-> Don't take it personally
-> Don't get emotonal
-> Count to 10
WTF?
If you’re an accomplished professional trying to find your way to the top...
This is your roadmap.
If you struggle to take critical feedback, read this:
-> Don't take it personally
-> Don't get emotonal
-> Count to 10
WTF?
If you’re an accomplished professional trying to find your way to the top...
This is your roadmap.
You're a mosaic.
Every experience, every mistake, every resounding success adds to the picture of you.
And you, being human, can grow & evolve.
The fuel for that growth?
Feedback
And the clearer you see yourself, the easier you can place each piece.
Don't wait for people to come at you w/ feedback.
Focus them where you want to grow.
-> Tell them your gap
-> Show them you're working on it
-> Solicit their advice
-> Tell them you applied it
Your desire + action = their perception of your growth.
Use every interaction to improve.
Don't ask: What could I have done better?
Do ask: Do you think ___ landed? If no, why not?
Targeted:
-> Minimizes the onus placed on the giver
-> Starts a more natural conversation
-> Preempts other feedback
-> Focuses them
Don’t force them to prove their views w/ examples.
Instead, ask for benchmarks of who does this excellently
Move from a binary good or bad to a relative scale.
Is this feedback about:
-> Moving from good to great?
-> Being wildly off the mark?
The feedback will not always make sense. It might not even be accurate.
But ask yourself:
-> What would have to be true for someone to experience me this way?
-> Have I heard something similar before?
Genuine curiosity encourages investment.
Did the feedback hurt?
Good, that means it hit close to home.
It got near a hard truth you already know but can't bring yourself to face.
You don't go to the gym and ask the trainer to go easy.
So why shy away from doing the real work here?
Good or bad. Accurate or inaccurate.
They had infinite choices of what to do with that moment, they spent it on you.
It may not always feel like a gift, but for growth it's invaluable.
So even if it takes a few deep breaths:
Give gratitude not attitude.
Don’t overreact to one criticism.
It's just a dot.
A single observation you can choose to place in your mosaic or not.
But don't ignore patterns.
When similar criticisms keep popping up from different people, chances are it's you not them.
CEO's pay six figures to executive coaches to do exactly this.
Why?
Because w/ their seat atop the org, most people will not be fully candid.
The coach?
They only stay on if the CEO gets better.
-> A number of thoughtful advisors
-> Guided by your development objectives
-> Watching with a keen, constructive eye
-> And willing to give you their unvarnished truth
How do you get that?
Before a big presentation, find your biggest critic:
"Could you help me? I'm trying to improve my delivery. Would you mind giving me feedback after on my pacing & energy?"
You've primed them to work for you vs come at you.
Your critic is now your coach.
-> Have them put it in context (1-10 scale or letter grades can help)
-> Be honest w/ yourself if you've heard it before
-> Curiously try on what they have to say
-> Remember to say Thank You
Now own it. Do the work to turn their fuel into action.
Follow me @dklineii for weekly threads on leadership lessons & mgmt tactics.
More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
