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AI, Automation, and Economic Impact - National Security Implications

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The slides from my presentation at the United Nations (UNICRI)/ Shanghai Institutes for International Studies event in Shanghai.

Event Title: Artificial Intelligence – Reshaping National Security

Event Host: United Nations (UNICRI), Shanghai Institutes for International Studies

Date: December 17 – 18, 2018

Team Member: Daniel Faggella, Emerj Founder and CEO

Presentation Title: AI, Automation, and Economic Impact – National Security Implications

Article on Emerj.com: https://emerj.com/emerj-team-updates/dan-presented-at-a-joint-unicri-shanghai-institutes-for-international-studies-event/

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AI, Automation, and Economic Impact - National Security Implications

  1. 1. AI, Automation, and Economic Impact - National Security Implications Daniel Faggella, CEO at Emerj
  2. 2. Presentation Overview ●  Critical trends and commonalities for automate-able work ●  Job types and sectors with highest automation risk ●  Basic frameworks for thinking through automation impact in major sectors (including sectors critical for national security), and policy considerations ●  Considering artificial intelligence and automation as a competitive advantage on a national scale
  3. 3. Emerj AI Research ●  Emerj is a market research firm focused the implications of AI for business / government leaders ●  Organizations planning very expensive technology initiatives use us to support critical decisions ●  We work with clients as large as the World Bank, and startups who’ve only raised $10MM
  4. 4. Emerj AI Research ●  We examine AI across three dimensions: ○  Applications (Possibilities) “What’s Possible?” ○  Implications (Probabilities) “What’s Working?” ○  Strategy (Plans) “What Action Should We Take?” ●  Our work focuses on extrapolating themes and trends from thousands of interviews, surveys, and case-study assessments across critical AI sectors ●  Our largest areas of focus: Healthcare, Finance, Defense
  5. 5. 3 C’s of Job Automation Context, Connection, Coordination
  6. 6. 1 - Context ●  “Context” refers to the number of competencies required to perform a job. Automate-able work has predictable inputs, work steps, and outputs, tends to involve a very limited number of competencies (low context). ●  Blue-collar examples: ○  LOW Context: Welding on an assembly line ○  HIGH Context: Plumbing ●  White-collar examples: ○  LOW Context: Insurance underwriting ○  HIGH Context: Managing a procurement department
  7. 7. 2 - Connection ●  “Connection” refers to the ability to relate to other people on an interpersonal level, to empathize and attune to the emotions of others. ●  Examples: ○  LOW Connection: Welder on an assembly line ○  LOW Connection: Software engineer ○  HIGH Connection: Elementary school teacher ○  HIGH Connection: High-ticket salesperson ○  HIGH Connection: Hospice nurse
  8. 8. 3 - Coordination ●  “Coordination” refers to the ability to marshall teams and resources to reach an orgazation’s goals. ●  Examples ○  LOW Coordination: Nurse ○  LOW Coordination: Programmer / software engineer ○  HIGH Coordination: Sales manager ○  HIGH Coordination: Chief technology officer
  9. 9. 3 C’s in Depth Full Presentation: https://www.youtube.com/watch?v=4NEIwKooOoI
  10. 10. People Skills and Automation ●  Jobs that might involve people skills are not always safe from automation. ●  Checkout clerks and telephone customer support reps might use “people skills” on a daily basis, but their jobs are potentially automate-able. ●  Jobs that are predicated on coordination and connection are relatively safe from automation, but jobs where connection or coordination are “nice to have”, but not necessary for the job, are less safe from automation.
  11. 11. Sector-Specific Impact ●  The impact of AI on specific sectors (agriculture, banking, retail, etc.) should be expected to vary from country to country based on a variety of variables: ○  Processes / management ○  End product ○  Trade agreements with other countries ○  Access to materials ○  Timeline to AI / tech adoption ○  Labor costs (high cost = high automation incentive) ●  Deep-dives into an individual country (Romania example with World Bank) allow for more detailed sector assessment
  12. 12. McKinsey’s Research* ●  McKinsey sees the following sectors as ripe for automation in the near term:
  13. 13. Bloomberg’s Research* ●  Highest risk: Cashiers, waiters, retail salespeople, sales reps
  14. 14. Emerj Research ●  Our interviews and surveys in large enterprises yield a relatively predictable pattern of the “types” of jobs that are likely to be automated in major sectors like healthcare, finance, and heavy industry: ○  Data entry and basic data manipulation roles ○  Low-context customer service and sales enablement roles ○  Extremely wrote processing jobs (insurance underwriters) ○  Search and discovery lackeys (paralegals) ●  These low-context jobs (and jobs like them) have a reasonably high likelihood of near-term automation.
  15. 15. Planning and Preparation for Automation
  16. 16. Determining Sectors of Relevance ●  Assess the industries that matter most for national security, or international economic competitiveness. ●  Considerations: ○  What sectors do we suspect to grow most in the coming ten years? ○  What sectors represent the greatest opportunity to strengthen our national security position? ○  What sectors involve technologies that we might repurpose for defense / security objectives?
  17. 17. Determine Critical Processes ●  Determine the processes / aspects of this work that is the most automate-able (lacking the 3 C’s): ○  Agriculture: ■  Seed planting (for certain crops), weeding (for certain crops) ○  Services: ■  Customer service, rote white-collar paperwork ○  Manufacturing: ■  Machine maintenance, manual assembly ○  Etc.
  18. 18. Determine Policy Levers ●  Determine the policies that might help to: ○  Plan for with potential job loss scenarios ■  Examples: Determine plans to dead with losses in transportation jobs, customer services jobs, etc ○  Retrain or upskill workers in critical fields ■  Examples: Expand the skillsets of data entry and data management employees, possibly by giving them more department-specific contextual skills ○  Enhance international competitiveness ■  Examples: Relieve restrictions to data access, or provide tax benefits to incentivize certain kinds of innovation
  19. 19. Defense / Security Levers ●  Almost every major industry sector has AI-related applications that can be repurposed for defense objectives: ○  Heavy industry: IoT data and predictive maintenance ○  Online media: Behavior attribution and user profiling / predictions ○  Finance: Enterprise search and document understanding ○  Etc. ●  Nations with a knowledge of their sectors of strength will likely want to turn economic power into defense advantage.
  20. 20. Defense / Security Levers ●  The United States seems to be at least bringing these questions to bear. ●  My most recent presentation at National Defense University (for generals / colonels) was less about AI innovation, and more about broad-repurposing and private sector partnerships. ●  I suspect that a large part of our research in the defense sector in the coming years will focus on translating economic strength into defensive strength.
  21. 21. Artificial Intelligence and Economic / Defense Competition
  22. 22. “Proprietary Data Plume” ●  In 2017 we conducted a lengthy series of interviews with venture capital investors in Silicon Valley, asking them to explain their methods for determining the potential of an artificial intelligence company. ●  The “Proprietary Data Plume” was the greatest theme that we encountered in this series of interviews. ●  The idea is that a company has the most monopoly power and sustained defensibility when it collects proprietary, high- value data, and leverages it continuously. The same basic concept can be applied to countries.
  23. 23. “Proprietary Data Plume” ●  In order to create a “Proprietary Data Plume”, companies (or nations) must involve the following three traits: ●  High value: Data must have direct relevance for the objectives that the organization is aiming for. ●  Perpetual, building with momentum: Data continuously is collected, and encourages more and more collection. ●  Proprietary: Data collected is unique and not accessible by other parties.
  24. 24. “Proprietary Data Plume” ●  No other companies have access to Google’s search data, or Amazon’s purchase data. ●  Outside of China, Google is untouchable for when it comes to search, because Google continuously gets more searches (interaction from users) than anyone else, allowing it to continue to present better and better results to users, further solidifying it’s position from any potential competitor, and gathering more user engagement (data) in the process. ●  The same could be said of Facebook and Amazon.
  25. 25. Example: India
  26. 26. Example: India ●  India is currently the back office of the world (IT services, customer services, outsourced IT). ●  IT services represents arguably the most important growth sector in the Indian economy, and it’s one of the relatively most profitable sectors as well. ●  Value proposition: Cheap, fast, less risk than hiring full-time staff or hiring domestic (US, EU) consultants / teams.
  27. 27. Example: India ●  Three possible scenarios with India IT services in the AI era: ●  1 - Get automated and lost most of the business in that sector. (Bad) ●  2 - Switch from low-level IT work to low-level data management work (labeling data, cleaning data). (Still bad) ●  3 - Encourage these companies to build robust products and BE THE ONES who automated and capture the value of these currently outsourced processes. (GOOD!)
  28. 28. Application ●  Question: ●  How might these same dynamics be managed in your country?
  29. 29. A Place for Regulation?
  30. 30. What Next?
  31. 31. That’s All, Folks I’ve included a list of some of the resources from this presentation on my final “References” slide. Feel free to send along an email with questions, or for a copy of the slides: dan@emerj.com Twitter - @danfaggella emerj.com
  32. 32. Resource List Emerj resources: ●  https://emerj.com/ethics-and-regulatory/job-security-in-the-age-of-artificial-intelligence/ ●  https://emerj.com/ai-podcast-interviews/surviving-the-machine-age-kevin-lagrandeur/ ●  https://emerj.com/ai-market-research/artificial-intelligence-risk/ ●  https://emerj.com/ai-market-research/artificial-intelligence-in-india/ ●  https://www.huffingtonpost.com/entry/investors-speak-where-ai-drives-real-value-in- business_us_58f6e1eee4b0f5cf16c7baaf ●  TEDx on automation: https://www.youtube.com/watch?v=4NEIwKooOoI Outside resources: ●  https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could- replace-humans-and-where-they-cant-yet ●  https://www.bloomberg.com/graphics/2017-job-risk/

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