Notebookin' w/ Mindy Podcast

Mindy · 08-13-2025, 11:38 AM

https://www.youtube.com/watch?v=jO77jdoxrs4

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Mindfulness
Aug 13, 2025

Notebookin' w/ Mindy

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[00:41] The Quantum Revolution: What is QIS?

[01:36] Meet the Key Player: NIST

[02:44] Power in Partnership: A Quantum Ecosystem

[04:24] Lab to Real Life: Tangible Impact

[05:41] Securing Our Future: The Encryption Challenge

https://www.youtube.com/watch?v=9xBTy-UvJsc

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Mindfulness
Aug 13, 2025

Notebookin' w/ Mindy

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The video discusses:

Understanding Your Autopilot [00:09]: Explains that a "hidden automatic you" is influenced by ingrained habits and beliefs.

Your Brain's Default Setting [00:58]: Introduces the "normalcy bias," where the brain assumes everything is fine, and the "perception box," which consists of deep-rooted beliefs that can limit potential.

The Power of Self-Awareness [02:39]: Highlights self-awareness as the first step to change, and the foundation of Emotional Intelligence (EI). It notes that while IQ might get you a job, EI helps you get promoted.

Your Toolkit for Change [04:17]: Outlines four domains of EI (Self-Awareness, Self-Management, Social Awareness, and Relationship Management) and a "Motivation Engine" that starts with Curiosity.

Unlocking Peak Performance [06:27]: Shares examples of how the normalcy bias impacted a 9/11 evacuation and how a survivor of the Tenerife crash escaped by having a rehearsed mental model. It also describes the "Flow State" as a state of "effortless effort" where performance is amplified.

https://www.youtube.com/watch?v=5nZvOIprzIU

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Mindfulness
Aug 13, 2025

Notebookin' w/ Mindy

Quote:

This video is about the paper "Attention Is All You Need" and its impact on AI. Here are some key timestamps from the video:

[00:00:15] - The video introduces the paper "Attention Is All You Need."

[00:00:23] - It explains how AI used to understand sentences one word at a time.

[00:00:58] - The video discusses the problem with this sequential processing.

[00:01:06] - Recurrent Neural Networks (RNNs) are introduced as the standard for language tasks at the time.

[00:02:13] - The new idea of a model seeing the entire sentence at once is proposed.

[00:02:29] - The mechanism of self-attention is explained.

[00:03:04] - The Transformer architecture, built entirely on attention mechanisms, is introduced.

[00:04:09] - The concept of positional encoding is described.

[00:05:02] - The results of the Transformer's performance are discussed.

[00:05:36] - The video explains why attention works so well.

[00:06:23] - The impact of the Transformer architecture on large language models like GPT is mentioned.

[00:06:40] - The video concludes with future implications of attention.