have researchers gone about augmenting themselves and their teams with powerful tools?

Moving up the abstraction tree one level beyond “custom analytics tools” to “general purpose cognitive assistants” for all occupations.  The vision is to augment and scale human expertise with cognitive assistants for all occupations in smart service systems, where all occupations is a moving target and O*NET OnLine is a first approximation of nearly a thousand occupations, many with task breakdowns, such as biochemical engineers. I have a short presentation to explain this a bit.  Cognitive assistants that have ingested all the literature in a field, and know the publications, talks, etc. of a person or team can be very useful for creativity and productivity boosts.  What architecture will allow industry and academia to collaborate and take on this grand challenge? How can we make rigorous and systematic the path to building and improving these systems, so that government, foundations, and industry can allocate more investment in these areas?

Some other institutes that study cognitive systems are:

Germany:

Institute for Cognitive Systems – TUM
www.ics.ei.tum.de/
Technische Universität München
The Institute for Cognitive Systems deals with the fundamental understanding and creation of cognitive systems. As our research interests fall in line with the …

Greece:

Cognitive Systems Research Institute (CSRI)
www.csri.gr/
The Cognitive Systems Research Institute (CSRI) is a research organisation, in Athens, Greece. The institute establishes highly interdisciplinary research teams …

Canada:

ICICS
www.icics.ubc.ca/
University of British Columbia
The Institute for Computing, Information and Cognitive Systems (ICICS) is a multidisciplinary research institute that promotes collaborative research in advanced …

Building cognitive assistants is hard work.  How might we make it easier by working together?

1. Do the benefits of  building cognitive assistance for boosting creativity and productivity justify the costs?
http://www.slideshare.net/spohrer/cognitive-20140912-v3

2. What occupations might provide the best ROI?
http://www.onetonline.org/

3. Where can one join discussions about this topic?
https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452

4. Where can documents and other information be shared?
http://www.cognitive-science.info

5. Can cognitive assistants contribute to smart service systems?
https://www.linkedin.com/groups?home=&gid=5109582

6.  Given that some faculty are expert at creating textbooks (e.g., chapters introducing concepts and relationships, case studies illustrating concepts and relationships, problem sets with questions and answers, etc.), how can this existing faculty expertise be shifted or transformed with appropriate tools to make building cognitive assistants easier?

As I research the history of cognitive assistants, these blog posts by Franz Dill and one more by D.J. Powers are interesting, relating to executive information systems to help CEO’s in the pre-spreadsheet era of computing, as well as executive decision support systems:

Early Business Intelligence Needs
http://eponymouspickle.blogspot.com/search?q=brad+butler

A Slice of the History of Executive Information Systems
http://eponymouspickle.blogspot.com/2011/03/slice-of-history-of-eis.html

A Brief History of Decision Support Systems
http://dssresources.com/history/dsshistoryv28.html

Broadly, Franz is interested in the topic of how the executive (and every decision maker, beyond the analyst/research), uses data, analytics, and intelligence systems to make better decisions.   Cognitive assistants with 3 L’s capabilities – language, learning, and levels (of confidence) – provide a next step and and new set of pathways to explore.

Franz emailed me about his work at P&G developing and deploying early advisory systems – “The consultant to all of our advisory systems in the 90s was Stanford and Teknowledge.   Using Prolog style systems, like M1.  Edward Feigenbaum and others were involved.  The most famous of our systems was the Coffee Blending advisory system,  which was used by green coffee blenders, and in use for the next decade, saved P&G millions.   Another famous system was the Copy Owl, which let Ad experts, use, reuse, modify and apply company advertising assets.  We even played with learning systems, I wrote an early neural net based induction system that matched advertising campaigns to new product initiatives.  A related system gathered cases and then used case based reasoning (CBR) to find best fits.   It was used for several years, then the task was outsourced.   Another system adapted or ‘learned’ supply chain solutions from traffic and inventory data. Early big data.   Except for the four examples of executive systems I sent you, and to a certain extent the Copy Owl, these were all used by corporate experts to augment their own specialty expertise.  Sometimes to replace their own expertise for easier problems and also often to scale their expertise more broadly.”  

Clearly some of these systems worked better than others, and their fates depended on many factors.  Franz is also interested in why some worked for a while, some did not work,  and how they could have been made to work in a specific corporate executive culture.  One comment that stuck with him was when an executive said something like “I am most interested in useful creativity, not keeping alive the expertise of past executives, even my own …. “.  So successful cognitive assistants will require more than just the expert system processes of accurate expertise capture … more effort is needing in trying to find the true dividing line between creativity and handling routine tasks efficiently.   Cognitive assistants have to be collaborative partners that handle routine tasks, but also engage in natural and creative exploration of new ideas and possibilities.  

Franz also commented that “What ultimately worked was extreme focus.  Narrow understanding of what was needed to be done, simple or complex.   And making sure the user (exec or analyst) was willing to follow the lead.  And their organization also would follow the recommendation.  And that the data involved was trusted for the given purpose by all …  All of this worked better with focus.    In three years we did perhaps thirty projects,  only three in the executive space. Many other projects in marketing, manufacturing, R&D, Supply Chain, HR, Sales … With a wide variance in success.  The successes paid for the effort, but did not sustain the AI function.”

The three systems in the executive space were part of a C-Suite Advisory Development effort at P&G from 1989-1991:

Automated CEO – lead by Bob Herbold (Later COO at Microsoft)
New Initiative Advisor – lead by Tom Moore
Major Capital Appropriation Screener  – lead by Bob Hunt

Across the industry it has been estimated the annual spending for artificial intelligence projects in industry was reaching $1B with nearly ten thousand employees, both the major firms and startups were experimenting with better AI tools and techniques, decision-support systems, advisory systems, performance-support systems, and a variety of other intelligent systems, but none with the language, learning, and levels (of confidence) capabilities of the cognitive assistants that are beginning to appear today.

Smart service systems will depend increasingly on the people inside and outside them equipped with cognitive assistants.   The ability to serve external and internal customers in the business world will likely depend on cognitive assistants more and more, as our tools come to know us.

See “A Brief History of Cognitive Assistants:” for more in this ongoing discussion:
https://www.linkedin.com/groups/very-brief-history-cognitive-assistants-6729452.S.5916323207719182338

Jim Spohrer DRAFT  05/25/2014 07:13 AM

Cognitive Systems for Every Profession

The Dictionary of Occupational Titles (DOT) contains hundreds of short paragraph description of occupations.  For example, Architect (001.061-010):

Researches, plans, designs, and administers building projects for clients, applying knowledge of design, construction procedures, zoning and building codes, and building materials: Consults with client to determine functional and spatial requirements of new structure or renovation, and prepares information regarding design, specifications, materials, color, equipment, estimated costs, and construction time. Plans layout of project and integrates engineering elements into unified design for client review and approval. Prepares scale drawings and contract documents for building contractors. Represents client in obtaining bids and awarding construction contracts. Administers construction contracts and conducts periodic on-site observation of work during construction to monitor compliance with plans. May prepare operating and maintenance manuals, studies, and reports. May use computer-assisted design software and equipment to prepare project designs and plans. May direct activities of workers engaged in preparing drawings and specification documents.

A system of professions exist in business and society (Abbot 1988).  Professions have jurisdiction over types of problems and their solutions, and reflect the division of expert labor.   All professions compile a body of knowledge and practices that define them, and university faculty extend and teach that codified knowledge.

We are at the dawn of an era where every professional will have one or more associated cognitive systems (see Appendix I below).  Cognitive systems ingest massive amounts of data, learn, permit natural language interactions, and provide levels of confidence in their recommendations.

In the era of smart system of systems, cognitive computing and other advances at long last make it possible to have cognitive systems capable of being true cognitive assistants.  Anyone lucky enough to have (or had) a great executive assistant knows the amazing boost for measures like productivity, quality, compliance, and innovativeness.

Productivity: From travel to organizing meeting and emails, to finding that presentation or contact from last year or three years ago.

Quality: Organizing and integrating feedback on draft documents, presentations, to following-up with thank-you’s and closing the loop.

Compliance: Any organization runs on a dozens of details, annual reports, and double checking compliance with process, procedures, policy and even regulations..

Innovativeness: By helping on all the above items, there is more time to think and interact with others on new topics of potential future value.

Up to this point in history, most people have never had the benefit of a great executive assistant, so they have to do-it-all-themselves, adopting good organization skills and disciplines to stay on top of everything needed in their professional and private lives.   A very few people have been lucky enough to afford both executive assistants at work and in professional/public life, and personal concierges in their home/private lives.   Because most people without personal assistants have both public and private roles, instead of dedicated assistants for each role, associated with each societal role, they have colleagues, friends and/or family who lend a hand to help out when needed.  Trusted relationships provide needed assistance for most people.

The best assistants also help those they serve “to up their games” and adopt better organization skills and disciplines over time.   Assistants who allow those they serve to remain scattered and undisciplined can “cover it up” for a shot period, but ultimately without elevating the efficiency and effectiveness of those they serve, growth, development, innovation, and advanacement suffer.  Like good coaches, good assistants understand that in the long run creating debilitating dependency is not what excellent service is all about, but instead growing capabilities on both sides of the service relationship is what leads to sustainable relationships and rewarding lives.  Anything less is merely transactional, not relational.  The exception, of course, is end of life assistance, where capabilities of the one being served dwindle over time, and the assistant must do more and more right up to the end.

In this short essay, the focus is “ProfessionCogs” designed to augment the intelligence of doctors, lawyers, engineers, journalist, and other professionals, however first let’s understand “Cogs” a little better.

Cogs: Learning, Language, and Levels

“Cogs” are human-made cognitive systems capable of being true cognitive assistants.  Cognitive assistants have three types of basic capability that we can summarize as learning, language, and levels.   We expect our cognitive assistants to not only remember the past, but learn from experience as well.  Furthermore, we expect to interact with cognitive assistants in natural ways, in written language at a minimum and ideally in spoken language with ability to use prosody, gestures, and even facial expressions to convey meaning naturally in multi-person interaction contexts, such as meeting and conference calls.  Finally, cognitive assistants must help us weigh alternatives and understand the level of confidence associated with different possibilities and recommendations.

So learning, language, and levels get cognitive systems to the point where they can be really useful.  For example, IBM’s Jeapordy! winning Watson system demonstrated all three capabilities.  Learning from previous correct and incorrect responses (training data), using a broad range of natural language terms and phrases (Wikipedia-type breadth), and levels of confidence in its responses (shown to the audience during the competition).  It is interesting to note that while no single IBMer could defeat the two most winning Jeopardy! players, the Watson Jeopardy! system, a very specialized “Cog,” which was developed by IBM and several university partners, could and did win.  “Cogs” can do some things that their developers or owners cannot do.

Cognitive Systems: A Diverse Set of Entities

In general, the set of all cognitive systems is a very broad set of entities. The term itself “cognitive systems” is somewhat hard to define clearly, simply, and precisely.   The set of all cognitive system entities that one could study include both biologically evolved entities as well as human-made, or artificially intelligent entities.   Cognitive science, the interdisciplinary field that studies the mind and it processes, includes researchers with expertise in both the evolution and function of brains as well as the design and practical applications of increasingly sophisticated artificial intelligence componentry.   Both fields provide methods for studying the physical structures that give rise to diverse cognitive processes (functions and behaviors).   Cognitive science and artificial intelligence are both also concerned with collective capabilities of interconnected networks of simpler cognitive systems that form larger structures – from social insects to cities to multi-agent systems of flying smart robots.

ProfessionCogs for Diverse Professions

In large organizations, ranking the performance of individuals and teams is a common practice.  From the best performers in a role to the weakest performers in a role, quantitative and qualitative assessments are used to evaluate performance.   Those with the lowest ranking have the greatest potential for improved performance, and therefore boosting their performance or replacing them with more qualified employees can be key to improving overall organizational performance.

Making Progress

Which brings us to the question of what is the best way to make progress going forward?

Definitions of cognitive systems make reference to human-level capabilities, such as the abilities to sense and respond to the world in intelligent ways.   However, cognitive science and artificial intelligence researchers include ants and thermostats as examples of cognitive systems, because they sense and respond to their environment.

Human-made systems with capabilities to sense and react to their environment have been around for years.

In sum, ProfessionCogs are cognitive systems/cognitive assistants designed to help professionals do their jobs better.

Further readings

There is a lot out there.  And more all the time.

Abbott, A. (1988). The system of professions: An essay on the division of expert labor. University of Chicago Press.  URL: http://psycnet.apa.org/psycinfo/1988-97883-000

====

Appendix 1: By Stephen Hamm, Cognitive Computing Overview

Cognitive Computing for Smarties, by Stephen Hamm

What is cognitive computing?

Cognitive computing enables the next level of partnership between people and computers to augment human intelligence, boosting the productivity and creativity of individuals and teams, thereby transforming industries and professions.

These systems ingest vast amounts of data, learn from their interactions with people and information sources, reason about their their level of confidence in derived knowledge, and interact using language and other means that are more natural to us.

Cognitive computing techniques provide the building blocks for iteratively developing increasingly sophisticated systems which help us to make better, faster decisions in our personal and professional lives.

The science behind cognitive computing:

To demonstrate cognitive computing in action, IBM built the now famous Jeopardy! TV game show winning machine known as Watson.

This history-making machine is capable of searching encyclopedic collections of information for potential answers to questions, ranking answers based on its confidence level in them, and pressing a button if it has enough confidence in its top-rated answer—all in less than 3 seconds.

IBM scientists worked for years to combine and innovate techniques from a number of computer science-related fields including machine learning, data mining, natural language processing, knowledge representation, text-to-speech synthesis, operations research, decision-making, game theory, cognitive science, psychology, linguistics, and more.

In universities, scholars typically pursue these fields in relative isolation. The Watson breakthrough came, in part, because IBM scientists and engineers combined the disciplines in new ways with a grand challenge goal always in mind.   Nevertheless, the breakthrough would not have been possible without the assistance of university researchers, and open frameworks such as Apache UIMA.

The annals of artificial intelligence, now include three game-playing machines from IBM: Watson Jeopardy! (2011), Deep Blue (Chess, 1997), Samuel’s Checker Player (1956).

However, the best is yet to come.

Cognitive systems will be able to…

–Understand multiple languages.
— Reason about levels of confidence in their derived knowledge.
–Converse with people in spoken dialogues.
– Multimodal interactions (see, gaze, facial expressions, hear, small, taste, touch, feel, empathy)
–Understand how professionals think—such as doctors and lawyers.
— Understand facial expression, voice, sensory information, and build deeper user models
–Help people make better decisions, learn complex material faster, make discoveries and create new knowledge.

And, they will get smarter over time.

Of all these capabilities, learning is key.

Like people, cognitive systems exhibit three types of learning over time.

Optimization: Learning to use existing knowledge more efficiently for specific tasks.

Education: Learning from other knowledge sources, people, books, the web, and other cognitive systems.

Discovery: Learning new surprising derived knowledge.

For now, most of the algorithms (cognitive computing techniques) for optimization, education, and discovery are provided manually by research scientists and engineers programming machines.   However, as the amount of knowledge in machine-readable form grows, knowledge itself with become a new form of big data for cognitive systems to use to derive new knowledge and algorithms in partnership with people and organizations that can benefit from this new knowledge.

As cognitive computers for all professions get smarter over time they will help people improve their performance as well.   With cognitive systems everyone can eventually have a combined expert tutor and cognitive assistant.   As professionals exhibit new best practices, their cognitive assistants will notice and learn, ultimately contributing to the body of knowledge for every profession.

In sum, cognitive computing enables the next level of partnership between people and computers to augment human intelligence, boosting productivity and creativity of individuals and teams, thereby transforming industries and professions.

Appendix II: Building Useful “Cogs” Is Hard Work, But No Longer Impossibly Hard

Why now?  Over the years, AI (Artificial Intelligence) researchers have come to appreciate just how hard the problems they have been working on actually are.  Overly optimistic claims in the past have contributed to so called “AI Winters” where funding dried up formost  AI proposals.  So rightly so, many skeptics are asking “Why Now?” to the claims that an AI Renaissance is underway.   Building these systems is still very hard work, but no longer impossibly hard.

Building “Cogs” (cognitive systems/cognitive assistants) that can answer questions is hard work.  However, for the first time in history, Linked Data on the web makes it feasible for many tasks.  As the amount of Linked Data on the web increases, some of the traditional very hard natural language processing tasks that early AI (artificial intelligence) systems attempted can be addressed.  The WWW’s Linked Data provides a practical solution to several early AI problems, such as (1) comprehensive online data sets, (2) knowledge representation at scale, (3) combinatorial explosion of inferences, and (4) insufficient memory or compute resources.

Jeopardy! is an interesting test of natural language and breadth of knowledge.  The correct response is always an entity (in the form of a question “What is X?”) that fits within a category (e.g., “Famous People”) and is referred by a clue.  In the age of Wikipedia, Wiktionary, Wordnet,Web Pages, and Linked Data, the words and phrases in the typical clue link to documents which contain the correct response in most cases within one hop, or in some cases a relatively small number of hops – three to five.  The diagram below illustrates what is meant by a “hop” from one web-page to the next (see Wiki Linked_Data for a better understanding).   Linked Data is a practical solution to the “combinatorial explosion” problem faced by early AI (Artificial Intelligence) systems.  Sometimes the correct response may require finding a set and performing some linguistic or numeric “Calculation” (e.g., Jeopardy Category: Letters of the Alphabet, Clue: Most Common State Name First Letter; Correct Response “M”).  This procedural knowledge common in word puzzles is part of what make Jeopardy! challenging for contestants.    Most Jeopardy! correct response includes an entity for which there is a web page, and a small number may include an entity for which there is no web page, such as result of a linguistic or numeric “Calculation.”

In multiple-choice grade level reading comprehension tasks, the correct response is most often an entity that is directly referred to in the answer passage, or in a web page document linked to a word or phrase in the answer passage by one hop, or in some cases a relatively small number of hops – three to five.  Again, Linked Data is a practical solution to the “combinatorial explosion” problem.

Professional certification can be thought of as grade level reading comprehension for a very long answer passage (one or two dozen books).

Professionals or experts are also often called on to present arguments for and against multiple future options.   Unlike tasks that require finding a single entity answer, there is no single best or right answer, only a list of possibilities with pro/con statements of support rank ordered for relevance to each option.  For example, expert debaters perform these types of tasks when they research issues and build cases.  Lawyers also perform these types of tasks.

Also, unlike tasks that require finding a single entity response, some tasks require summarization of events, meetings, publications, bodies of research.  For example, journalists are often confronted with these tasks, or textbook writers creating material that is grade-level appropriate.

Problems in pattern recognition and robotics are also still very hard, but many specific tasks are no longer impossibly hard.

Appendix III:  Non-Technical Issues Are Also Hard Work, But Not Impossibly Hard

As the range of tasks that cognitive systems can address there are three types of non-technical concerns: (1) threats to privacy, safety and security (??? by governments, businesses, criminals, terrorist, etc.), (2) threat to job security (Byrnjolffson & MacAfie,), (3) threat to species security (Weizenbaum, Joy, etc.).   Based on the law of comparative advantage, getting all entities to “up their game” creates the most individual and collective benefits.