Recently finished reading @AdamMGrant so I decided to jot down these practical takeaways to improve my rethinking skills. Might be relevant to you as well! 👇
1 - Think Like A Scientist:
When forming an opinion, resist the temptation to preach, prosecute, or politick. Treat your emerging view as a hunch or a hypothesis and test it with data.
2 - Define Your Identity In Values, Not Opinions:
See yourself as someone who values curiosity, learning, mental flexibility, and searching for knowledge. Keep a list of factors that would change your mind.
3 - Seek Out Info That Goes Against Your Views:
Follow people who make you think - even if you usually disagree with what they think.
4 - Don't Confuse Confidence With Competence:
To prevent over-confidence in your knowledge, reflect on how well you can explain a given subject.
5 - Harness The Benefits Of Doubt:
Reframe any doubts as an opportunity for growth. Knowing what you don't know is often the 1st step toward developing expertise.
6 - Embrace The Joy Of Being Wrong:
Take mistakes as a sign that you've discovered something new. It helps you focus less on proving yourself - and more on improving yourself.
7 - Learn Something New From Each Person You Meet:
Ask people what they have been rethinking lately, or start a conversation about times you have changed your mind n the past year.
8 - Build A Challenge Network, Not Just A Support Network:
Identify your most thoughtful critics and invite them to question your thinking. Tell them why you respect their pushback - and where they usually add the most value.
9 - Don't Shy Away From Constructive Conflict:
Frame disagreement as a debate: people are more likely to approach it intellectually and less likely to take it personally.
10 - Practice The Art of Persuasive Listening:
Show interest in helping people crystallize their own views and uncover their own reasons for change by increasing your question-to-statement ratio.
11 - Question How Rather Than Why:
When people explain HOW they would make their views a reality, they often realize the limits of their understanding and start to temper some of their opinions.
12 - Ask How People Originally Formed An Opinion:
To help people re-evaluate, prompt them to consider how they'd believe different things if they'd been born at a different time or in a different place.
13 - Acknowledge Common Ground:
Admitting points of convergence shows that you're willing to negotiate about what's true, and it motivates the other side to consider your POV.
14 - Remember That Less Is More:
Instead of diluting your argument, lead with a few of your strongest points.
15 - Complexify Contentious Topics:
Instead of treating polarizing issues like 2 sides of a coin, look at them through the many lenses of a prism. Seeing the shades of gray can make us more open.
16 - Expand Your Emotional Range:
Mix in a broader set of emotions by showing some curiosity or admitting confusion/ambivalence.
17 - Abandon Best Practices:
If we want people to keep rethinking, we might be better off adopting process accountability and continually striving for better practices.
18 - Keep A Rethinking Scorecard:
Track how thoroughly different options are considered in the process. A bad process with a good outcome is luck. A good process with a bad outcome might be a smart experiment.
19 - Rethink Your Actions, Not Just Your Surroundings:
Joy can wax and wane, but meaning is more likely to last. Building a sense of purpose often starts with taking actions to enhance your learning or your contribution to others.
20 - Make Time To Think Again:
Schedule a weekly time for re-thinking and un-learning. Reach out to your challenge network and ask what ideas/opinions they think you should be reconsidering.
End 🙏
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During this quarantine time, I binge-watched @Stanford#CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI
Bayesian meta-learning generates hypotheses about the underlying function, samples from the data distribution, and reasons about model uncertainty. It is suitable for problems in safety-critical domains, exploration strategies for meta-RL, and active learning.
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There are various Bayesian Meta-Learners pre-neural-nets: