build knowledge like a tree
build knowledge like a tree
from ama with elon musk
sometimes, i feel that creating accelerated learning environments feels like painting a sand mandala. specifically, for synchronous sessions that are set up as intense training and practice, there is a structure that gets made over and over again, like the structure of the mandala, to drive different outcomes.
this is a quick sketch of the session structure as it connects time (imagine it like the radial nature of a clock starting at the 12 position up top) with categories:
yellow = social warm up (intro) and social debrief (outro)
blue = mindset training
green = knowledge (usually in emerging tech or science)
red = skillset (sometimes stand alone, often integrated into deliberate practice in the area of knowledge)
the outcomes are like the final mandala making the invisible structure visible through what gets done. because the quality of the session is based on the quality of participation, it feels like it gets cleared off the table (like the mandala) to start fresh until it’s run next.
moving beyond the temporary nature of mandalas for synchronous learning, i’m curious to better understand what potential structures there are
this had me thinking about a new framework for knowledge building.
a bit ago, elon musk did an ama on reddit answering the question “how do you learn so fast?”
here is what he had to say
musk: i do kinda feel like my head is full! my context switching penalty is high and my process isolation is not what it used to be.
frankly, though, I think most people can learn a lot more than they think they can. they sell themselves short without trying.
one bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, ie the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to.
and so from what musk broke down:
trunk: core principles or foundational knowledge of a subject, sometimes called mental models
big branches: key concepts or main areas derived from the foundational knowledge.
smaller branches: sub-concepts or specialized areas branching out from the key concepts
leaves: specific details or facts related to the sub-concepts
it reminds me of the book of trees by manuel lima where he’s shows the thousands of years humans have been using the figurative and structural analogy of a tree to represent different knowledge areas: legal codes, memorization tactics for beliefs, representing family lineages, as well as an organizing structure for large data sets or company charts.
trees are good for hierarchy, immutability, and organization. they are highly expressive and also have deeper structural meaning.
leonardo da vinci mapped out a mathematical relationship to a tree branch and its trunk:
“every year when the boughs of a tree have made an end of maturing their growth, they will have made, when put together, a thickness equal to that of the main stem.”
christoph eloye, a visiting physicist to ucsd, created a model using fluid dynamics to show that it’s not the flow of water to determine structure but competition for light paired with resistance to wind.
while these were interesting historical representations, they failed to hit at the cognitive nature of what musk was outlining. so i sketched some trees to explore.
caveat: sketched in 1min to get quantity over quality. will explore more if and as it makes sense to.
tree of individual personal development (character)
trunk:
mindsets - attitude towards life as well as cultivated values through behavior
experiences - a collection of moments
identity - the role of self in relationship to others
big branches:
mindsets
curiosity (will double click on this below)
bias towards action
antifragility
do things that make sense
experiences » i don’t know this category yet. wonder if there are core experiences like things that define personal networks (schools, city move, relationships, family)
identity
relationship to your self
relationship to your role at work
relationship to your friends
relationship to your family
smaller branches (double clicking on curiosity)
mindset
curiosity
how to cultivate curiosity?
what stifles curiosity?
leaves: specific details or facts related to the sub-concepts
mindset
curiosity
how to cultivate curiosity?
ask questions to yourself
ask questions to others
seek knowledge for the sake of it
understand the nature of your own curiosity
what stifles curiosity?
prescriptive outcomes
pedagogical nature of top down learning
tree of writing online (skillset)
trunk:
learning new things
communicating value to readers
seeking understanding
big branches:
learning new things
chose topics
fundamental core concepts
explore different perspectives
communicating value to reader
it’s engaging
clear
skimmable
seek understanding
why does this matter for you?
why does this matter to someone else?
connect the dots
smaller branches:
learning new things
chose topics
fundamental core concepts
explore different perspectives
learn from startup founders (bold bets)
tree of knowledge
the best way to understand how knowledge fractures out like a tree, it’s easier to look at an example. check out this intro to quantum computing by rachel lee (tks alum).
and here is the structure of the blog post as a knowledge based tree.
note, while this was an explore type learn article where rachel mapped the territory, each one of the sub concepts could be broken down even further for both truthfulness in fact and rigor in thought.
it’s been fun to sketch quickly to visual different ideas and connect dots for structural knowledge. this is not by any means done but instead a work in progress as i explore what structural principles in learning and finding new ideas for the next accelerator program.