The National Level Impact of Predicted Work Futures. Prof Bernard Yeung Dean and Stephen Riady Distinguished Professor, NUS Business School

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2 The National Level Impact of Predicted Work Futures Prof Bernard Yeung Dean and Stephen Riady Distinguished Professor, NUS Business School

3 Overall theme In human history, science and industry revolution economic development IHL always play a role: Science industrial revolution facing the future of work Gives us enlightenment, skills development and values Supports a virtuous cycle to improve human lives Comment on the National Impact on predicted future of work IHL, Business and Government partnership Copyrights Bernard Yeung 2018

4 Value of IHL over time The Renaissance (15 th to 16 th centuries) critical thinking, planted the seeds for Advancement in Science (16 th, 17 th and into the 18 th centuries). Ancient IHL played a significant role Keeping alive literacy, theology, philosophy, math, astronomy, medicine, and pushing up scientific method. Nicolaus Copernicus University of Krakow (in Poland) ( ), William Gilbert Cambridge U, [magnet electricity] ( ), Galileo Galilei university of Pisa ( ), Johannes Kepler University of Tubingen, (Germany) ( ) Johannes Kepler University of Tubingen. [law of planetary motion] ( ), William Harvey Cambridge and University of Padua [blood circulation] ( ), Christiaan Huygens [mechanics, optics] ( ), Isaac Newton Trinity Cambridge, ( ),.. Copyrights Bernard Yeung 2018

5 Value of IHL over time Science and constraints bred the Industrial Revolution (mid-18 th century) Applied and translational science improved efficiency and living standards o Modern businesses, organizations, and matching institutions Firm hierarchy, finance, accounting, governance,. A virtuous cycle: growth more resources for more technological growth more changes Copyrights Bernard Yeung 2018

6 Higher education s role in the virtuous cycle Training grounds to produce people to serve the church and government Produce thinkers, scientists, Enlightenment, values, make people better able to positively lead and embrace changes The same in the East:» 范仲淹执教应天府书院时, 经常教导学生要 从德, 而不能仅以科举仕进作为求学的最终目的 o 学 问 思 辨 行. Since the 20 th Century, much growth in applied science University education for marketable knowledge, skills or marketable signal o 60s, 70s 80s, 90s o Born business schools o Born analytical social sciences Copyrights Bernard Yeung 2018

7 The pace of Industrial Revolution has gone exponential in the 21 st Century Material science, additive manufacturing, bio-medical, genomes ICT, Big Data, Algorithm, ML, AI.. o Technological progress has changed business models, formats, human behavior and relationship, and o o o Uber, robot advisory, Fin-Tech, Accounting-Tech, Marketing-Tech, Legal-Tech, Med-tech Drones, driverless this and that, people less this and that Fast and unexpected changes/disruptions Big Data, ML and AI fast instantaneous optimization and judgment that beats human (e.g., high speed trading algorithms) o o Change job requirements, human retreat? Don t be beaten by machines lest the data rich and information poor paradox o Interactive collaboration between machine and human Copyrights Bernard Yeung 2018

8 National level impact? - Economy, Business, Sociology, Society, Culture, Politics,. Focus on the co-evolution in IHL, Business and Government o Students, people face job uncertainty: opportunities vs stresses o Companies have to be ready for a digital world o Government see new opportunities and new tasks Copyrights Bernard Yeung 2018

9 Education has to change The value of traditional training drops; more need on general skills, ability to self-learn, to self-discover solutions The value of a university/business degree is grounded on K Q S C» K (Knowledge) Q (quality of judgment) S (skills in working with people) V (values)» Weightage changes: Value of K drops but of Q and S rises while V is always there» New requirement: Adopt to smartpeople working with intelligent machines How do we adjust? Cross disciplinary Principle, data, technology and human literacy; Critical thinking, divergent thinking, constructive dissatisfaction Practicum (bridge academic and real world, work as a team) Shorter and focused programs but continuous learning A holistic learning community (government, businesses, academic partnership)

10 Businesses change An AI culture starting from the top to the bottom Data scientists, staff, and CEO as a team with machine Collect, collate data, algorithm to serve users Beware of user journey, interaction between data, algorithm, platform dynamic and people Use human cognitive functionality to utilize data and powerful machine learning to create/grab opportunities Learning culture Machine learning is often black box in black box out and has flaws data definition changes, feedback effect changes, missing dimension continuous investment in human resources, training, and customized executive education programs becomes a competitive necessity Culture of value fast grab of short run bottom-line is scary e.g., beware of statistical discrimination

11 Government Usual suspect: Deal with income distribution and nationalism, nativism,. Deal with new things like cryptocurrencies, ICO, crowd funding, platform economy Use digitalization and algorithms to produce smart cities, smart health care system, smart tax collecting, social and financial stability, etc.

12 Fundamental issues in governing Technological progress allows government or the private sector to have more records, thus, possibly more information and expands everyone s feasibility set A system design problem in governing in the digitalized world: centralized vs a decentralized system in generating (or not generating) the data and in allowing access of the data. Should a government or a big business have all the transaction records Is this too big a temptation in developing an overbearing government? An ugly monopoly? Generically How and what data should be generated and collated by whom? Who should have access to the data? Data are not information. Data rich but information light?» Who should generate the information? With the right incentives? Social analytics needed. Who should have what market power? Regulatory power? Political power?» Power corrupt people? Power breeds angels? Challenge: can we let something we do not know well with no one accountable take too much space and responsibilities

13 Fast changing world We do not know what we do not know Know what we know and don t know Do not know what we know and what we don t know Collaboration across government, business, academics and people has been a proven way to climb a virtuous cycle Copyrights Bernard Yeung 2018

14 Finish Thanks for listening

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