13. cc: Ricky Kharawala - https://unsplash.com/@rk2productions?utm_source=haikudeck&utm_medium=referral&utm_campaign=api-credit
Early days yet – build learning in
14. Summary
• Align the incentives, especially with culture.
• Culture is people – mix them up.
• Build capabilities with every effort, but not for
their own sake.
• Prep the data before you need it.
• Don’t just work with startups, act like a VC
• Institutionalize learning & collaboration, hire
generalists.
• Read “Lean Enterprise”.
Brief intro of IAG and Firemark, including name origin.
http://firemarklabs.com.au/
Lincoln’s Gettysburg Address as example of brevity and essentialism - 2 minutes vs 2 hours! – no one remembers Senator Edward Everett
Understand the essentials, look up the details
Dan Pink’s ”Pinkcast” about one percent
https://www.danpink.com/pinkcast/pinkcast-1-10-the-most-important-thing-i-learned-in-law-school/
Tough – define what is is NOT. ADMIRAL Grace Hopper – early compiler, first computer bug most dangerous words “that’s the way we do things around here”
Bottom-line: recognizing that most of the smart people are elsewhere . . .
Worth it just for the bibliography. Touches on every key concept needed for innovating at speed and scale.
Comparison to 7-Habits . . .
Anecdote about programming optimizations – graded on lines, ram or storage . . .
Each API a profit center example – cost signal. Ratio budgets.
Example of comfort noise in VoIP and slowdown for claims photos results to be seen as “thoughtful”.
But in innovation context, CULTURE is a HUMAN construct. Change management, incentives, sure, but rotate people! Manager reverts to gold-plating as engineer. Westrum in DevOps.
Highest Paid Person’s Opinion
Some disciplines, like Agile/SCRUM, must be adopted at least nearly whole in order to realize benefits.
Don’t just work with startups, manage your innovation portfolio like a VC/PE/Hedge Fund. Learning is the coin of the realm.
Hinton sees the gulf being bridged by computer programs built using neural nets. He acknowledges the limitations of the technology to date when he states, “At present, it’s hard to train neural networks with more than about a billion weights. That’s about the same number of adaptive weights as a cubic millimeter of mouse cortex.”
(https://cesran.org/the-current-state-of-ai-as-we-begin-2018.html)
Brain is about 100 trillion connections, 100 billion neurons.
