Talent hunt

Looking for early career and second career talent who would like to spend a year in Silicon Valley working on open source AI (artificial intelligence) projects, such as:
(1) software-defined data-centers for AI workloads
(2) open source AI smartphone assistance for automatic generation of resume/CV/annual-performance review writeups
(3) predicting scientific discoveries and redoing re-discovery with open/fewer resources
(4) open source AI smartphone apps and startup ideas
(5) individuals owning and monetizing all their personal data and citizen science twist
(6) trust, privacy, and security of personal AI data systems
(7) grand challenges of AI:
(7a) experience traces – external and internal representation of all experiences
(7b) episodic memory – trusted and socially acceptable store for all a person’s experience
(7c) commonsense reasoning – fast general understanding of physical and social worlds
(7d) social interactions – requires social commonsense
(7e) conversational search – better than keyword search conversations on any topic
(7f) ingest textbooks – ingest and apply academic knowledge
(7g) ingest lawbooks – ingest and apply institutional knowledge
(7h) collaborative coaching – people perform better when tool removed

Applicants should have deep knowledge and experience using an open source AI system, such as:

TensorFlow: https://www.openhub.net/p/tensorflow
MXNet: https://www.openhub.net/p/mxnet
Theano: https://www.openhub.net/p/theano
Caffe: https://www.openhub.net/p/caffe
deeplearning4J: https://www.openhub.net/p/deeplearning4j
H20: https://www.openhub.net/p/h2o-3
SystemML: https://www.openhub.net/p/apache-systemml

Some open source AI systems and communities

Ideal for a career transition/resume building experience.

Looking for open source AI projects related to smarter food service systems

For example, most of the fish we eat are mislabeled….

What if someone did an open source AI project similiar to what Thrun did for skin cancer for recognizing fish after different food process stages?

I am curious especially about open AI for food-related projects done by:

And any connections to open source AI communities:

TensorFlow: https://www.openhub.net/p/tensorflow
MXNet: https://www.openhub.net/p/mxnet
Theano: https://www.openhub.net/p/theano
Caffe: https://www.openhub.net/p/caffe
deeplearning4J: https://www.openhub.net/p/deeplearning4j
H20: https://www.openhub.net/p/h2o-3
SystemML: https://www.openhub.net/p/apache-systemml


Some open source AI systems and communities

Books: Life, Death, Neurotribes, Medicine Creative Destruction, Systems Research, Regulations, Service Process Design

1. Christensen CM (2007) How will you measure your life. Havard Business Review Classics. Boston MA.
Christensen CM (2010) How will you measure your life. harvard business review. Jul 1;88(7-8):46-51.

“On the last day of class, I ask my strudents to turn those theoretical lenses on themselves, yo find cogent answers to three questions: First, how can I be sure that I;ll be happy in my career? How, how can I be sure that my relationship with my spouse and family will become an enduring source of happiness? Third, how can I be sure I will stay out of jail?” (p. 5)

“… from Frederick Herberg, who asserts that the powerful motivator in our lives isn’t money; it’s the opportunity to learn, grow in responsibilities, contribute to others, and be recognized for achievements.” (p. 6)

“Create A Strategy For Your Life: … If a company’s resource allocation process is not managed masterfully, what emerges from it can be very different from what management intended…. They didn’t keep the purpose of theur lives front and center as they decided how to spend their time, talents, and energy.” (p.8-10)

“I promise my students that if they take the time to figure out their life purpose, they’ll look back on it as the most important thing they discovered at HBS.  If they don’t figure it out, they will just sail off without a rudder and get buffeted in thevery rough seas of life.” (p. 12)

“Allocate Your Resources: … In contrast, investing time and energy in your relationship with your spouse and children typically doesn’t offer that immediate sense of achievement… If you study the root causes of business disasters, over and over you’ll find this predisposition toward endeavors that offer immediate gratification.” (p. 16 – 17)

“Create A Culture: … The theory arrays these tools along two dimensions – the extent to which members of the organization agree on what they want from their participation in the enterprise, and the extent to which they agree on what actions will produce desired results.” (p. 18)

“Like employees, children build self-esteem by doing things that are hard and learning what works.” (p. 21)

“Avoid The “Marginal Costs” Mistake: … If we knew the future would be exactly the same thing as the bast, that approach would be fine. … The marginal cost of doing something wrong ‘just this once’ always seems alluringly low.” (p. 22)

“Remember The Importance Of Humility: … We all decided that humility was defined not by self-deprecating behavior ot attitudes but by the esteem with which you regard others.” (p. 26-27)

“Choose The Right Yardstick: … Don’t worry about the level of individual prominence youhave achieved; worry about the individuals you have helped become better people.” (p. 29-30).

2. Rinpoche S (1992) The Tibetan Book of Living and Dying, ed. P. Gaffney and A. Harvey (San Francisco: HarperOne).

“Foreword by His Holiness the Dalai Lama; … In this timely book, Sogyal Rinpoche focuses on how to understand the true meaning of life, how to accept death, and how to help the dying, and the dead.” (p. ix)

“Samten’s death was not an easy one. The sound of his labored breathing followed us everywhere, and we could smell his body decaying.” (p. 3)

“What all of this is showing us, with painful clarity, is that now more than ever before we need a fundamental change in our attitude to death and dying.” (p. 10)

“So from the Tibetan Bddhist point of view, we can divide our entire existence into four continuously interlinked realities: (1) life, (2) dying and death, (3) after death, and (4) rebirth.  These are known as the four bardos: (1) the natural bardo of life, (2) the painful bardo of dying, and (3) the luminous bard of dharmata, and (4) the karmic badro of becoming.” (p. 12)

“Death is a vast mystery, but there are two things we can say about it: It is absolutely certain that we will die, and it is uncertain when or how we will die.” (p. 15)

“Bar mean ‘in between’ and do means ‘suspended’ or ‘thrown.'” (p. 102)

“Masters often use this particular comparison to show how difficult it is to maintain awareness during bardo states. … By following the training of these practices, it is actually possible to realize the states of mind while we are still alive.” (p. 108-109)

“Of all the ways I know of helping people to realize the nature of the mind, that of the practice of Dzogchen, the most ancient and direct stream of wisdom within the teaching of Buddhism, and the source of the bardo teaching themselves, is the clearest, most effective, amd most relevant to the environment and needs of today… Human beings have come to a critical place in their evolution, and this stage of extreme confusion demands a teaching of comparably extreme power and clarity… The Dzogchen masters are acutely aware of the dangers of confusing the absolute with the relative… What this means is that the entire range of all possible appearances, and all possible phenomena ina all the different realities, whether sansara or nirvana, all of these without exception have always been and will always be perfect and complete, within the vast and boundless expanse of the nature of mind.” (p. 150-153)

“When people ask me how best to give someone permission to die, I tell them to imagine themselves standing by the bedside of the person they love and saying with the deepest most sincere tenderness: ‘I am here with you and I love you.  You are dying, and that is completely natural; it happens to everyone.  I wish you could stay here with me, but I don’t want you to suffer any more.  The time we have had together has been enough, and I shall aways cherish it.  Please now don’t hold onto life any longer.  Let go. I give you my full and heartfelt permission to die. You are not alone, now or ever.   You have all my love.” (p. 183)

3. Silberman S (2015) Neurotribes: The legacy of autism and the future of neurodiversity. Penguin.

“One of the hardest things about having a child with autism, parents told me, was struggling to maintain hope in the face of dire predictions from doctors, school administrators, and other professionals who were supposed to be on their side.  When Leah was diagnosed, an autism specialist told Marnin, ‘There is very little difference between your daughter and an animal.’ ” (p. 9)

“One of the most promising developments since the publication of ‘The Geek Syndrome’ has been the emergence of the concept of neurodiversity: the notion that conditions like autism, dyslexia, and attention-deficit/hyperactive disorder (ADHD) should be regarded as naturally occurring cognitive variations with distinctive strengths that have contributed to the evolution of technology and culture rather than mere checklists of deficits and dysfunctions.” (p. 16)

“Autism made its debut in the first edition of the bible of psychiatry, the DSM-I, in 1952, as ‘schizophrenic reaction, childhood type.” (p. 381)

4. Topol E (2013) The creative destruction of medicine: How the digital revolution will create better health care. Basic Books.

“These extraordinary accomplishments, from dissecting and defining DNA to creating such pervasive electronic technologies that immediately and intimately connect most individuals around the world  [discover of DNA, cellphone, personal computer, internet, digital devices, social networks, sequencing], have unwittingly set up a profound digital disruption of medicine… This really boils down to a story of big convergence: a convergence of all six of the major technological avances, likely representing the greatest convergence in the history of humankind…” (p. 5)

“The holy grail of evidence-based medicine is the large-scale randomized, double-blind, placebo-controlled clinical trial performed under the most rigorous considtions.  This means that typically 100,000 or more patients are randomly assigned…” (p. 21)

5.  Edson MC, Henning PB, Sankaran S (2017) A guide to systems research: Philosophy, Processes, and Practice. Translational Systems Sciences, Springer.

“This chapter services as an introduction to the evolution of systems theory and practice in order to articulate a framework for systems research.” (p. 1)

“A central challenge of systems research is expressing implicit understanding of change and making that explicit.” (p. 199)

6.  Freij A (2017) Mastering the impact of regulatory change: The capability of financial services firms to manage interaces.

“Framing the research problem: This chapter explains the importance of gaining a better understanding of what firms do to manage new requirements resulting from regulatory changes, what actions they take to implement the requirements and what separates successful firms in the market from others in this regard.” (p. 1)

7. Field J (2017). Designing service processes to unlock value. Second Edition.  Business Expert Press.

“We then delve further into the concept of value and what it means to each of the participants in the service process.  The value co-creation framwork…” (p. 7)


Simple framework for rethinking future jobs

There is a lot of speculation about jobs of the future.

How might one approach rethinking education and future jobs?  Here is one framework based on the social, mental, physical component tasks of work:

Many entry-level service sector jobs have social task components, requiring apprenticeship education.

Many high-end service sector jobs have mental task components, requiring higher education.

Many of the disenfranchised workers – manufacturing, transportation, construction, maintenance, agricultural jobs – have physical task components, typically requiring hands-on with specialized equipment education – often tools in a neighbor’s garage.

For this last group of workers, who enjoy the physical task components of their work,  the “maker movement” seems to be a possible high-tech enabled route, requiring apprenticeships to learn the latest “hands-on” technologies…

….as documented by Mark Hatch in his book….

Hatch M (2013) The maker movement manifesto: rules for innovation in the new world of crafters, hackers, and tinkerers. McGraw Hill Professional.
“Manifesto: Make, Share, Give, Learn, Tool Up, Play, Participate, Support, Change” p. 1-2
“But because the maker revolution is physical, it is destined to be bigger.” P. 3
“A 98 percent reduction in the cost of launching a product or company means, for example, that what used to cost $100,000 now costs just $2,000.” P. 7
“Tools are getting easier to use, they are more powerful, and they are cheaper to acquire than at any other time in history.  Materials are becoming more accessible, more sophisticated, and more fun to work on and with.” P. 23
“The key thing here is that the costs of resources for a start-up are falling” p. 43
“The largest untapped resource on the planet is the spare time, creativity, and disposable income of the ‘creative class.’” P. 52

This framework for rethinking future jobs should also encourage at very young age, multidisciplinary systems T-shaped thinking which can be very hands on and include field trips to see how things work in cities or self-sufficient home/farms, and other places where people work in smart service systems – see for example T-shapes skills, depth and breadth, which IBM embraces. Depth for problem solving and doing (mental, physical), and breadth for communications (social).  The adaptiveness of T-shaped professionals for future work and innovation is the shift from specialized I-shapes, to add breadth for adaptiveness to the deep I-shapes, which are still needed of course, they just need to be more adaptive and flexible to thrive in the age of accelerations.

Smart Service Systems

Investment in smart, people-centered service systems (NSF):
2016 $13M:  https://www.nsf.gov/news/news_summ.jsp?cntn_id=189628
2015 $10M:  https://www.nsf.gov/news/news_summ.jsp?cntn_id=136268

Would be great to get funding up to $100M a year from ~$10M  year over the last few years.

Smart service systems = socio-technology systems with artificial intelligence/machine learning to augment people in their social roles, where economics and public policy are also considered… so basically an interdisciplinary approach to real world systems with people, technology, and money flows and rules/laws, in them.  Because these systems learn, some people prefer cyber-social learning systems to smart service systems.

This is the latest NSF workshop – let me know, you are invited if you have an interest – March 29-30, Santa Clara, CA: http://www.servicescienceprojects.org/ISSIPNSF/

Theme: industry-university collaboration for smart service systems (this includes AI for digital cognitive systems for all occupations)



Predicting discoveries or automated hypotheses from the literature

Spangler S, Wilkins AD, Bachman BJ, Nagarajan M, Dayaram T, Haas P, Regenbogen S, Pickering CR, Comer A, Myers JN, Stanoi I (2014) Automated hypothesis generation based on mining scientific literature. InProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining 2014 Aug 24 (pp. 1877-1886). ACM.  URL: http://scholar.harvard.edu/files/alacoste/files/p1877-spangler.pdf

This early study tackles a basic problem that is challenging progress in every field of human intellectual activity: we have become much better at generation of information than at its integrative analysis. This leads to deep inefficiencies in translating research into progress for humanity. No scientist can keep up with the unrelenting flow of new studies and results, even within specialized fields.

The method is trained by chronologically ordering the literature, and using the past to try to predict the future.

Eight unsolved grand challenges of AI/CogSci

The eight unsolved grand challenges of AI/CogSci are readily apparent if one watches a child growing up to adulthood, and noticing capabilities that arrive at different ages.

The list below provides a set of goals for students building open source AI software for smartphones.  Moreover, to stimulate university startups, students should be learning to build, understand, and work with an open source cognitive assistant on their smartphones that helps them learn, plan their career opportunities, develop cognitive assistants for all occupations they plan to enter.

The above student projects will become much easier, once these eight unsolved grand challenges of artificial intelligence and cognitive science have been solved:

People these capabilities developing from 0-5 years of age:
(1) minute experience (Fiore)
(2) episodic memory (Schank, Socher)

Oddly enough, (2+) deep learning for perception and action is getting pretty well solved (some super-human capabilities) – and this is why so many people think AI is solved, or nearly about to be solved, because they think this one think near the bottom-middle of the cognitive capability hierarchy allows everything to be solved quickly – and it does not.  There are things lower and higher in the stack of cognitive capabilities that remain very challenging – grand challenges, in fact.

People these capabilities developing from 5-10 years of age
(3) commonsense reasoning (Lenat)
(4) social interactions (Forbus)

People these capabilities developing from 10-15 years of age
(5) fluent conversations (Klein)
(6) ingest textbooks (Etzioni)

People these capabilities developing from 15-20 years of age
(7) ingest regulations (Searle)
(8) collaboration augmentation (Engelbart)

People require about 10 millions minutes of experience to acquire these capabilities and become an adult in society.

Then adult people require about 2 million minutes of experience to go from novice to expert in an occupation or social role where experts already exist to learn from.

In slightly more detail:

(1) minute experience (Fiore) – requires representing both external inputs and internal inputs – no one really knows how one minute of experience works in a person.
(2) episodic memory (Schank) – requires building a dynamic memory that experience can be added to, and performance on certain tasks improves and does not degrade with additional experiences.

See minute 50 for dynamic memory by Socher Salesforce: https://www.youtube.com/watch?v=oGk1v1jQITw

“Cartoonization” might be a good approach to explore – see the work of Devi Parikh – https://filebox.ece.vt.edu/~parikh/CVL.html

Cartoonization is summarizing a long series of videos into a cartoon that is then part of a rank and retrieve question answering systems – episodic dynamic memory.  Killer app for smart cameras might be cartoonization – a TensorFlow-based system being taught to perform the “killer end-user app – cartoonization or summary cartoons” from a smart camera that builds an episodic memory of the last decade, year, month, week, day, hour views – reduced to a two minute cartoon of “expectation violation” or “interesting incidents” over those time periods… rank-and-retrieve Q&A on the cartoon might also be good.

(3) commonsense reasoning (Lenat) – requires reasoning changes to be compiled for rapid memory lookup.
(4) social interactions (Forbus) – requires animal level and then beyond animal level awareness and modeling of others.
(5) fluent conversations (Klein) – very hard, how do people do it?
(6) ingest textbooks (Etzioni) – very hard, especially when diagrams are included, etc.
(7) ingest regulations (Searle) – very hard, to go beyond social rules and manners, to become good at understanding the laws and institutions that shape behavior.
(8) collaboration augmentation (Engelbart) – very hard, requires people first that people know how to collaborate, and then people with cognitive assistants to interact fluidly on tasks as well.

Rob Farrell (IBM Research) and Paul Maglio (IBM Research, UCMerced) look at the list of capabilities above, and were not satisfied with my list – since they wanted clear tasks that a machine would have to perform, not a loose description of a vague capability like commonsense reasoning.

Rob wrote:

Is the idea to break these down according to cognitive functions?  [JIM: Actually developmental capabilities sequence, mental simulation of a child growing up]

I feel like AI challenges should be an obvious, verifiable, optimal, or indistinguishable from humans. [JIM: Yes, AI systems need to accomplish clear tasks to be evaluated.]

One way to do this is to have the challenges not jut focus on the early (“ingest”) or later (“common sense reasoning”) parts of the process, but go end-to-end and drive research in the various cognitive functions but include the target function. Another reason for this is that cognitive functions tend to be more intertwined than we think (Lakoff etc.). [JIM, yes – they are highly intertwined]

So the challenges could be things roughly in the categories you proposed but elaborated in this way. Here is a quick crack at it: [JIM: Thanks so much Rob – great first crack at it]

1) experience representation – generating a natural language description of a complex physical object (e.g. car) from a video of it performing a range of functions (e.g., avoidance)
2) episodic memory – writing a diary every day for a year based on online chat interactions , social network messaging, etc. with a simulated “life” and then answering questions about these “personal” experiences. Also measure degree of connection to others before and after.
3) common sense reasoning – carrying on a dialogue (speech) about the nuances complex concepts such as what counts as conservative or liberal politics or whether an outdoor scene is ‘beautiful’
4) social interactions – robotically navigating a complex physical space (e.g., a Disney park) by interacting with people (e.g., “excuse me”) and objects (e.g, pushing a turnstile).
5) natural conversation – learning another language from audio and text examples enough to respond to questions from native speakers about a complex activity (e.g., what is sold by this store?) with enough accent, grammar, word choice etc. to be understood by the askers
6)  reading (textbook) – Learn math from textbooks and apply to a novel domain by reading different textbooks about that domain (e.g. physics)
7)  reading (lawbook) – make judgements on legality similar to human judges on complex decisions
8) collaboration augmentation – participate in a collaborative activity with two other people to speed up the activity and improve its quality

Additional comments:
1) proposal is too hard, much like an expert system – I am looking for data traces through time that include external environment data as well as hidden internal data – see/hearing and thinking data trace.  Much more difficult, and fundamental what I am asking for.   Robot learning at CITRIS People and Robots, Ken Goldberg, and the work of Pieter Abbeel is getting close.

2) I like this one – auto-diary idea – converting trace of a person behavior into something detailed

3) proposal is too hard, like a debater – I am looking for something that is quickly able to be surprised by commonsense reasoning violations

Commonsense approaches: http://www.kdnuggets.com/2016/08/common-sense-artificial-intelligence-2026.html

Startups looking at deep learning and commonsense reasoning: http://www.technomontreal.com/en/news-center/news/microsoft-acquires-deep-learning-startup-maluuba-yoshua-bengio-to-have-advisory

A long history with Lenat and Cyc: https://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine/

4) sort of OK, like a robot dog at a theme part

5) kind of like this learning-another-language idea – vocabulary, context, matter – but looking for something better than keyword search of the web, but with commonsense reasoning and social interaction playing a role.

6) Allen Institute has a project to ingest a textbook and answer the questions at different grade levels -this one is OK.

7) This is more of 6) but for law books with reasoning about institutions and laws – but ingesting and answering law book questions is the right direction.   RegTech (Regulation Technologies) on the rise.

8) Right – being able to think about a collaborative project, if different people with different skills are vailable – managers have to do this a lot.


To read about task versus ability evaluation of AI systems read this:

To read about Psychometric AI testing read this:

To see a nice CHC diagram, check this:

Jobs of the Future

Alan Kay one of my mentors at Apple during the 1990’s was well-known for saying the best way to predict the future is to invent it…..

President James Mellinchamp (Piedmont College, Georgia) just sent me this nice list of 55 jobs of the future, by Futurist Thomas Frey: http://www.futuristspeaker.com/business-trends/55-jobs-of-the-future/.

What I especially like about the list is it has different sections – Jobs Before 2020 (1-26), The Dismantlers (27-32), and Jobs After 2030 and Beyond (33-55).

Jobs Before 2020: Augmented Reality Architects, Alternative Currency Bankers, Seed Capitalists, Global System Architects, Locationists, Waste Data Manager, Urban Agriculturalists, Business Colony Managers, Competition Producers, Avatar Designers, Avatar Relationship Managers, 3D Printing Engineers, 3D Food-Printer Engineers, Book-to-App Converters, Social Education Specialists, Privacy Managers, Wind Turbine Repair Techs, Data Hostage Specialists, Smart Dust Programmers, Personality Services, Smart Contact Developers, Nano-Medics, New Science Philosopher-Ethicists, Organ Agents, Octogenarian Service Providers, Elevated Tube Transport Engineers

The Dismantlers: Prison System Dismantlers, Hospital and Healthcare Dismantlers, Income Tax System Dismantlers, Government Agency Dismantlers, Education System Dismantlers, College and University Dismantlers

Jobs in 2030 and Beyond: Drone Dispatchers,  Brain Quants, Tree-Jackers, Plant Psychologists, Extinction Revivalists, Robotic Earthworm Drivers, Gravity Pullers, Time Hackers, Clone Ranchers, Body Part & Limb Makers, Memory Augmentation Therapists, Time Brokers – Time Bank Traders,  Space-Based Power System Designers, Geoengineers – Weather Control Specialists, Plant Educators, Nano-Weapons Specialists, Lip Designers, Mass Energy Storage Developers, Earthquake Forecasters , “Heavy Air” Engineers,  Robot Polishers, Amnesia Surgeons,  Executioners for Virus-Builders

Some of my favorites bolded above – and I would like to add “Better Innovation Namers” to the list of Jobs Before 2020.   Much needed job for sure.

My own predictions are show on slide #11 here in this presentation:  http://www.slideshare.net/spohrer/understanding-20161128-v8

The summary of my predictions is that by 2025 a big job will begin to be helping people to use their cognitive assistants to be better professionals, such as doctors and lawyers – healthier-people helpers, and more-just-society helpers.  By 2035 a big job will begin to be helping people to use their cognitive mediators to launch multiple startup companies.   By 2055 a big job will begin to be helping people to use their cognitive mediators to manage better their every growing digital workforce of about 100 digital workers per 1 biological person.  Of course, the best way to predict the future is to inspire the next generation to build it better.  This “build it better” will shift and become rapidly rebuilding from scratch for a number of reasons.  To get a hint of why this is so, check out this Circular Economy: From Consumer to User video that speaks to products becoming service offerings: https://www.youtube.com/watch?v=Cd_isKtGaf8

Here is one last perspective on jobs of the future from Andrew McAfee (MIT) in a nice podcast interview from the economist – speech recognition from a free iPhone app translated some key sections for me below…

Babbage: The automation game
How quickly will robots disrupt global industries and what will the implications be?
We explore with economist Andrew McAfee at the World Economic Forum in Davos.


[7:00] Automation vs Transformation: A clear distinction?

No, it is really not a clear distinction, because technology always does two things simultaneously: it substitutes for people, and the tasks that they do, and at the same time it’s a compliment, it’s an aid for people on the tasks that they do; sometimes even within the same job.  My exhibit A for that is bank tellers and if you remember back as soon as ATM machines came out we hear again that litany of prediction that bank tellers were about to become an endangered species, and if you look at what actually happened the number of bank tellers, in America at least, rose fairly steadily for decades because banks opened up more branches and we actually needed those tellers to do a different set of things but we still really needed those people.  We hit peak bank teller in America about a decade ago and as far as we can tell the total number of bank tellers in America has dropped by about 20% since that peak and that’s not because of one particular tech breakthrough, it’s because of a combination of ATM machines, PC banking, smartphone based banking, electronic payment systems, the technology progress is cumulative and eventually it can turn net complementing jobs into net substituting of jobs.

[9:00] Offshoring vs Automation:

America remains a manufacturing powerhouse, if you look at output.  We are second to China now, but we turn our more manufactured goods than Germany, Italy, France and India combined, and output goes up almost every non-recession year.   Now the year of peak American employment in manufacturing was 1979, and we are down a significant amount of total jobs since then.  That is not a globalization story, that is a technology story and an automation story. Tom you bring up the excellent point that we don’t see this recent amazing technology surge in the productivity statistics yet. I think that’s to be expected, if people are leaving relatively high productivity manufacturing jobs and moving into relatively low productivity service sector jobs. You would expect to see productivity as we measure it go down then.

Now what I anticipate happening is that we’re going to see a boost in service sector productivity thanks to things like excellent speech and voice recognition systems.  The ability of the new technologies to scan huge amounts of pretty unstructured information and generate a pretty clean insightful report out of that.  I think we also need to include in this discussion the overall greater material prosperity and abundance, because of tech progress, is good news.  Now job loss and wage stagnation are real concerns, and I think we see with things like Brexit and the election of Donald Trump what can happen when people feel left behind by the progress that’s going on. So I don’t mean to minimize those concerns at all, but we need to keep in mind we’re creating an overall more prosperous world.  The pressing question for us is how we share that prosperity.

[11:00] What we have to look forward to

Think about a near future where the elderly, and the disabled, and the blind can get around much more easily than they can now.  Think about a future where absolute best in the world medical diagnosis is available, not just to people who live near the great research hospitals in the world, but via pieces of technology and screens and cameras and labs on the chip and smart phones all around the world. Now that is not a science-fiction vision of the future.  Each of those things we see very clearly right now. For me the question is how do we get there while having most people feel like they’re part of that and that they are contributing to it and they have some sense of dignity and meaning and community, while these bizarre technologies are, not happening to them, but happening around them and happening in their lives and their families.  That’s the vision I would like to articulate.

Annual re-read list – my top ten books

From bottom (longest time on list) to top (shortest time on list)… I try to re-read once a year if I can… http://service-science.info/archives/4333

(10) Fagin R, Halpern JY, Moses Y, Vardi M (2004) Reasoning about knowledge. MIT press.

One of the most cited books in artificial intelligence – a must read.  Reasoning about the value of knowledge has yet to be written.

(9) Goodsell DS (2009) The machinery of life. Springer Science & Business Media.

Speed of molecules is a key insight the above: http://book.bionumbers.org/how-fast-do-molecular-motors-move-on-cytoskeletal-filaments/

(8) Moss D (2007) A concise guide to macroeconomics. Harvard Business School Press, Boston, Massachusetts.

If you only have 30 minutes – this is a fine substitute: https://www.youtube.com/watch?v=PHe0bXAIuk0

(7) De Chardin T. Pierre (1959) The phenomenon of man. Trans. Bernard Wall. New York: Harper & Row.

A complex person I wish I had known: https://en.wikipedia.org/wiki/Pierre_Teilhard_de_Chardin

(6)  Searle JR (1995) The construction of social reality. Simon and Schuster.

Culture is amazing emergent phenomenon – and how we come to agree on certain institutional facts is fascinating.

Big impact on culture and institutional facts coming; see Doug Lenat (Dr. Commonsense Reasoning) wrote about “weak immortality” in this issue of AI Magazine article – search for “weak immortality”

(5) Peavy RV (1997) SocioDynamic Counselling: A Constructivist Perspective.  “The person is not the problem, the problem is the problem.” Trafford.

What is subjective experience and how can the re-telling and re-making of the stories of our lives help us adapt to tragedy and suffering?

(4)  Auerswald P (2011) The coming prosperity: How entrepreneurs are transforming the global economy. Oxford University Press.

How can we change the culture of the world to make startups the new olympic sport of choice?

(3) Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press.

T-shapes skills and mindset without using that terminology – socio-technical system design loop and human techno-extension factor.

(2)  Mohr BJ, Amelsvoort PV (2016) Co-creating humane and innovative organization:  Evolutions in the practice of socio-technical system design.  Global STS-D Network Press.

Service science studies socio-technical system evolution of capabilities, constraints, rights, and responsibilities.  The cost burden of responsibilities is politics.

(1) Dartnell L (2015) The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm. Penguin.

How can we change the culture of the world to make rebuilding rapidly from scratch a priority?

(1 above) Displaced from re-read stack (temporarily?):
Hawley AH (1986) Human ecology: A theoretical essay. University of Chicago Press.

(2 above) Displaced from re-read stack (temporarily?):
Simon HA (1996) The sciences of the artificial. MIT press.

(3 above)  Displaced from re-read stack (temporarily?):
Deacon TW (2011) Incomplete nature: How mind emerged from matter. WW Norton & Company.

Learning mechanisms – evolution, brain, culture – are important – if you don’t have time for Deacon, then a fine summary (of another book) to substitute is:

(4 above) Displaced from re-read stack (temporarily?):

Norman DA (1993) Things that make us smart: Defending human attributes in the age of the machine. Basic.

However, should re-add this book, since I find I am promoting it more and more, because of discussions about intelligence augmentation as well as living with complexity.  However, I find it Auerswald’s focus on entrepreneurship key, and his new book “Code Economy” combines the entrepreneurship, recombinations, design, and augmentation themes nicely.  Still, Norman also has this more recent book that is relevant to today’s design challenges:
Norman DA (2010) Living with complexity. MIT press.https://www.amazon.com/Living-Complexity-Press-Donald-Norman/dp/0262014866/
And I also like this related book by Samuel Arbesman a lot:
Arbesman S (2016) Overcomplicated: Technology at the Limits of Comprehension. Penguin.


Still they are not in my 2017 stack of top ten books to re-read, though I re-read them….  so many good books to re-read, and so little time…

Fiction and other genre (spiritual) go elsewhere – but my all time favorite science fiction book, which I truly love, is without a shred of doubt:

Stephenson N (1995) The Diamond Age: Or, A Young Lady’s Illustrated Primer.  Bantam Spectra.

The need for next generation cognitive curriculum

(1) For CEOs to understand cognitive

How does one win?

Tesla Gathers More Autopilot Miles in a Day Than Google Has in Its Whole Program

For CEO’s who want to truly understand cognitive, I recommend reading or listening to the following on next cross country flight…

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World 1st Edition
by Pedro Domingos (Author)

For under $20 one can get the audio version, and listen to it as well…   just 6 hours at 2-3x listening speed.

They can also read the book summary in less than one hour:

The gist is people evolved cognitive capabilities because of powerful learning processes applied to lots of experience: evolution, brain, culture.

10 million minutes experience is required to become an adult in our society.  2 million minutes more to become an expert worker – pilot, doctor, etc.

Experience is interaction with the environment and/or other cognitive entities.

How many minutes of experience do you learn from in your business or work environment every day? 600 minutes per day? what could increase this?

What are the powerful learning processes that you are applying to your front-stage customer interactions and back-stage employee/supplier interactions?

“Cognitive” is an adjective to describe the system that learns to improve performance from experience, not simply the technology or computing piece.

(2) For IBM to lead in cognitive

For next generation cognitive curriculum I have proposed….

… a lifelong learning and career planner for smartphones – cost $1M per year for 2 years for basic version and invest for 5 years for deluxe version.

A big selling point for this system is that is it will help people write their annual performance review and update their resume as well!

(3) For advanced learners… the future

… it is important to teach people about the five unsolved problems of digital cognitive systems… probably all five will be solved in 10 years…

(1) episodic memory (Schank books “Dynamic Memory” and “Scripts, Plans, Goals, and Understanding”)
(2) commonsense reasoning (Doug Lenat’s CYC is a foundation, not a solution)
(3) social interactions (Ken Forbus had a nice piece in AI Magazine on “Software Social Organisms”)
(4) fluent conversation (Builds on the items in this list, plus Speech Acts and much more…)
(5) ingest textbooks (Allen Institute has this as a grand challenge, and SRI did some good work too)

(1) – (4) are pretty much accomplished in the first 10 million minutes of experience of a person…

(5) can be done then, but requires 2 million minutes of experience per domain for a person…

when these unsolved problems are solved, then anyone will be able to build, understand, and work with digital cognitive systems in their personal and professional life.

…just like having a service, assistant, collaborator, coach, mediator in the form of a person in your life today… but one that knows you better in some ways than you know yourself….

Here is the DRAFT video that explains all of the above in more details…

To which add this final thought/question, based on this quote from Domingos “Master Algorithm” book:

Domingos wrote: Natural learning itself has gone through three phases: evolution, the brain, and culture. Each is  product of the previous one, and each learns faster. Machine learning is the logical next stage of this progression. Computer programs are the fastest replicators on Earth: copying them takes  only a fraction of a second. But creating them is slow, if it has to be done by humans. Machine learning removes that bottleneck, leaving a final one: the speed at which humans can absorb change.

My question: Since, it takes about 10 million minutes of experience for a person, embedded in our culture, to progress to adulthood, and an additional 2 million minutes of experience more to progress from novice to expert – in areas as diverse as pilots to doctors to dancers and musicians and AI researcher….  biological cognitive systems (BCS) process one minute of experience in one minute, but digital cognitive systems (DCS) will be able to process millions of minutes of experience in a second – so booting DCS up could be quite rapid – once we understand “experience” bootup better. What is “the best representation of one minute of experience” for BCS and DCS development?