Skip to main content

2018.02.27 Meeting Notes



INCOSE Augmented Intelligence Challenge Team
2018.02.27 Meeting Notes
Attendance: Mark Petrotta, Troy Peterson, Bill Schindel, Jon Wade, Jimmy McEver, Donna Rhodes

Introductions (mp: I captured keywords from intros)

JW: Thinking machines, “centaur systems”, instrument systems->use data, devops, anthropology, failing fast->learn fast
JM: Complex Systems WG@INCOSE, agile process->feedback into next interation, operational feedback from user, optimize learning, enhance acquisition&sustainment, instrument our process, AI enabled system engineering capabilities, PAL: personal assistant for learning
??: sequence of decisions, frame questions, agility / resilience – design for, Reference: ziva bjamiled / Matt French (mp: can’t find reference)
DR: anthropology/cognitive science, humans&systems, iterative-human/model interaction,
BS: model based patterns, reference models, experimentation, reasoning,

Meet every other week, same time

Comments

Popular posts from this blog

2018.03.13 Meeting Notes

INCOSE Augmented Intelligence Challenge Team 2018.03.13 Meeting Notes Attendance: Mark Petrotta, Troy Peterson, Bill Schindel Agenda- Charter, Goals, Measures of Success Goal 1: Develop a conceptual framework for Aug Int See attached file, particularly Slide 31 Using agile SE reference architecture as example, three major system boundaries (slide 18)                System 1: System of Model: The Target System (and Components): (Definition) The logical system of interest                System 2: System of Engineering                System 3: System of Innovation (Slide 25, see red arrow) System 2 includes Learning & Knowledge Manager for Target System (and Components): Responsible for learning new things about the Target System, its Component...

2018.03.27 Meeting Notes

INCOSE Augmented Intelligence Challenge Team 2018.03.27 Meeting Notes Attendance: [X] Mark Petrotta, [X] Troy Peterson, [X]Bill Schindel, [X]Jon Wade, []Jimmy McEver, [X]Donna Rhodes Recap of last meeting Types of learning: INLINE – Real time AFTER THE FACT – what happened? Chess AI -> learns YOU -> helps you Decision -> action -> AI analyze results Person <-> AI                Helps you learn Learning & Knowledge Manager vs LC Manager(gears) Learning & Knowledge Manager LC Manager(gears) Learn new things Not learning new things Reflecting on past history Exploit what is already known Apply what is already known Applies to ISO15288 processes Applies to ISO15288 processes AI Waves: 1)       80’s AI: Knowledge capture 2)       ML/...