Types of Jobs and Work

In the future, as is true today, there are number of types of jobs and work.

Enterprise worker: Working for an enterprise that pays you – this can be like working for IBM, the US government, MIT, the Red Cross, or other organizations. O*NET lists the roughly 1000 types of occupations in these organizations, and with the rise of AI – organizations will employ people who know how to use AI to augment their performance and take responsibility for a set of outcomes and processes within the enterprise. See O*NET.

Platform worker: Self-employed entrepreneurs (e.g., founders of your favorite startup) and gig economy workers (e.g., your favorite Uber driver) who depend on customers, venture capital firms, as well as enterprises with platforms that help people gain an income through the use their platform or other offerings. See sole proprietorship, entrepreneurship and temporary worker/gig economy.

The above two types of work depend on large enterprises including large corporations to exist. For example, the Forbes Global 2000 enterprise workers as well as self-employed platform workers that depend on a large enterprise. The next two are less dependent on large enterprises and corporations.

Appropriate technology worker: A worker who is able to provide for self and dependents through knowledge of the built-environment and how to maintain it. For example, getting energy from windmills that one knows how to maintain. Recycling and re-using materials are in this category of work. Abilities both to use and maintain are key for sustainability. See DIY and appropriate technology.

Primitive technology worker: A worker who is able to provide for self and dependents through knowledge of the natural environment. Hunter gathers and farmers have traditionally been in this category, but use of local AI devices is changing the nature of this type of work. See maker culture and primitive technology.

These are the types of jobs and work that have existed for a long time, and now they are changing through the use of local AI – a type of technology that augments human intelligence, including what to do and how to do things.

No Coding Required

An IBM colleague recently sent me a pointer to an interesting article about how scientists and researchers are increasingly using familiar tools like Mathematica to get the benefits of AI/ML/DL with no coding required (Hutson 2019).

At IBM CODAIT (Center for Opensource Data and AI Technologies) making AI/ML/DL more accessible to data scientists and AI developers is part of an on-going mission to democratize AI in the enterprise, for business and society (Bommireddipalli 2018). The Model Asset eXchange (MAX) and the Data Asset eXchange (DAX) are great places to start. The developers of AI-powered applications need AI models and data scientists need data – so MAX and DAX provide good starting points.

Of course, with a little bit of programming experience one can do even more!

References

Bommireddipalli V, Fu MM, Holt B, Malaika S, Singh A, Spohrer J, Truong T (2018) Open source and AI at IBM. 20181212 URL: https://developer.ibm.com/blogs/open-source-ibm-and-ai/

Hutson M (2019) No coding required: Companies make it easier than ever for scientists to use artificial intelligence. “… software program, Mathematica, added machine learning tools that were ready to use, no expertise required. He began to play around, and realized AI might help him choose the plausible geometries for the countless multidimensional models of the universe that string theory proposes. …. One of the latest systems is software called Ludwig, first made open-source by Uber in February and updated last week. Uber used Ludwig for projects such as predicting food delivery times before releasing it publicly. Ludwig can train itself when fed two files: a spreadsheet with the training data and a file specifying which columns are the inputs and outputs. Once it learns to recognize associations, the software can process new data to label images, answer questions, or make numerical estimates. At least a dozen startups are using it, plus big companies such as Apple, IBM, and Nvidia, says Piero Molino, Ludwig’s lead developer at Uber AI Labs in San Francisco, California.”  20190731 URL: https://www.sciencemag.org/news/2019/07/no-coding-required-companies-make-it-easier-ever-scientists-use-artificial-intelligence

HCIS 2020: Call for Papers – Human-Centered Intelligence Systems

HCIS 2020: 14th International Conference on Human-Centered Intelligent Systems
June 17-19, 2020 | Split, Croatia

Register here: http://hcis-20.kesinternational.org

Part of KES Smart Digital Futures (SDF 2020) http://sdf-20.kesinternational.org

Aim

Contemporary advances in the field of artificial intelligence have led to a rapidly growing number of intelligent services and applications. Artificial intelligence is often characterized in an impersonal way: on this view, intelligent systems operate entirely independently of human intervention. Public discourse on ‘autonomous’ algorithms which work on ‘passively’ collected data contributes to this view. However, this perspective obfuscates the extent to which human work necessarily underpins and enables contemporary AI systems. The human element in intelligent systems includes tasks such as optimizing knowledge representations, developing algorithms, collecting and labeling data, and deciding what to model in the first place. Investigating artificial intelligence from a human-centered perspective requires a deep understanding of the role of human ethics, practices, and preferences for the development of—and interaction with—intelligent systems.


Dates, Conference and Publication

  • Submission Deadline: 10 January 2020
  • Acceptance Notification: 10 February 2020
  • Camera Ready Submission: 10 March 2020
  • Presentation at Conference: 17-19 June 2020
  • Publication: Springer; Series on Smart Innovation, Systems and T echnologies


Scope

We invite research- and practice-based contributions to the Human-Centered Intelligent Systems (HCIS) conference, which is collocated under the umbrella of KES Smart Digital Futures. The conference will have a general track and will also include a set of specialized invited sessions. We are also calling for organizers of invited sessions who would like to address special related subjects (http://hcis-20.kesinternational.org). The following topics define the conference scope, although other associated subjects may be applicable.

Optional: We invite advanced students as well as doctoral students to submit papers and attend the Doctoral Consortium. The Consortium will have a plenary session followed by an individual mentoring session. Each student will present his/her research project, including research questions and goals, the stage of their research process, and future research plans.

Digital Humanism and Artificial Intelligence

  • Ethics and Value Alignment in Human-Machine Interactions
    • Ethical Decision-Making and Intelligent Systems: Fairness and Equality, Transparency, Explanation, Privacy, Safety, Responsibility, Reflection
    • Value Trade-Offs and Ethical Dilemmas
    • Distinctively Human Qualities: Expertise, Judgement, Intuition, Empathy, Moral Compass, Creativity
  • Political and Social Dimensions
    • Social Computing and Artificial Intelligence
    • Human Rights
    • Democracy, Inclusion, Freedom of Speech
    • Effective Regulation, Policy-Making, and Legal Compliance
    • Ethical Leadership in the Deployment and Procurement of Intelligent Systems
    • Impact of Information Technology on Business and Society

Artificial Intelligence and Cognition

  • Smart Interpretation of Images, Numbers, Texts, Voices, Dialogs, Sensors, Actors, Information, Signals, Events
  • Cognitive Computing and the Internet of Things
  • Human and User Modeling
  • Knowledge Technologies and Semantic Web
  • Knowledge Modeling, Representation, Reasoning and Inference
  • Transparency, Explanation and Rationality
  • Real-Time Data Stream Processing
  • Machine Learning and Big Data Analytics
  • Statistical Learning
  • Clustering and Classification
  • Neural Networks and Deep Learning
  • Support Vector Machines
  • Pattern Recognition
  • Analytic and Rational Machines
  • Decision Support, Simulation and Management
  • Transformation of Human Tasks

Intelligent Services and Architectures

  • Intelligent Digital Systems and Services Architecture, Modeling and Engineering
  • Digital Business, Products, Services, and Systems
  • Resilient and Adaptive Software Architecture
  • Digital Enterprise Architecture
  • Internet of Things and Sensor Networks
  • Robotics and Remote Control
  • Recommender Services and Chatbots
  • Intelligent Platforms and Ecosystems
  • Blockchains and Distributed Transactions
  • Cyber Security, Identity and Digital Rights Management
  • Innovation in Service Engineering, Delivery, and Quality Assessment
  • Social Network Modeling and Intelligent Systems
  • Knowledge Modeling and Cognitive Maps
  • Human-Centered Service Systems
  • Dynamics of Action and Interaction
  • Adaptive Systems, Services, and Processes
  • Reliability and Resiliency
  • Digital Business Modeling and Management
  • Virtual Environments
  • Ecology of Service Systems
  • Smart and Wise Systems
  • Complexity of Socio-Technical Systems
  • Systems Evolution and Innovation Processes

Intelligent Interaction and Visualization

  • Mobile Technologies and Intelligent Services
  • Intelligent Audio, Video and Signal Processing
  • Intelligent Language Analytics and Generation
  • Speaker and Sound Recognition
  • Intelligent Visualization, Interaction, Collaboration and Communication
  • Intelligent Virtual and Augmented Reality
  • Intelligent Human-Computer Interaction
  • Intelligent Multimodal Interactive Systems
  • Intelligent Dashboards and Adaptive Hypermedia Systems
  • Intelligent User and Role Models
  • Intelligent Affective Computing
  • Process and Systems Monitoring
  • Systems Diagnostics and Transparency
  • Resilient Systems
  • Autonomous Control
  • Ergonomics, Interaction and Visualization
  • Human Factors and Aging
  • Ergonomics and Design
  • Human Factors in Software, Service and Systems Engineering
  • Usability and User Experience
  • Interaction and Game Design
  • Robots and Unmanned Systems
  • Simulation Environments and Systems
  • Safety Management and Human Factors

Intelligent Applications and Use Cases

  • Smart Systems in Science, Medicine, Health Care, Management Systems, Administration, Finance, Banking, Insurance, Consulting, Knowledge Transfer, Retail, Manufacturing, Logistics, Smart Energy, Industrial Environments, Smart Cities, Smart Home, Architecture and Sustainable Urban Planning, etc.
  • Industry 4.0: Advanced Digital Manufacturing, Management and Process Control
  • Sensor and Actor-based Autonomous Systems and Robotics
  • Intelligent Science and Educational Services
  • Intelligent Digital Libraries

 

Submission Guidelines and Review Process

Authors are invited to submit original, unpublished papers which are not under review for another conference, workshop, or journal by the time of submission. The contributors should address one or more research areas included above.

Detailed submission information is available on the conference page:  http://hcis-20.kesinternational.org (“Information for Authors—Submission of Papers”).

Submitted papers will undergo double-blind peer review by at least two members of the program committee. Prior to submission, please ensure that you have removed any information from your paper which could identify the authors. Paper acceptance is based on the following criteria: novelty, technical soundness, practical or theoretical impact, clarity, and presentation. At least one author per paper submission is required to register for the conference, and to present the paper.


Organization

Honorary Chairs: T. Watanabe, Nagoya University, Japan, and L. C. Jain, University of Canberra, Australia and University of Technology Sydney, Australia
General Chair:
Alfred Zimmerman, Reutlingen University, Germany
Executive Chair:
R. J. Howlett, University of Bournemouth, UK
Program Chair:
Rainer Schmidt, Munich University of Applied Sciences, Germany
Publicity Chair (Local):


International Program Committee

The list of the IPC members will be added shortly to the conference page.

AI in Government – CFP July 26, Conference Nov 7-9, Washington DC AAAI FSS

AAAI Fall Symposium Series

AI in Government and Public Sector Fall Symposium

Preliminary Call for Participation (Deadline July 26)

Conference November 7-9, 2019, Washington DC

The democratization of AI has begun. AI is no longer reserved for a few highly specialized enterprises. As free, easy-to-deploy AI models proliferate, we see that simple, localized, but nonetheless very useful AI applications are beginning to pervade society. Government and the public sector are not immune from this trend.

However, AI in Government and the Public Sector faces unique challenges and opportunities. These systems will be held to a higher standard since they are supposed to operate for the “public good.” They face increased scrutiny for transparency, fairness, explainability, and operation without unintended consequences. Governments provide critical services and are expected to be the provider of last resort, sometimes backstopping the commercial sector. How can the development, deployment, and use of these systems be managed to ensure they meet these requirements by design and in practice?

This symposium will focus on these unique elements of government and public sector AI systems. We invite contributions addressing topics including:

  • Early areas for adoption of AI – What public sector problems exist where AI can play a large/important role without deep new experimentation? How can socially desirable challenges be configured to leverage AI’s strengths, in, for example, fighting terrorism, better serving vulnerable populations, understanding acquisition regulations, combating child trafficking, life-long education and training, etc.
  • Using AI to encourage public service innovation – What areas are less immediately approachable by AI, but still pose an urgent need, and hence offer a significant enough financial and social reward to justify experimentation by public administrators? One example is administrative law cases in which there is a large need for AI/Automation due to the huge number of backlogged cases. What can be done to facilitate the use of AI in Government (e.g., standards) and what might hinder adoption of AI that the community might correct?
  • Trust and Transparency – Ensuring transparency and comprehensibility in the governmental use of AI, to avoid anti-democratic preferential access and treatment to select members of society. This includes questions such as open data and accountability of both officials and AI systems, as well as open source code and almost certainly open models and training methods. The debate on whether open source is safer or less safe than closed source may be explored. Which presents a greater danger of hacking and external manipulation? What polices might be needed to mitigate problems and facilitate adoption?
  • Robust & Resilient – Ensuring that AI systems are designed and built to be robust and resilient in the face of systemic, cyber, external manipulation and deception, and other risks. Are redundant AI systems a solution? While open code requirements allow the detection of back doors and other problems, how this will work with trained AI models? How to harden AI-based models from model poisoning designed to misdirect or bias results?
  • Bias – Developing AI methods to support auditing to detect bias, and then benchmark any efforts to mitigate unwanted bias. What might be done to detect and mitigate unwanted bias in, for example, machine learning or data collection?
  • Role of Public-Private Partnerships – What is the role of public-private partnerships in researching, creating, and operating AI systems for government? Should AI R&D institutes be created to enable multi- disciplinary research with academia and industry and provide a conduit for early adoption and transition of AI technologies in government? What other approaches should be considered to accelerate the development and adoption of AI in government? How can one establish and foster public-private partnerships around AI to the benefit of both?
  • Verification and Validation for Deep Learning – Validation of deep learning models in government applications. Often the correctness of the classifications that a deep learning model implements is ultimately derived from regulation or some other complex text. How do we validate these models, when human interpretation is so much a part of the correctness criteria? As models continuously learn, how do we validate that they still meet their original specifications?
  • Translating from .com to .gov – The reality is that .com adoption is happening faster than .gov adoption of AI. What best practices and approaches can be transferred from the .com experience to benefit .gov?
  • Interaction Paradigms – Insights about various paradigms for AI usage in government operations, such as intelligence augmentation/human-machine collaborative approaches, various levels of autonomy, methods for

handling uncertainty / conflicting evidence and opinion, various types of users (government employees,

general public, elected officials).

Systematic Approach for the use of AI in government – Policies, methodologies, guides or elements in support of such use (e.g. taxonomies, ontologies). In deploying AI technologies to improve government operations, there can often be a tension between effectiveness and protecting ownership and control rights to information, both directly provided and derived, about private sector citizens and other entities, especially since worldwide governments regulate such issues differently. What are these tensions and trade-offs and how can they be addressed?

Privacy – Factoring into AI-based models Privacy issues as they relate to GDPR and other National and State regulations, compliance and penalty issues

Leveraging AI innovations from open source – There are hundreds of open source AI related projects focusing on several AI sub-domains such as deep learning, machine learning, models, natural language processing, speech recognition, data, reinforcement learning, notebook environments, ethics and many more. How can government entities leverage the abundance of open source AI projects and solutions in building their own platforms and services? Based on which criteria should we evaluate various projects aiming to solve same or similar problems? What kind of framework should be in place to validate these projects, and allowing the trust in AI code that will be deployed for public service?

Cultivating AI literacy – The relationship between the public sector and AI will benefit from a widespread acceptance of what constitutes AI. How can we have a productive conversation with the public? Would the conversation around AI benefit from having criteria for deciding when it is most productive to talk about AI as opposed to various closely related terms such as “modern automation”, “machine learning”, “software”, “computer science”, etc.?

AI engineering best practices – The increasing prevalence of machine learning in automation exposes AI to real-world data, raises concerns about data drift, data poisoning, adversarial AI, and more. The increasing complexity of probabilistic models and data pipelines raises the cost of understanding a system well enough to fix it when it breaks. These diverse concerns urgently call for AI engineering guides to help ensure robustness and transparency in AI solutions What new software engineering challenges does AI pose? What new best practices or standards for engineering AI are required? Do new best practices for engineering AI conflict with existing software engineering best practices?

Incentivizing AI engineering best practices – The ability of the government and public sector to leverage AI depends in part on the availability of AI implementations that attain the highest levels of transparency, in terms of the documentation, the modularity of implementation, adherence to potential standards. How should the government incent appropriate discourse and resolution of these issues? Should this happen under the umbrella of academia or elsewhere?

Submissions The symposium will include presentations of accepted papers in both oral and panel discussion formats, together with invited speakers and software demonstrations. Potential symposium participants are invited to submit either a full-length technical paper or a short position paper for discussion. Full-length papers must be no longer than eight (8) pages, including references and figures. Short submissions can be up to four (4) pages in length and describe speculative work, work in progress, system demonstrations, or panel discussions.

Please submit via the AAAI EasyChair.org site choosing the AAAI/FSS-19 Artificial Intelligence in Government and Public Sector track ( https://easychair.org/conferences/?conf=fss19 ). Please submit by July 26. Contact Frank Stein (fstein@us.ibm.com) with any questions.

Organizing Committee Frank Stein, IBM (Chair); Mihai Boicu, GMU; Lashon Booker, Mitre; Michael Garris, NIST; Mark Greaves, PNNL; Ibrahim Haddad, Linux Foundation; Anupam Joshi, UMBC; Zach Kurtz, SEI; Shali Mohleji, IBM; Tien Pham, CCDC ARL; Greg Porpora, IBM; Alun Preece, Cardiff University; Jim Spohrer, IBM;

Remembering Jean Paul Jacob (1937-2019): Brazil, IBM, and Berkeley’s Invisible Hand

Berkeley School of Engineering, June 20th, 2019

Remembering Jean Paul Jacob – the invisible hand guiding Brazil, IBM, and Berkeley into the future. Jean-Paul would surely have us smiling and laughing in spite of this solemn occasion today. Thank-you so much Berkeley friends.

Jean Paul was a master communicator, like Carl Sagan in my mind. However, instead of billions and billions of stars, Jean-Paul spoke of billions and billions of transistors.

Jean Paul was cool – he looked a bit like “the most interesting man in the world” from the beer commercials, with his well-groomed beard – which he slowly pulled on as he contemplated his next witty remark.

We miss Jean Paul greatly – his razor-sharp wit and passion for explaining the future of technology to business and government leaders, as well as journalist and students of diverse backgrounds. Carolyn Wallace who is here today was our Almaden customer briefing leader, and she scheduled Jean Paul in hundreds of presentations over the years.

Jean Paul had 57 years of service at IBM, more than anyone else I have ever known. Forty years as a regular employee and seventeen years as IBM Research Emeritus.

After getting his bachelor’s degree in electrical engineering in Brazil and then moving to France in 1960, Jean Paul first started working for IBM Nordics in 1962 in Stockholm Sweden. The next year, he moved to IBM Scientific Computing Research Center in California to work on the NASA Space Mission Simulator, and getting his Berkeley degrees. He spent the 1970’s back in Brazil working with universities on scientific computing, and then helped establish in 1980 the IBM Brazil Scientific Center and Institute for Software Engineering, where he met Fabio Gandour, a doctor, who later came to work for IBM (and Camille Crittendon read Fabio’s Eulogy early). In the mid 1980’s he returned to California, and had offices both at the newly built IBM Almaden Research Center and the IBM Cottle Road Storage Systems center down the hill in San Jose. In 1991, he met Mike Foster.

I met Jean Paul when I started at IBM Almaden in 1998, and Jean Paul took on the big challenge of helping me learn to make a proper presentation. This proved to be a lifelong challenge for Jean Paul – but I hope he is smiling down on us all today, since I used no slides.

Thank-you Berkeley friends for this wonderful event, and I would just like to share a few of the comments that have been collected from those who Jean Paul worked with over the years at IBM:

Robin Williams (IBM Research – Almaden, Retired): “Jean Paul would go to Brazil about once a year and later I would hear that he was on TV there being interviewed, that he gave great talks about the future of technology and got rave reviews.  He was treated like a rock star. “

Sergio Borger (IBM Brazil, Executive): “JP was a role model for me, since I was in high school.”

John Cohn (IBM Fellow): “In honor of Jean Paul, I made this video called Video, about The Pickle Lightbulb which I know was one of his favorite science experiments.”

Fernando Koch (IBM): “Jean Paul Jacob was a master communicator. When he spoke about the future of technology, people listened”

Laura Anderson (IBM Almaden): “He was legendary.”

Ted Selker (IBM Fellow, Retired): “Jean Paul Jacob’s energy and caring were infectious… I so  appreciated him introducing me to fascinating opportunities and experiences… The most surprising experience was when he had Playboy Brazil interview me about our research project called Room With A View.”

Mike Ross (IBM Almaden Communications, Retired): “JP’s entertaining/evolving talk/presentation .”The Future Is Not What It Used To Be!” – would love to have an image of the JP-2000 universal portable computer/communicator he predicted in the early 1990s … which was so outrageous when he predicted it … but in retrospect, clearly something similar to what smartphones are today.”

Dan Russell (Google, formerly IBM, author of “The Joy of Search” book): “My favorite JP memory is that for years I had a standing Thursday afternoon meeting with him.  When I’d show up he’d say “Where’s my Dan Russell list??” and look for a piece of paper with my name on it. “

Ethevaldo Siqueira (Brazilian Journalist): “Few scientists in Brazil and the United States have contributed more than Jean-Paul Jacob to enrich our knowledge of digital technologies”

Robert Morris (IBM, Retired, former Director of IBM Almaden): “Thanks for forwarding the sad news regarding Jean Paul.  He was such a towering force and was so truly caring about people and institutions.  He will be missed.”

Rich Pasco (IBM, Retired): “Jean Paul Jacob was my boss of sorts, in that he arranged my two trips to Brazil in the 1980’s to teach. I deeply respected his work in bringing about international collaboration on scientific and technical topics.”

Sonia Sachs (former IBM Almaden Researcher): “He always made me laugh. Jean Paul was incredibly selfless, never saying much about himself. Always listening. And he never let his illness reduce his incredible sense of humor, his interest in making our conversation light and happy. He said that he would find a way to communicate with me from the “Beleleu,” i.e., in the afterlife… We made a lot of jokes about the Beleleu conversations, all of which delighted Jean Paul. “

For more stories click here.

Timeline

Young Jean Paul Jacob

1937 (Jan) Brazil – Born São Paulo

1960 Brazil – Electrical Engineering degree from the Instituto Tecnológico de Aeronáutica

1960-1962 Europe – France Industry and Academic Positions, including possible master’s degree at the Sorbonne in aeronautical engineering.

1962 Europe, Sweden, Stockholm – IBM Europe Nordics

1963 USA, CA, San Jose, IBM San Jose  – NASA Space Mission Simulations, and PhD Berkeley 1966 Math & Engineering

1969 Brazil – Faculty University of São Paulo (USP), the Instituto Tecnológico de Aeronáutica (ITA) and the Federal University of Rio de Janeiro (UFRJ) – Systems Department.

1980 Brazil – Founded IBM Brazile Scientific Center and Institute for Software Engineering
(Fabio Gandour: “Jean Paul was the second person that I met when in the early 80’s I, still a practicing MD, went to IBM Brasil to propose a partnership between the Hospital Foundation of the Federal Disctric and the then IBM Brasil Scientific Center.”)

1986 USA, CA, San Jose – IBM Research Almaden – Research Staff Member (Visiting Scholar Stanford and Berkeley)
(Mike Foster: “In 1991, when I started at Almaden, he had offices in Almaden and in Building 28 on plant site.” On 20190618, Email Michael Foster mfoster@nhusd.k12.ca.us)

2002, USA, CA, Emeryville – Retired – IBM Researcher Emeritus Faculty Berkeley

2019, April 7 – Passed away, Emeryville, CA USA after a life well-lived.

Additional information: https://alchetron.com/Jean-Paul-Jacob

Jean Paul on Right in Top photo – “the most interesting man in the world”

Journals, Conferences, Books

Journal of Service Research https://journals.sagepub.com/home/jsr Editor-in_Chief Michael K. Brady, Florida State University, USA

INFORMS Journal of Service Science https://pubsonline.informs.org/journal/serv Editor-in-Chief Saif Benjaafar, University of Minnesota, USA

Journal of Systems and Service-Oriented Engineering (IJSSOE) https://www.igi-global.com/journal/international-journal-systems-service-oriented/1155 Editor-in-Chief: Dickson K.W. Chiu (The University of Hong Kong, Hong Kong) [Additional Contact Katelyn Hoover khoover@igi-global.com]

Originally posted by Jim Spohrer on 2 January 2013, 7:15 pm

Calls for papers with Service Science themes

Journals

Journal of Service Research
Editor-in-chief: Mary Jo Bitner
Founding Editor: Roland Rust
Impact Factor: 2.732
Ranked: 16 out of 113 in Business
Source: 2011 Journal Citation Reports® (Thomson Reuters, 2012)
News: About 25 articles a year since about 1990

Journal of Service Science (INFORMS)
Founding Editor: Robin Qiu
News: About 33 articles per year since 2009

International Journal of Information Systems in the Service Sector (IJISSS)
An Official Publication of the Information Resources Management Association
Editor-in-chief: John Wang
News: About 33 articles per year since 2009

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET)
An Official Publication of the Information Resources Management Association
Contact: Miguel-Angel Sicilia (University of Alcalá, Spain)
News: About 30 articles per year since 2010

International Journal of E-Services and Mobile Applications
Editor-in-chief: Ada Scupola
News: About 20 articles per year since 2009

International Journal of Services Sciences
Inderscience Publishers
Editor-in-chief: Desheng (Dash) Wu
News: About 12 articles per year since 2008

Service Science
Online electronic journal
Editor-in-chief: Minder Chen
News: About 4 articles per year since 2008

Journal of Service Science
Clute Institute
Contact: Ronal Clute
News: About 11 articles per year since 2008

International Journal of  u- and e- Service, Science and Technology
Science and Research Support Society (SERSC)
Contact: Jianhua Ma, Hosei University, Japan
Contact: Byeong-ho Kang, University of Tasmania, Australia
News: About 25 articles per year since 2008

Journal of Service Science and Management
Contact: Editor-in-Chief Prof. Samuel Mendlinger Boston University, USA
News: About 50 articles per year since 2008

Service Science and Management Research
Contact: Editorial Board: Dr. Rocío Pérez de Prado, University of Jaén, Spain
News: About 2 articles per year since 2012

International Journal of Quality and Service Sciences
Contact: Editor Professor Su Mi Dahlgaard Park, Lund University, Sweden ijqss@ch.lu.se
News: About 25 articles per year since 2008

Journal of Service Science Research
Contact: Editor-in-Chief: Daihwan Min
Society: The Society of Service Science
News: About 12 articles a year since 2008

Production Planning and Control
Organisational transformation in servitization
Deadline for submission: January 14th, 2013
Contact: “Paolo Gaiardelli” <paolo.gaiardelli@unibg.it>

Conferences, Workshops, Seminars:

Naples Forum on Service 2013 
Contact: Francesco Polese
Contact: Cristina Mele
Contact: Evert Gummesson
News: final deadline to submitt a proposals is 15 January 2013.
‘2013 Naples Forum on Service’ to be held in Ischia, June 2013

POMS College
Contact: Ravi Behara
Contact: Gang Li
News: deadline for abstract submission is January 18, 2013.
24th Annual Meeting in Denver Colorado May 3-6, 2013

MIT and the Digital Economy
Contact: @ErikB
Friday, January 18, 2013, Noon – 7:00 p.m.
Grand Hyatt San Francisco, 345 Stockton Street, San Francisco, CA
Participating speakers at this time include:
Rod Brooks – Founder, Chairman, and CTO, Rethink Robotics
Erik Brynjolfsson, PhD ’91 – Director, The MIT Center for Digital Business,
Schussel Family Professor of Management Science, MIT Sloan School of Management
Douglas Leone, SM ’88 – General Partner, Sequoia Capital
Andrew McAfee, ’88, ’89, LGO ’90 – Associate Director and Principal Research Scientist, MIT Center for Digital Business
Gokul Rajaram, MBA ’01 – Product Director, Ads, Facebook

Service oriented Enterprise Architecture for Enterprise Engineering (SoEA4EE 2012)
Contact: Selmin Nurcan
Contact: Rainer Schmidt
Info: Working on a 2013 special issue for IJISSS on SoEA4EE

ICIW 2013,
The Eighth International Conference on Internet and Web Applications and Services
Contact: Steffen Fries
June 23 – 28, 2013 – Rome, Italy

5th Annual International Service Innovation and Design
eminar on March 14, 2013!
Contact: Laurea – Uuden edellä | Prime mover
5th International SID Seminar | March 14, 2013 at 8:30-17:30
• What is the role of design in value creation?
• How do you ensure sustained value creation for all stakeholders?
• How do you improve your competitive advantage?

MSI’s conference Beyond the Product: Designing Customer Experiences at Stanford University on February 19-20, 2013 in Stanford, CA.
Contact: #custexpMSI

4th Summer School of the European Social Simulation Association (ESSA)
Hamburg University of Technology, July 15-19, 2013
Matthias Meyer and Iris Lorscheid
Hamburg University of Technology
Institute of Management Control and Accounting
http://www.cur.tu-harburg.de
The NAACSOS mailing list is a service of NAACSOS
North American Association for Computational and Organizational Science

THROUGH-LIFE ENGINEERING SERVICES (TESconf 2012)
2nd International conference of
5th & 6th November 2013
Cranfield, Cranfield University, UK
Sponsor: EPSRC Centre for Innovative Manufacturing in Through-life Engineering Services
Contact: Rajkumar Roy
Deadline: 15th February 2013
Center first annual report:
1st Annual Report for 2011-12

University-Industry Demonstration Partnership Project Summit
January 15-17, 2013
National Academies’ Keck Building
500 5th St NW, Washington, DC 20001
Contact: Anthony Boccanfuso
UIDP: University-Industry Development Program

SERVICE COMPUTATION 2013, The Fifth International Conferences on Advanced Service Computing
May 27 – June 1, 2013 – Valencia, Spain
Submission deadline: January 22, 2013
Sponsored by IARIA,
Contact: ?

First International Conference of Serviceology
Contact: @Yurikos

22nd annual International Conference on Management of Technology,
in Porto Alegre, Brazil (April 14-18, 2013)
Contact: “iamot@miami.edu”, Yasser.Hosni@ucf.edu, tkhalil@nileuniversity.edu.eg

15th IEEE Conference on Business Informatics

[successor of the IEEE Conference of e-Commerce and Enterprise Computing (CEC)]

Vienna (Austria), 15 – 18 July 2013
Contacts: Huemer Christian <huemer@big.tuwien.ac.at>, “Birgit Hofreiter” <birgit.hofreiter@tuwien.ac.at>
* Paper Submission: March 1, 2013

10th WSEAS International Conference on Engineering Education (EDUCATION ’13)
University of Cambridge, UK, February 20-22, 2013

Books:

Service Science: Research and Innovations in the Service Economy
Springer, Series Editors: Bill Hefley and Wendy Murphy
Website:

Service Systems & Innovations in Business & Society
Business Expert Press (BEP)
BEP, Series Editors: Haluk Demirkan and Jim Spohrer

Encyclopedia of Quality and the Service Economy (SAGE)
Contact: Su Mi Dahlgaard-Park

Finland 2019: OpenTech AI Workshop (May 6-7, Helsinki)

Join the live stream here.

Presentation on Future of AI here. Perspective 1: David Cox (IBM-MIT Partnership) – Narrow AI, Broad AI, General AI, and Perspective 2: Jim Spohrer (IBM) – Better Building Blocks

Highlights include keynotes from European Commission, Business Finland, VTT projects, and IBM on AI & Ethics, Future of AI. Please consider attending, and/or submitting a poster or tutorial.
Name: AI Workshop (2019 Finland #OpenTechAI)
Date: May 6-7, 2019
Location: IBM Finland HQ, Laajalahdentie 23, Helsinki
Size: 120+ Attendees, 1/3 industry, 1/3 academic, 1/3 government and other
For Invitation Contact: Jim Spohrer <spohrer@us.ibm.com>
Registration: http://ibm.biz/IBMOpentechAI2019
Sponsors: IBM Finland and VTT Finland (~3000 Researchers)
Focus: Open Data and AI Technologies
Note: Finland is a leader in open data, and aspires to be the world leader in “applying” artificial intelligence in real use cases to benefit society.

Agenda
Day 1 (Monday May 6)
1:00 pm-4:00 Tutorials (multiple tracks)
4:00-5:00 Free time
5:00-7:00 Reception and Poster session

,Day 2 (Tuesday May 7) – will be live streamed
7:30am Breakfast
8:30 Welcome from Mirva Antila (IBM Finland Country Manager) and Antti Vasara (President and CEO VTT Finland)
9:00 Morning keynotes
Keynote: EC: Data for AI and related EU policies and programmes, Speaker: Kimmo Rossi (Head Research & Innovation Sector, European Commission)
Keynote: Finland: Finnish AI Landscape and Roadmap, Speaker: Mika Klemettinen (Director, Digitalisation, Business Finland)
Keynote: IBM: Put AI to Work for Business, Trust and Transparency, Co-speaker: Ann-Elise Delbec (IBM), Dr. Jean-Francois Puget (IBM DE, Kaggle Grand Master)
12:00 Lunch
1:00 Afternoon three panels (each with one moderator, three panelists)
Panel 1: AI and Healthcare Projects
Miikka Kiiski, Janne Huttunen, Mikael von und zu Fraunberg, Aleksi Kopponen,
Panel 2: AI and Industry/Energy Projects
Laura Sutinen, Shuli Goodman (Linux Foundation), Caj Södergård, Juho Korpela, Juha Rokka,Samuli Savo (Stora Enso)
Panel 3: AI and Open Source Projects
Haddad (Linux Foundation), Swirtun (FOSSID), Pakkala (VTT), Peret (Nokia), Roter (Mozilla)
4:00 Closing keynote – Future of AI, Jim Spohrer (IBM)
5:00 Closing thank-you

Program Committee
    Daniel Pakkala (VTT) and Jim Spohrer (IBM)
    Päivi Cederberg and Teppo Seesto (IBM Finland)
    Susan Malaika (IBM) and Tuomo Tuikka (VTT)
    Ibrahim Haddad (Linux Foundation)
    Eveliina Paljärvi
(IBM) – publicity

How to Participate

Just to emphasize we’d love posters too – or even a small tutorial? Please submit proposals here https://easychair.org/conferences/?conf=otai2019

You can see photos from last year’s successful poster session here https://twitter.com/sumalaika/status/1113606625942806533

Link to last year’s event: https://developer.ibm.com/opentech/2018/01/29/helsinki-march-2018-opentech-ai-workshop/

Link to this year’s call for tutorials and posters: https://developer.ibm.com/opentech/2019/03/25/helsinki-may-2019-opentech-ai-workshop/


For Invitation Contact: Jim Spohrer <spohrer@us.ibm.com>
 Registration: http://ibm.biz/IBMOpentechAI2019

ISSIP Guide to Service/Systems/Innovation Degrees and Certifications

Motivation:
People from around the world email me every week, asking about “service/systems/innovation degrees and certifications.” Their questions range from (1) where to get a PhD/Masters/Bachelors as a fulltime student, to (2) same, but as a remote, working professional, part-time students, to (3) short-term, certification or certificate of completion for one week workshop, attending a few day conference, or full or have day tutorial at a conference. Next they want to know how long it will take and estimated cost of such a program, and if there is an email address or URL to take next steps to get more information. Finally they ask for a textbook recommendation or one book they could easily purchase and read to start preparing to apply to programs to get needed degree or certifications. Some ask if there are any online courses from Coursera or Udacity that I could recommend.

Traditional response:
In response to these queries, I have a short list of universities (by country) and textbooks that I usually point people to…. However, I would like to do better than that.

Better response:
To do better my first thought was to have ISSIP design a survey, and then email members and colleagues to try to collect the first draft version of the information more systematically. Compiling an ISSIP guide to service/systems/innovation degrees and certifications seems to be worth investigating a bit… Then realized maybe someone or some organization has already done this in part or completely…

Questions:
(1) Has this already been done? What is best that exists?

(2) Needs to be done? Would below be an OK first pass survey?

Proposed Survey:
Survey estimated time to complete 20 minutes if your institutions offers service/systems/innovation degrees or certifications:

Definition (casting a wide net, since it is a big tent): A “service/systems/innovation degree or certification” includes a service-related emphasis with a specialized expert teaching/research faculty or instructors for traditional academic degrees such as design, marketing, management, engineering, operations, economics, computing, web services, information systems, industrial engineering, operations research, analytics, decision-making, data sciences, artificial intelligence, law, public policy, anthropology, humanities, ethics, leadership, entrepreneurship, innovation, open innovation, complex systems, sustainable systems, or other traditional or non-traditional degree or certification. The faculty or instructors for these programs may be from academia or from industry, government, or other areas of practice or industrial research or professional development. The expert instructors must have distinguished themselves, in some way such as: (a) publications in service/systems/innovation research literature, (b) positions of responsibility and distinction in business, government or practice, or (c) some other form of professional success.

For “service/systems/innovation degree or certification” as defined above…

  1. Degrees/Certifications: For your institutions, please indicate which of these are granted:
    (a) PhD degree
    (b) Masters degree
    (c) Bachelor degree
    (d) Associate degree
    (e) Certificate of Completion, Certification, or Badge
    (f) Other
  2. Student Options: For each of the above selected, indicate student options…
    (a) PhD degree – full-time, night-school (working professional), remote students, online
    (b) Masters degree – full-time, night-school (working professional), remote students, online
    (c) Bachelor degree – full-time, night-school (working professional), remote students, online
    (d) Associate degree – full-time, night-school (working professional), remote students, online
    (e) Certificate of Completion, Certification, or Badge – full-time, night-school (working professional), remote students, online
    (f) Other – full-time, night-school (working professional), remote students, online
  3. Time, Cost: Please indicate estimated time to complete and cost
    (a) PhD degree- time (years), cost ($)
    (b) Masters degree- time (years), cost ($)
    (c) Bachelor degree- time (years), cost ($)
    (d) Associate degree — time (years), cost ($)
    (e) Certificate of Completion, Certification, or Badge – time (months, weeks, days, hours, as appropriate), cost ($)
    (f) Other – time (years, months, weeks, days, hours, as appropriate), cost ($)
  4. Center: For your institution, do you have an existing center of excellence with service/systems/innovation related research, and if so is there a URL?
    (a) Yes, name:
    (b) Yes, URL:
    (c) No
    (d) Other
  5. Textbook: Do you recommend a specific textbook or book for students preparing to get a degree/certification, and if so is there a URL?
    (a) Yes, name:
    (b) Yes, URL:
    (c) No
    (d) Other
  6. Workshops/Conferences: Do you recommend a specific workshop or conference that students/professionals can attend to get a certificate of completion, certification, badge, etc., and is so is there a URL?
    (a) Yes, name:
    (b) Ues, URL:
    (c) No
    (d) Other
  7. Can you recommend institutions or people to contact to survey?
    (a) Suggestion 1:
    (b) Suggestion 2:
    (c) Suggestion 3:
    (d) Other
  8. Are you an ISSIP member? If so, would you like to see an annual ISSIP Guide to Service/Systems/Innovations Degrees and Certifications?
    (a) Yes member, Yes like to see annual guide
    (b) Yes member, No need for annual guide
    (c) No, not a member
    (d) Other
  9. Any final thoughts, or comments?
    (a) Comments:

From Handbook of Service Science diagram:


Page 706, HOSS1
Page 706, Handbook of Service Science (2010)

From Cambridge SSME Report:

pages 23-24 Cambridge SSME report.
Pages 23-24 from Cambridge SSME report (2008)

Example of a well-designed anthropology online Masters from University of North Texas: http://anthropology.unt.edu/graduate/online-masters-program

Others can be found here: https://www.guidetoonlineschools.com/degrees

SERVSIG lists these: http://www.servsig.org/wordpress/teaching/services-marketing-syllabi/

The New Foundational Skills of the Digital Economy & Universities Respond

Universities are responding to the need for all graduates to have foundational skills for a data-driven, AI-powered, digital economy. These new university programs will create graduates with depth in traditional disciplines, as well as broader boundary spanning skills – resulting in T-shapes. Over time, our data will become our AI helper.

Two Skills Reports

Two skills reports are especially relevant to the breadth and depth of skills of T-shaped Adaptive Innovators, from BHEF and NESTA:

BHEF (2018): Markow W, Hughes D, Bundy A (2018) The New Foundational Skills of the Digital Economy: Professionals of the Future. Burning Glass and Business Higher Education Forum Report (BHEF). URL: http://www.bhef.com/sites/default/files/BHEF_2018_New_Foundational_Skills.pdf

“Modern jobs integrate an array of broadly demanded skills. These are not the specialized skills of the engineer the physicist, working with advanced mathematical models, so much as they are those of the analyzer of complex bodies of data, the software programmer, the project manager, and the critical thinker.”

Oddly worded, since engineers and physicists are typically critical thinkers who know how to analyze complex bodies of data. That said software programmer (especially Python), and project manager (especially Agile methods with scrums) are not always taught to engineers and physicists at the bachelors level.

NESTA (2017): Bakhshi H, Downing J, Osborne M, Schneider P (2017) The Future of Skills: Employment in 2030. London: Pearson and Nesta. URL: https://www.nesta.org.uk/report/the-future-of-skills-employment-in-2030/

  • Around one-tenth of the workforce are in occupations that are likely to grow as a percentage of the workforce and round one-fifth are in occupations that will likely shrink.
  • Education, healthcare, and wider public sector occupations are likely to grow while some low-skilled jobs, in fields like construction and agriculture, are less likely to suffer poor labor market outcomes than has been assumed in the past.
  • The report highlights the skills that are likely to be in greater demand in the future, which include interpersonal skills, higher-order cognitive skills, and systems skills.
  • We also identify how the skills make up of different occupations can be altered to improve the odds that they will be in higher demand in the future.
  • The future workforce will need broad-based knowledge in addition to the more specialised skills that will are needed for specific occupations.”

The last bullet point in the above NESTA report is especially relevant to T-Shaped Adaptive Innovators with breadth (“broad-based knowledge”) and depth (“more specialised skills”).

Systems thinking and collaborative problem-solving are also characteristics of T-Shaped Adaptive Innovators:

  • ” Interestingly, systems skills, relatively underexplored in the literature, all feature in the top 10. Systems thinking emphasises the ability to recognise and understand socio-technical systems – their interconnections and feedback effects – and choose appropriate actions in light of them. It marks a shift from more reductionist and mechanistic forms of analysis and lends itself to pedagogical approaches such as game design and case method with evidence that it can contribute to interdisciplinary learning (Tekinbas et al., 2014; Capra and Luisi, 2014; Arnold and Wade, 2015).
  • —  The combined importance of these skills and interpersonal skills supports the view that the demand for collaborative problem-solving skills may experience higher growth in the future (Nesta, 2017). “

Four Universities Respond

The importance of Data Sciences and Artificial Intelligence to all disciplines, occupations, and yes, even cultures (values), is becoming increasingly apparent to universities, so they are starting AI Colleges, Sub-Universities, and Centers to explore AI’s impact across the board, and/or managing complex systems from a transdisciplinary perspective.

For example, consider MIT, Berkeley, Stanford, UC Merced.

MIT (Oct. 15, 2018), see: https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html

The goal of the college, said L. Rafael Reif, the president of M.I.T., is to “educate the bilinguals of the future.” He defines bilinguals as people in fields like biology, chemistry, politics, history and linguistics who are also skilled in the techniques of modern computing that can be applied to them.

But, he said, “to educate bilinguals, we have to create a new structure.”

Academic departments still tend to be silos, Mr. Reif explained, despite interdisciplinary programs that cross the departmental boundaries. Half the 50 faculty positions will focus on advancing computer science, and the other half will be jointly appointed by the college and by other departments across M.I.T.

Traditionally, departments hold sway in hiring and tenure decisions at universities. So, for example, a researcher who applied A.I.-based text analysis tools in a field like history might be regarded as too much a computer scientist by the humanities department and not sufficiently technical by the computer science department.

Berkeley (Nov 2, 2018), see: https://www.insidehighered.com/news/2018/11/02/big-data-ai-prompt-major-expansions-uc-berkeley-and-mit

Berkeley provost Paul Alivisatos said that simply expanding the university’s existing computer sciences department would not be enough to match the surge of interest.

“Pretty much any field of inquiry and knowledge connects to [data science],” he said. “We wanted to create a structure that would allow that new methodological development to grow more, but also allow it to be widely used everywhere, where it can be beneficial.”

He said Berkeley envisions incorporating faculty members from fields as varied as sociology, public health and physics into a kind of “data science commons” to deepen their research. “From what we can tell, pretty much every part of this university wants to be involved, which is great.”

The field, Alivisatos said, is forcing other disciplines to come to terms not just with the widespread availability of data from diverse sources, but with “new methods that allow it to be sifted and analyzed.”

David Culler, Berkeley’s interim dean for data sciences, said the new division will be a peer of the university’s other schools and colleges. “But rather than standing apart from them, it’s really integrated with them,” he said, since these days, data science “touches almost every domain of inquiry.”

Culler said Berkeley, like most major universities, has been “grappling with this for at least five years” as it tried to figure out how to fit new computational disciplines into the broader world of other academic fields.

“The frontiers of knowledge are extremely integrative, and yet to a large extent, institutions of higher learning are very hierarchical,” he said.

Stanford (Mar 15, 2019), see: https://www.mercurynews.com/2019/03/15/stanford-unveils-new-ai-institute-built-to-create-a-better-future-for-all-humanity/amp/

“The scope and scale of impact of the Age of AI will be more profound than any other period of transformation in our history,” Li and co-director John Etchemendy said in an online note about the new institute. “AI has the potential to radically transform every industry and every society.”

The institute will take advantage of Stanford’s strength in a variety of disciplines, including AI, computer science, engineering, robotics, business, economics, genomics, law, literature, medicine, neuroscience and philosophy, according to promotional materials.

“Our goal is for Stanford HAI to become an interdisciplinary, global hub for AI thinkers, learners, researchers, developers, builders and users from academia, government and industry, as well as leaders and policymakers who want to understand and leverage AI’s impact and potential,” the institute said.

UC Merced (Dec 12, 2018), add complex systems thinking, see: https://news.ucmerced.edu/news/2018/uc-merced-designing-management-school-future

The planning initiative is a faculty-led effort to create a new, transdisciplinary school that draws upon the expertise of scientists, researchers and practitioners from broad backgrounds to instill the next generations of leaders with the skills and knowledge needed to understand, design and manage complex systems.

The process will take several years, but Professor Paul Maglio, recently named director of the Gallo School Planning Initiative, said it’s time to look to the future and the next big development at UC Merced.

“We think the time is right to establish a new Gallo school at UC Merced to carry forward the interdisciplinary mission and vision of the campus and that relates broadly to management, decision making, information, communication and sustainability, and embraces the complexities of real interactions between people, institutions, technologies and the natural world,” Maglio said.

Brian Fitzgerald (BHEF) just send me this with more universities responding in the DC area: Cardenas-Navia I, Fitzgerald BK (2019) The digital dilemma: Winning and losing strategies in the digital talent race. Industry and Higher Education. 2019 Mar 25:0950422219836669. This was very interesting: In his study, 60% of the acquired employees left within 3 years—double the rate direct hires.  The study also found that acquired employees were more likely to find their own companies, many of which appeared later to compete against the acquiring company (Kim, 2018). In Figure 1, blended professional – domain knowledge looks like academic disciplines.  Industry knowledge, for example healthcare, retail, finance, etc. – is what IBM would be looking for.

My Advice

My advice to students and life learners of all ages:

Skills: Build: Data sciences and python programming for AI to build next generation learning systems.
Skills: Teach: Learning sciences for social-emotional-learning (SEL) skills
Skills: Collaborate/Lead: Agile scrum master and positive leadership.
Skills: Understand: Complex systems: Smarter/wiser service systems and service science, and service-dominant logic mindset.
Skills: Memberships: GitHub, Kaggle, Wikipedia, ISSIP.org -> becoming active in these communities of builders, teachers, collaborators

Working to become a T-Shaped Adaptive Innovator and learning about “service science” may also be helpful to those interested in working at IBM some day. IBM is always looking for high integrity individuals who are global citizens interested in building a smarter/wiser planet. Collaborating across industries, disciplines, cultures is hard, hard work, so not for everyone. See https://service-science.info/archives/3328

Acknowledgements

Thanks to Steve Kwan (SJSU Emeritus) for suggesting BHEF report and Stanford report, and Christine Leitner for suggesting the NESTA Report.

Annotated Bibliography Item: Language Models are Unsupervised Multitask Learners

FYI: Annotated Bibliography Item: Language Models are Unsupervised Multitask Learners

Wu J, Child R, Luan D, Amodei D, Suskeve I (2018) Language Models are Unsupervised Multitask Learners. URL: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

Good summary of the above here: https://towardsdatascience.com/one-language-model-to-rule-them-all-26f802c90660

“Abstract. Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on task-specific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText… These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.” (p. 1);

“1. Introduction. Current systems are better characterized as narrow experts rather than competent generalists. We would like to move towards more general systems which can perform many tasks – eventually without the need to manually create and label a training dataset for each one. The dominant approach to creating ML systems is to collect a dataset of training examples demonstrating correct behavior for a desired task, train a system to imitate these behaviors, and then test its performance on independent and identically distributed (IID) held-out examples… Multitask learning (Caruana, 1997) is a promising framework for improving general performance. However, multitask training in NLP is still nascent… his suggests that multitask training many need just as many effective training pairs to realize its promise with current approaches. It will be very difficult to continue to scale the creation of datasets and the design of objectives to the degree that may be required to brute force our way there with current techniques. This motivates exploring additional setups for performing multitask learning.” (p. 1);

“2. Approach. Learning to perform a single task can be expressed in a probabilistic framework as estimating a conditional distribution p(output|input). Since a general system should be able to perform many different tasks, even for the same input, it should condition not only on the input but also on the task to be performed. That is, it should model p(output|input, task). This has been variously formalized in multitask and meta-learning settings. Task conditioning is often implemented at an architectural level, such as the task specific encoders and decoders in (Kaiser et al., 2017) or at an algorithmic level such as the inner and outer loop optimization framework of MAML (Finn et al., 2017). But as exemplified in McCann et al. (2018), language provides a flexible way to specify tasks, inputs, and outputs all as a sequence of symbols. For example, a translation training example can be written as the sequence (translate to french, english text, french text). Like-wise, a reading comprehension training example can be written as (answer the question, document, question, answer). ” (p. 2).

“Language modeling is also able to, in principle, learn the tasks of McCann et al. (2018) without the need for explicit supervision of which symbols are the outputs to be predicted. Since the supervised objective is the the same as the unsupervised objective but only evaluated on a subset of the sequence, the global minimum of the unsupervised objective is also the global minimum of the supervised objective. In this slightly toy setting, the concerns with density estimation as a principled training objective discussed in (Sutskever et al., 2015) are side stepped. The problem instead becomes whether we are able to, in practice, optimize the unsuper- vised objective to convergence. Preliminary experiments confirmed that sufficiently large language models are able to perform multitask learning in this toy-ish setup but learning is much slower than in explicitly supervised approaches.” (p. 2).

“While it is a large step from the well-posed setup described above to the messiness of “language in the wild”, Weston (2016) argues, in the context of dialog, for the need to develop systems capable of learning from natural language directly and demonstrated a proof of concept – learning a QA task without a reward signal by using forward prediction of a teacher’s outputs. While dialog is an attractive approach, we worry it is overly restrictive. The internet contains a vast amount of information that is passively available without the need for interactive communication. Our speculation is that a language model with sufficient capacity will begin to learn to infer and perform the tasks demonstrated in natural language sequences in order to better predict them, regardless of their method of procurement. If a language model is able to do this it will be, in effect, performing unsupervised multitask learning. We test whether this is the case by analyzing the performance of language models in a zero-shot setting on a wide variety of tasks.” (p. 2);

“2.1. Training Dataset. Most prior work trained language models on a single domain of text, such as news articles (Jozefowicz et al., 2016), Wikipedia (Merity et al., 2016), or fiction books (Kiros et al., 2015). Our approach motivates building as large and diverse a dataset as possible in order to collect natural language demonstrations of tasks in as varied of domains and contexts as possible.” (p. 3);

“Instead, we created a new web scrape which emphasizes document quality. To do this we only scraped web pages which have been curated/filtered by humans. Manually filtering a full web scrape would be exceptionally expensive so as a starting point, we scraped all outbound links from Reddit, a social media platform, which received at least 3 karma. This can be thought of as a heuristic indicator for whether other users found the link interesting, educational, or just funny.” (p. 3);

“The resulting dataset, WebText, contains the text subset of these 45 million links. To extract the text from HTML responses we use a combination of the Dragnet (Peters & Lecocq, 2013) and Newspaper1 content extractors. All results presented in this paper use a preliminary version of WebText which does not include links created after Dec 2017 and which after de-duplication and some heuristic based cleaning contains slightly over 8 million documents for a total of 40 GB of text. We removed all Wikipedia documents from WebText since it is a common data source for other datasets and could complicate analysis due to over-lapping training data with test evaluation tasks.” (p. 3-4);

“2.2 Input Representation. A general language model (LM) should be able to compute the probability of (and also generate) any string. Current large scale LMs include pre-processing steps such as lower-casing, tokenization, and out-of-vocabulary tokens which restrict the space of model-able strings… Byte Pair Encoding (BPE) (Sennrich et al., 2015) is a practical middle ground between character and word level language modeling which effectively interpolates between word level inputs for frequent symbol sequences and character level inputs for infrequent symbol sequences. Despite its name, reference BPE implementations often operate on Unicode code points and not byte sequences… To avoid this, we prevent BPE from merging across character categories for any byte sequence. We add an exception for spaces which significantly improves the compression efficiency while adding only minimal fragmentation of words across multiple vocab tokens. This input representation allows us to combine the empirical benefits of word-level LMs with the generality of byte-level approaches. Since our approach can assign a probability to any Unicode string, this allows us to evaluate our LMs on any dataset regardless of pre-processing, tokenization, or vocab size.” (p. 4)’

“2.3. Model. We use a Transformer (Vaswani et al., 2017) based architecture for our LMs. The model largely follows the details of the OpenAI GPT model (Radford et al., 2018) with a few modifications.” (p. 4);

“3. Experiments. We trained and benchmarked four LMs with approximately log-uniformly spaced sizes. The architectures are summarized in Table 2. The smallest model is equivalent to the original GPT, and the second smallest equivalent to the largest model from BERT (Devlin et al., 2018).” (p. 4);

“3.1. Language Modeling. As an initial step towards zero-shot task transfer, we are interested in understanding how WebText LM’s perform at zero-shot domain transfer on the primary task they are trained for – language modeling. Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark.” (p. 4);

“For many of these datasets, WebText LMs would be tested significantly out-of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string which is extremely rare in WebText – occurring only 26 times in 40 billion bytes. We report our main results in Table 3 using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible.” (p. 4-5);

“[3.2 – 3.8 Tasks.] 3.2. Children’s Book Test. The Children’s Book Test (CBT) (Hill et al., 2015) was created to examine the performance of LMs on different categories of words: named entities, nouns, verbs, and prepositions… 3.3. LAMBADA. The LAMBADA dataset (Paperno et al., 2016) tests the ability of systems to model long-range dependencies in text… 3.4. Winograd Schema Challenge. The Winograd Schema challenge (Levesque et al., 2012) was constructed to measure the capability of a system to perform commonsense reasoning by measuring its ability to resolve ambiguities in text… 3.5. Reading Comprehension. The Conversation Question Answering dataset (CoQA) Reddy et al. (2018) consists of documents from 7 different domains paired with natural language dialogues between a question asker and a question answerer about the document… 3.6. Summarization. We test GPT-2’s ability to perform summarization on the CNN and Daily Mail dataset (Nallapati et al., 2016). To induce summarization behavior we add the text TL;DR: after the article and generate 100 tokens with Top-k random sampling (Fan et al., 2018) with k= 2 which reduces repetition and encourages more abstractive summaries than greedy decoding. 3.7. Translation. We test whether GPT-2 has begun to learn how to translate from one language to another. In order to help it infer that this is the desired task, we condition the language model on a context of example pairs of the format english sentence = french sentence and then after a final prompt of english sentence = we sample from the model with greedy decoding and use the first generated sentence as the translation. 3.8. Question Answering. A potential way to test what information is contained within a language model is to evaluate how often it generates the
correct answer to factoid-style questions.” (p. 5-7).

“4. Generalization vs Memorization. Recent work in computer vision has shown that common image datasets contain a non-trivial amount of near-duplicate images.” (p. 8).

“5. Related Work. A significant portion of this work measured the performance of larger language models trained on larger datasets.” (p. 8).

“6. Discussion. Much research has been dedicated to learning (Hill et al.,2016), understanding (Levy & Goldberg, 2014), and critically evaluating (Wieting & Kiela, 2019) the representations of both supervised and unsupervised pre-training methods. Our results suggest that unsupervised task learning is an additional promising area of research to explore.” (p. 9);

“7. Conclusion. When a large language model is trained on a sufficiently large and diverse dataset it is able to perform well across many domains and datasets. GPT-2 zero-shots to state of the art performance on 7 out of 8 tested language modeling datasets. The diversity of tasks the model is able to perform in a zero-shot setting suggests that high-capacity models trained to maximize the likelihood of a sufficiently varied text corpus begin to learn how to perform a surprising amount of tasks without the need for explicit supervision.” (p. 10);

References include one of my favorites from IBM Research (circa 1980, based on a lot of work in the 1970’s) – Jelinek, F. and Mercer, R. L. Interpolated estimation of markov source parameters from sparse data. In Proceedings of the Workshop on Pattern Recognition in Practice, Amsterdam, The Netherlands: North-Holland, May. , 1980