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AI Perspective
Sega Cheng (程世嘉)
Co-founder/CEO
StraaS & LIVEhouse
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MixTaiwan 20170208-趨勢-程世嘉-future work and education
The world as we know it
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Self-Driving Truck OTTO - 2016
The world's first shipment
by self-driving truck, a
120-mile journey with no
driver in front seat.
190 KM trip
50,000 cans of beer
Technology
Source: NVIDIA
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Deep Learning
ImageNet Challenge 2011 - 2015
What Deep Learning is Capable of
What Deep Learning is Capable of (cont’d)
Games for Training AI - Self-Driving Cars
Source: DeepDrive.io
Artificial General Intelligence
Jobs, Income, Automation
John Henry
1870
Talcott,
West Virginia
“The Influence of
machinery on the
interests of the
different classes of
society”
- David Richardo 1821
“The Return of the Machinery Question”
- The Economist 2016
Jobs
How Susceptible are Jobs to
Computerisation?
Carl Benedikt Frey · Michael Osborne - Sep. 2013
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Automated Vehicles Case Study (2016)
What Kind of Jobs will AI Create?
1.Engage w/ existing AI technologies
2.Develop new AI technologies
3.Supervise AI technologies in practice
4.Facilitate Societal shifts that accompany AI technologies
Credit: James Hodson
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Income
(不患寡而患不均, 孔子)
Top 0.01% Income Share, 1913 - 2015
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Wealth Inequality (World) - 2014
GDP per capita vs. Median Personal Income 2015 (US)
The great
decoupling
Non-Farm Labor and Corporate Profits Share of GDP
Trended up
over the last 2
years though
Is College Degree Important? Yes
Policy
(1940)
3 Attitudes towards AI
● Optimist: Ray Kurzweil, Peter Diamandis, Larry Page, Sergey Brin
● Pessimist: Stephen Hawking, Elon Musk, Nick Bostrom, Martin Ford
● Pragmaticist: Erik Brynjolfsson, Thomas Davenport
● Research Goal: The goal of A.I. research should be to create not undirected intelligence, but beneficial intelligence.
● Research Funding: Investments in A.I. should be accompanied by funding for research on ensuring its beneficial use,
including thorny questions in computer science, economics, law, ethics, and social studies, such as:
○ How can we make future A.I. systems highly robust, so that they do what we want without malfunctioning or getting
hacked?
○ How can we grow our prosperity through automation while maintaining people’s resources and purpose?
○ How can we update our legal systems to be more fair and efficient, to keep pace with A.I., and to manage the risks
associated with A.I.?
○ What set of values should A.I. be aligned with, and what legal and ethical status should it have?
● Science-Policy Link: There should be constructive and healthy exchange between A.I. researchers and policy-makers.
● Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of
A.I.
● Race Avoidance: Teams developing A.I. systems should actively cooperate to avoid corner-cutting on safety standards.
● Safety: A.I. systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and
feasible.
● Failure Transparency: If an A.I. system causes harm, it should be possible to ascertain why.
● Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory
explanation auditable by a competent human authority.
● Responsibility: Designers and builders of advanced A.I. systems are stakeholders in the moral implications of their use,
misuse, and actions, with a responsibility and opportunity to shape those implications.
● Value Alignment: Highly autonomous A.I. systems should be designed so that their goals and behaviors can be assured to
align with human values throughout their operation.
The Asilomar A.I. Principles (Jan 2017)
● Human Values: A.I. systems should be designed and operated so as to be compatible with ideals of human dignity, rights,
freedoms, and cultural diversity.
● Personal Privacy: People should have the right to access, manage and control the data they generate, given A.I. systems
power to analyze and utilize that data.
● Liberty and Privacy: The application of A.I. to personal data must not unreasonably curtail people’s real or perceived liberty.
● Shared Benefit: A.I. technologies should benefit and empower as many people as possible.
● Shared Prosperity: The economic prosperity created by A.I.I should be shared broadly, to benefit all of humanity.
● Human Control: Humans should choose how and whether to delegate decisions to A.I. systems, to accomplish human-chosen
objectives.
● Non-subversion: The power conferred by control of highly advanced A.I. systems should respect and improve, rather than
subvert, the social and civic processes on which the health of society depends.
● A.I. Arms Race: An arms race in lethal autonomous weapons should be avoided.
● Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future A.I.
capabilities.
● Importance: Advanced A.I. could represent a profound change in the history of life on Earth, and should be planned for and
managed with commensurate care and resources.
● Risks: Risks posed by A.I. systems, especially catastrophic or existential risks, must be subject to planning and mitigation
efforts commensurate with their expected impact.
● Recursive Self-Improvement: A.I. systems designed to recursively self-improve or self-replicate in a manner that could lead
to rapidly increasing quality or quantity must be subject to strict safety and control measures.
● Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit
of all humanity rather than one state or organization.
The Asilomar A.I. Principles (Jan 2017) - Cont’d
Scrutability and Accountability
(AI’s Black-Box Problem)
EU: “Right to Explanation” April 2016
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Policy Responses - R&D, Education, Safety Net
1.Invest in and develop AI for its many benefits
a. Cyberdefense
b. Detection of fraudulent transactions and messages
2.Educate and train people for jobs of the future
a. Expand the availability of job-driven training and opportunities for lifelong learning
3.Aid workers in the transition and ensure broadly shared growth
a. Modernize the social safety net
Research & Development Strategic Plan
1.Make long-term investments in AI research
2.Develop effective methods for human-AI collaboration
3.Understand and address the ethical, legal, and societal implications of AI
4.Ensure the safety and security of AI systems
5.Develop shared public datasets and environments for AI training and testing
6.Measure and evaluate AI technologies through standards and benchmarks
7.Better understand the national AI R&D workforce needs
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
0.1%
Education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
MixTaiwan 20170208-趨勢-程世嘉-future work and education
Return of the MOOC
The current phase of digital
unbundling can pave the
way to more flexible,
lifelong learning journeys
McKinsey & Company
Learn how to re-learn quickly
Follow Me on Facebook
Sega Cheng
And we are hiring!

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MixTaiwan 20170208-趨勢-程世嘉-future work and education

  • 1. Future Work & Education AI Perspective Sega Cheng (程世嘉) Co-founder/CEO StraaS & LIVEhouse fb.me/segacheng
  • 3. We Enable Your Online Video Business
  • 5. The world as we know it
  • 7. Self-Driving Truck OTTO - 2016 The world's first shipment by self-driving truck, a 120-mile journey with no driver in front seat. 190 KM trip 50,000 cans of beer
  • 14. What Deep Learning is Capable of
  • 15. What Deep Learning is Capable of (cont’d)
  • 16. Games for Training AI - Self-Driving Cars Source: DeepDrive.io
  • 20. “The Influence of machinery on the interests of the different classes of society” - David Richardo 1821
  • 21. “The Return of the Machinery Question” - The Economist 2016
  • 22. Jobs
  • 23. How Susceptible are Jobs to Computerisation? Carl Benedikt Frey · Michael Osborne - Sep. 2013
  • 25. Automated Vehicles Case Study (2016)
  • 26. What Kind of Jobs will AI Create? 1.Engage w/ existing AI technologies 2.Develop new AI technologies 3.Supervise AI technologies in practice 4.Facilitate Societal shifts that accompany AI technologies Credit: James Hodson
  • 31. Top 0.01% Income Share, 1913 - 2015
  • 35. GDP per capita vs. Median Personal Income 2015 (US) The great decoupling
  • 36. Non-Farm Labor and Corporate Profits Share of GDP Trended up over the last 2 years though
  • 37. Is College Degree Important? Yes
  • 40. 3 Attitudes towards AI ● Optimist: Ray Kurzweil, Peter Diamandis, Larry Page, Sergey Brin ● Pessimist: Stephen Hawking, Elon Musk, Nick Bostrom, Martin Ford ● Pragmaticist: Erik Brynjolfsson, Thomas Davenport
  • 41. ● Research Goal: The goal of A.I. research should be to create not undirected intelligence, but beneficial intelligence. ● Research Funding: Investments in A.I. should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as: ○ How can we make future A.I. systems highly robust, so that they do what we want without malfunctioning or getting hacked? ○ How can we grow our prosperity through automation while maintaining people’s resources and purpose? ○ How can we update our legal systems to be more fair and efficient, to keep pace with A.I., and to manage the risks associated with A.I.? ○ What set of values should A.I. be aligned with, and what legal and ethical status should it have? ● Science-Policy Link: There should be constructive and healthy exchange between A.I. researchers and policy-makers. ● Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of A.I. ● Race Avoidance: Teams developing A.I. systems should actively cooperate to avoid corner-cutting on safety standards. ● Safety: A.I. systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible. ● Failure Transparency: If an A.I. system causes harm, it should be possible to ascertain why. ● Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority. ● Responsibility: Designers and builders of advanced A.I. systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications. ● Value Alignment: Highly autonomous A.I. systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation. The Asilomar A.I. Principles (Jan 2017)
  • 42. ● Human Values: A.I. systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity. ● Personal Privacy: People should have the right to access, manage and control the data they generate, given A.I. systems power to analyze and utilize that data. ● Liberty and Privacy: The application of A.I. to personal data must not unreasonably curtail people’s real or perceived liberty. ● Shared Benefit: A.I. technologies should benefit and empower as many people as possible. ● Shared Prosperity: The economic prosperity created by A.I.I should be shared broadly, to benefit all of humanity. ● Human Control: Humans should choose how and whether to delegate decisions to A.I. systems, to accomplish human-chosen objectives. ● Non-subversion: The power conferred by control of highly advanced A.I. systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends. ● A.I. Arms Race: An arms race in lethal autonomous weapons should be avoided. ● Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future A.I. capabilities. ● Importance: Advanced A.I. could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources. ● Risks: Risks posed by A.I. systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact. ● Recursive Self-Improvement: A.I. systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures. ● Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization. The Asilomar A.I. Principles (Jan 2017) - Cont’d
  • 44. EU: “Right to Explanation” April 2016
  • 47. Policy Responses - R&D, Education, Safety Net 1.Invest in and develop AI for its many benefits a. Cyberdefense b. Detection of fraudulent transactions and messages 2.Educate and train people for jobs of the future a. Expand the availability of job-driven training and opportunities for lifelong learning 3.Aid workers in the transition and ensure broadly shared growth a. Modernize the social safety net
  • 48. Research & Development Strategic Plan 1.Make long-term investments in AI research 2.Develop effective methods for human-AI collaboration 3.Understand and address the ethical, legal, and societal implications of AI 4.Ensure the safety and security of AI systems 5.Develop shared public datasets and environments for AI training and testing 6.Measure and evaluate AI technologies through standards and benchmarks 7.Better understand the national AI R&D workforce needs
  • 51. 0.1%
  • 57. The current phase of digital unbundling can pave the way to more flexible, lifelong learning journeys McKinsey & Company
  • 58. Learn how to re-learn quickly
  • 59. Follow Me on Facebook Sega Cheng And we are hiring!