What: ‘Distributed AI Systems for Optimizing Human and Machine Intelligence’
When: Wednesday, 8/17/16 at 11am CDT for live version, IASA Full Members can access recorded version
Where: IASA Global eSummit on Machine Learning (free with registration http://www.iasaglobal.org/august-esummit/ )
I would like to thank Doug McDavid for recommending me as a presenter to IASA Global and for the assistance of Daryl Carr, Patrick Morrison and rest of the team at IASA Global.
Our story dates back to the 1980s when my late business partner Russell Borland and I met and became friends. In those days I was in many ways a business architect and Russell was an IT architect. The Kyield journey began a decade later over the course of a very intense time in our live lean lab we built from scratch in the mid 1990s, during which time my then new theory ‘yield management of knowledge’ took shape.
The term we used in the 1990s to describe our focus was a knowledge systems lab and incubator—representing much of the non-robotic work in AI at the time. Looking back the ice age of AI peaked a few years earlier around 1990 and was just beginning to thaw by the mid 1990s. Among the better-known KS labs was Stanford’s KSL, which is where I ran into Deborah McGuinness who later served on our advisory board.
Fast-forward nearly two decades to the point where technical viability was finally demonstrated in distributed fashion over high volume networks about three years ago. We’ve been engaging with senior management teams in the world’s leading organizations ever since. With recent improvements in hardware, software and machine learning the theorem can be applied as originally intended.
This will be the first public talk on our work at Kyield, which spans two decades of R&D. With that brief additional intro aside, these are the top 10 priorities I will cover in my talk:
- List definitions for the talk on AI, distributed AI, AI augmentation, and beneficial AI. Nothing particularly new but provides parameters.
- Discuss the governing rule (s) of law (s) that influence or determine what is possible, decision making, and why.
- Provide examples with brief discussions of different types of AI systems.
- Discuss key issues we’ve confronted in AI systems, including machine learning.
- Talk a bit about our work at Kyield at the confluence of humans and machines in distributed organizations.
- Provide a graphic with discussion on governance structure, the eight functional areas we focus on, and relationship management.
- Display an architecture we use to show yield management of knowledge applied to a CALO (continuously adaptive learning organization—full higher quality version of above). I’ll spend some time here discussing universal issues.
- Provide a dashboard example of how a CALO manifests in conjunction with our Kyield OS (integrated with several SW programs).
- Discuss a few use case scenarios I’ve explored with dozens of senior management teams.
- Explore specific issues to chief architects we’ve encountered and observed, strategic issues for organizations, and suggest some options to consider.
I will then attempt a rational conclusion, invite your questions, and attempt to answer in a coherent manner given time constraints.
I hope you can make it the live event on Wednesday or view the recorded version in the near future.
Founder and CEO