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Protocol One H4D 2020 Lessons Learned

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business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, NSIN, I-Corps, JTAC

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Protocol One H4D 2020 Lessons Learned

  1. Targeting analysts need to develop effective tools to aid in the identification of protected sites in order to preserve them during military operations. Sponsor Problem Statement : Pilots miss key ground-level communications from JTAC’s in time-sensitive situations, increasing the risk of civilian casualties (CIVCAS). Current Problem Statement : 100 Interviews to date Michael Cooper BS CS, MS CS Jason Ginsberg BS EE, MS CS Xinlan Emily Hu BS CS, MS SymSys Matthew Trost BS CS, EE Minor Sponsor:
  2. Article 54(2) of the 1977 Additional Protocol I provides: It is prohibited to attack, destroy, remove, or render useless objects indispensable to the survival of the civilian population... Overarching Motivation: Protect Civilian Lives
  3. Chinese Embassy Belgrade, Serbia, 1999 MSF Hospital Kunduz, Afghanistan, 2015
  4. We interviewed 100 people: Combat Forces Fighter Pilots JTACs Targeteers Ground Commanders Special Forces AI Engineers Apple, Facebook, Microsoft, Google NGOs Intelligence Community Fmr. Directors: NSA, CIA, NRO Analysts: NGA, DIA Combatant Commands AFRICOM, INDOPACOM, STRATCOM, CENTCOM, SOCOM, EUCOM Deployment Prime Defense Contractors Air Force Research Laboratory
  5. Our Problem Evolution
  6. Starting with our Sponsor’s Problem WEEKS 1-2 Geospatially defining No Strike Facilities is time consuming for analysts. No Strike Boundary
  7. Saving Analysts’ Time with AI Entries on No-Strike Lists lack “geospatial boundaries”. ● Boundaries are useful for targeting and weaponeering. ● Analysts draw by hand -- incredibly slow. An AI solution to this problem would save years of analyst time. - Combatant Command Chief of Targets
  8. Taking a step back: learning about targeting process. Planning Stage Commander’s Intent Courses of Action Weaponeering Battle Damage Assessment Air Tasking Cycle Patrol ID Intelligence Intelligence Intelligence 3 Days Targeteering
  9. Drawing boundaries is just one small piece. Planning Stage Commander’s Intent Courses of Action Weaponeering Battle Damage Assessment Air Tasking Cycle Patrol ID Intelligence Intelligence Intelligence 3 Days Targeteering
  10. (1) Intelligence Supports Decision Making Planning Stage Commander’s Intent Courses of Action Weaponeering Battle Damage Assessment Air Tasking Cycle Patrol ID Intelligence Intelligence 3 Days Targeteering Intelligence
  11. NGO Self- Reports MIDB Contains info on targets & NSFs Used by targeteers SIGINT HUMINT GEOINT MASINT Targeteers get Intel from the Modernized Integrated DB
  12. Patrol ID (2) Dynamic vs Pre-planned Strikes Intelligence Planning Stage Commander’s Intent Courses of Action Weaponeering Battle Damage Assessment Air Tasking Cycle Intelligence Intelligence 3 Days Targeteerin g
  13. Shifting Focus to Dynamic Strikes WEEKS 2-5 Dynamic strikes have higher risk of mischaracterizing targets.
  14. Risk of Target Mischaracterization in Dynamic Strikes Apartment Building Mosque Enemy HQ
  15. Risk of Target Mischaracterization in Dynamic Strikes Apartment Building Mosque Enemy HQ Planting IED? Irrigation
  16. If you want to protect humans, you have to understand the local civilian patterns of life. - UN Civilian Protection Specialist
  17. On-the-Ground Sources Next MVP: Local Civilian Patterns-of-Life
  18. On-the-Ground Sources Local Life Database Next MVP: Local Civilian Patterns-of-Life
  19. On-the-Ground Sources Local Life Database Next MVP: Local Civilian Patterns-of-Life Analysts
  20. On-the-Ground Sources Local Life Database Next MVP: Local Civilian Patterns-of-Life Analysts On-Hand Access Commanders Ground Troops + Pilots Targeteers
  21. Reactions were lukewarm. “Unless you could look inside walls…” “How do you trust that the info isn’t being manipulated?” “That’s interesting but not compelling.”
  22. Getting the Data is Hard In my year of running targeting in Iraq, we saw maybe four incidents. – Lead Intel Targeteer, Iraq vs.
  23. We reached an impasse.
  24. Until one pilot presented a new problem in this space...
  25. Until one pilot presented a new problem in this space... and we pivoted.
  26. Protocol One’s Problem Evolution WEEKS 5-10 Pilot-JTAC communications demand a high cognitive load.
  27. Key Beneficiaries Pilots PAINS Radio interrupts Hand-copying GAINS Replay + Redundancy JTACs PAINS Fear accidental friendly or civilian fire GAINS Greater confidence. One-and-done Civilians PAINS Disruption, threat to life, injury. GAINS Improved economic, social welfare
  28. KEY PARTNERS ● Air Force Research Laboratory ● 13th ASOS / 92nd AGOW ● JAIC ● 422nd Test & Evaluation Squadron KEY RESOURCES ● AI expertise to re- train SOTA models on domain specific data set. ● 1,000 hrs of transcribed JTAC training data VALUE PROPOSITIONS Streamline JTAC/pilot comms. during CAS missions. Reduce pilots repeat- requests from the JTAC. Increase confidence in informational relay, correlation, and targeting: pilots, JTAC, civilians, all find this valuable. MISSION ACHIEVEMENT/IMPACT FACTORS Reducing cognitive load on Pilots, so they can better process ROE. JTACs require less time to talk pilots on to a target via radio. Yet, still retain digital efficiencies. KEY ACTIVITIES ● Implement ASR for pilot-JTAC comms ● Design simple UX with replay, aut- parsing, speaker notation, and customization. DEPLOYMENT Construct voice-to-text system running on pilot tablets. Link radio stream into tablet auxiliary input. On-device inference. BUY-IN & SUPPORT Work within key partners to integrate this product into the ATAK system. MISSION BUDGET/COST Maximize spending on data and engineers. Burn rate of $60,000/mo. BENEFICIARIES JTAC USAF Fighter Pilots Civilians KEY PARTNERS ● Air Force Research Laboratory ● 13th ASOS / 92nd AGOW ● JAIC ● 422nd Test & Evaluation Squadron
  29. Finding Advocates, Building Prototype over Summer Seeking Funding Acquiring Data Building Partnerships
  30. Looking Back Where we started: helping analysts save time Where we are now: helping pilots save civilian lives
  31. Looking Back Where we started: helping analysts save time Where we are now: helping pilots save civilian lives Help super multi-taskers
  32. Looking Back Where we started: helping analysts save time Where we are now: helping pilots save civilian lives Help super multi-taskers focus on the tasks that matter the most.
  33. Huge Thank You to: Our Mentors: Rafi Holtzman, Jennifer Smith-Heys, Matthew ‘Fuego’ Tuzel Our Sponsors: Joe Murray, Matt Moore The Hacking for Defense Course Staff: Nate Simon, Sam Lisbonne, Valeria Rincon, Steve Blank, Steve Weinstein, Joe Felter, Pete Newell, Tom Bedecarré, Jeff Decker And Finally: Our classmates, and especially all of our interviewees.
  34. team_protocol_one@lists.stanford.edu

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