This book “helps bridge the gap between simply memorizing or blindly accepting information, and the greater challenge of critical analysis and synthesis,” according to Amazon.
Skip the MBA, follow Elon Musk's idea and read these 10 books instead.
A thread..
This book “helps bridge the gap between simply memorizing or blindly accepting information, and the greater challenge of critical analysis and synthesis,” according to Amazon.
A classic textbook on organizational culture and leadership.
This textbook is now in its 14th edition, so it must be pretty useful.
Originally published in 1961, this is “one of the most influential books about business organizations ever,” Amazon claims.
Save yourself even more by checking the older editions of this one — the price drops from $75 to $3.
Heard of Maslow’s Hierarchy of Needs? This is where the idea comes from.
This doesn’t look like a light read at all but it tackles an interesting theme: new, critical takes on the subject of organizations and how people behave within them
A discussion of navigating those moments when your business and values are in conflict.
“The only comprehensive, up-to-date guide to today’s revolutionary management support system technologies,” says Amazon
Another classic textbook, now in its 11th edition.
Next issue (Tuesday) is gonna be lit 🔥
https://t.co/p52Tj7sNEm
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)