Do you know what exactly came out from the "Samundra Manthan", apart from Amrit and Halahal?

https://t.co/Bi207pqwYX
Comment bellow the lesson that you learned from the most important incident of the Hinduism and I may put it on thread, 'lessons we get from samundra manthan'

More from Hathyogi

#Thread on speed of light in terms of vedic measurement.

Galileo Galilee was among the first to try to measure the speed of light in the early 17th century. Today we know that the exact speed of light is defined as 29,97,92,458 metres per second (approximately 186000 mi/s).


But, when it comes to Indian prospect, there are many references about the speed of light much before the 17th century.
The speed of light is not directly from the Rigveda Samhita but It is given by a great Indian scholor Sāyaṇācārya, popularly known as Sayana.


Sayana was a sanskrit scholar under king Bukka Raya I and Harihara II of Vijaynagar empire in 14th century. His commentary on vedas are very famous and was translated from Sanskrit to English by Max Müller, himself.


RigVeda verse 1.50.4-
तरणिविश्वदर्शवो ज्योतिष्कृदसि सूर्य । विश्वमा भासि रोचनम् ।।
Swift and all beautiful art thou, O Surya, maker of the light, Illuming all the radiant realm.

As it talks about maker of light. Commentary of Sayana in this verse is as:


तथा च स्मर्यत योजनानां सहस्रम् द्वे द्वे शते द्वे च योजने । एकेन निमिषार्धेन क्रममाण नमोऽस्तु ते ॥
It is remembered, [O Sun] bow to you, you who traverse 2,202 yojanas in half a nimesha.
Here it is talking about speed of Sun.

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)

You May Also Like