In 2011, I played in a private pickup basketball run that was organized by an NBA trainer.
We played every day for a week. There were about 20 players involved, roughly 15 of them active NBA players, and the rest of us overseas guys.
I felt I’d done OK for the first few days. On Wednesday of that week, I sent a text to the trainer who was running the show and asked him for his assessment of my performance thus far in the week.
His feedback wasn’t great.
In summary, he told me that I wasn’t doing much of note, and that I needed to do a better job of making an impact on both ends of the floor.
In other words: You ain’t doing shit.
Last week I spoke to a business coach, someone who I was considering hiring.
He looked at my materials, asked me some questions and offered some feedback. He pointed out many “areas for improvement” that I agreed would need to be addressed, whether with this coach, someone else, or on my own.
You’ve probably heard of “machine learning.” How it works is really simple.
A computer has a desired outcome, along with a set of instructions that will, maybe, get it to that outcome.
The computer takes action number one, then asks a question: did action number one get me closer to the desired outcome?
Yes or no.
The computer then takes action number two, then asks the same question: did action number two get me closer to the desired outcome?
Yes or no.
And it keeps going through all the actions. There can be thousands of actions. There are computer chips that can go through thousands of actions and Q&As in less time than it takes you to read this article.
Every yes or no answer is the computer (or, the program being executed by the computer, to be exact) “learning” what works and what doesn’t.
You know when you log into a website and that “captcha” comes up with a grid of photos and you’re told, “check every photo that contains cars”?
Every time you pass that captcha, a computer program is “learning” what cars look like. Multiply that by millions of people logging onto stuff all day, every day.
This isn’t about computer learning. It’s about feedback.
You see, that Q&A program could receive 30 “no” answers in a row — 30 straight actions that did NOT help it get closer to its goal.
30 times in a row, a coach or trainer telling it that its actions are NOT what we want.
And how does the program respond?
The same way every time: it notes what worked and what didn’t, adds that feedback into its program, and proceeds onto the next action.
No emotion. No reaction. No bitching.And how does the program respond? The same way every time: it notes what worked and what didn’t, adds that feedback into its program, and proceeds onto the next action. No emotion. No reaction. No bitching. Click To Tweet
Yes, I am comparing how a computer responds to feedback to how you respond to feedback.
But I’m not suggesting that you turn into a computer. You’re human. You have emotions. You react. Sometimes, you bitch.
What I’m suggesting, instead of becoming a computer, is that you discipline yourself to take constructive (read: “this will help you if you listen”) feedback the way a computer program does: dispassionately.What I’m suggesting, instead of becoming a computer, is that you discipline yourself to take constructive (read: “this will help you if you listen”) feedback the way a computer program does: dispassionately. Click To Tweet
Accept the feedback. Add it to your knowledge base. And don’t make the same mistake again, since you were already told that such-and-such action doesn’t work.
It’s just feedback. The better you listen, and the faster you implement, the quicker you get the result.
Find someone whose feedback you trust, and accept it.
Where in your life could you benefit from having fewer emotional reactions to feedback? Reply and let me know — I read all responses.
Make sure to take the following MasterClasses related to this very topic —
#1031: The Value Of Not-Friends In Your Life
#468: The Graveyard: The Most Valuable Real Estate On Earth
#229: A Real Decision Does Not Have A Plan B
#410: Stories We Tell Ourselves Vs. The Truth: Let’s Get Clear
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