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Artificial Intelligence for Start-Up Funding Success

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Presented at TechTonic Tuesday hosted by NCTechConnection.
Dr. Kehler is Chief Scientist, Co-Founder and Board Member at CrowdSmart; a technology-based investment company dedicated to doubling the success rate of startups. Dr. Kehler has over 30 years of experience as an entrepreneur and CEO.

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Artificial Intelligence for Start-Up Funding Success

  1. 1. CrowdSmart and the Innovation Economy Predicting Startup Success by Revolutionizing the Innovation and Investment Process Tom Kehler, Ph.D. 1
  2. 2. Key drivers of innovation - past 3 decades 1980’s -mid 90’s: Microprocessor, OS, Software Early 1990’s - mid 2000’s: Internet, Java, eCommerce Mid 2000’s - present: Mobile, Cloud, iOT
  3. 3. The evolving market for AI 1980’s- mid 90’s: AI/Expert Systems Dedicated Workstations Mid 1990’s - 2010: AI “Winter” Internet data generation 2010 - present: AI/Machine Learning/Deep Learning
  4. 4. Investment in AI growing rapidly
  5. 5. 30 billion Internet- connected objects by 2020
  6. 6. Key drivers of innovation - next 10+ years • IoT demands new chip designs optimized for power consumption, massive real time data and security • AI and Machine Learning transforms virtually every industry - and the global workforce • Intersection of biological and digital technologies drives unprecedented breakthroughs in disease prevention and treatment 7
  7. 7. People overestimate what’s possible in 2 years and underestimate what is possible in 10 years. - Seymour Papert, MIT
  8. 8. 9 The average lifespan of a Fortune 500 company is only 15 years
  9. 9. The challenge: thinking long term while acting fast
  10. 10. How can companies and countries effectively compete in the global innovation ecosystem?
  11. 11. Global innovation centers
  12. 12. What are the best regions for startup formation?
  13. 13. What elements drive a startup ecosystem?
  14. 14. Fundamentals of an innovation-driven economy Increase access to capital Improve access to talent Open up access to markets Establish pro-innovation regulatory & fiscal policies
  15. 15. How do leading companies innovate and compete in the 21st century?
  16. 16. Worry less about these competitors … Disruptive, discontinuous innovation rarely comes from your giant competitors
  17. 17. And more about death from a thousand cuts
  18. 18. Growing Corporate VC in finance, media, healthcare, advanced manufacturing and AI
  19. 19. The venture model is changing
  20. 20. Challenges of analyzing seed investments • Minimal (or no) publicly available data • Difficult to assess accuracy of founders’ assumptions about technology & market • Difficult to test customer acceptance for new products “
  21. 21. Who are better predictors? Seasoned experts or a diverse collection of experts? 23
  22. 22. Expert Failure at Predicting the Future Ken Olsen, the founder of DEC told a convention of the World Future Society, “There is no reason for any individual to have a computer in his home.” Bob Metcalfe Co-inventor of ethernet, Founder 3COM 24
  23. 23. Collective Intelligence improves decision making A diverse group of individuals can outperform any expert Better decisions combine human and machine intelligence using AI/Machine Learning The goal: remove the human bias and lack of data that leads to bad decisions
  24. 24. What is CrowdSmart? a technology driven investment company Rapid learning environment for startups Transparent investment process Provides a rapid investment evaluation platform for investors 26
  25. 25. Finding and Funding the Right Startups Top quality startups Investor Groups Universities Accelerators 27
  26. 26. How it works • 9 reason • 8 reason • 7 reason • 10 reason • 7 • 6 • 9 • 8 • 6 reason • 7 reason • 8 reason • 9 reason • 5 • 7 • 8 • 8 • 9 • 9 • 9 • 8 • 4 • 5 • 6 • 5 Evaluation team size ~30 28
  27. 27. Analyzing the top reasons Classified as largeMarketOpportunity Predictors: undeniably scalable market (.9),worldwide (.88), unique (.64) Classified as marketTimingRisk Predictors: trial (.21), immediacy (.21), early (.16) Rating =10 Rating =2 29
  28. 28. Our Technologies: Machine Learning Classifiers 30
  29. 29. Startup Feedback
  30. 30. Summary • Find the best startups • Predict their success by engaging a diverse group of individual investors and experts • Allow startups to learn from the evaluation team • Apply the latest of Artificial Intelligence, Natural Language Processing and Bayesian Learning technologies 32

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