Swami Vivekandanda on Adi Shankaryacharya

"The greatest teacher of the Vedanta philosophy was Shankaracharya. By solid reasoning he extracted from the Vedas the truths of Vedanta, and on them built up the wonderful system of Jnana that is taught in his commentaries" 1/6

Swami Vivekananda considered Adi Shankaracharya (Adi Shankara) as the greatest teacher of Vedanta philosophy.

Vivekananda was an admirer of Adi Shankara and translated Adi Shankara’s poem Atma Shatakam (also known as Nirvana Shatakam).
Shankara came, and showed that the real essence of Buddhism and that of the Vedanta are not very different, but that the disciples did not understand the Master and have degraded themselves, denied the existence of the soul and of God, and have become atheists.
Vivekananda said "Shankaracharya had caught the rhythm of the Vedas, the national cadence. Indeed I always imagine that he had some vision such as mine when he was young, and recovered the ancient music that way..."
Shankara taught that 3 things were the great gifts of God: Human Body, Thirst after God & a Teacher who can show up the light. When these three great gifts are ours, we may know that our redemption is at hand. Only knowledge can free and save us but with knowledge must go virtue.
The above are Swami Vivekananda’s precious words on Adi Shankaracharya.

There is much more things to sat about influence of Shakarcharya's work on Swami Vivekananda but the topmost is Advaita vedanta.

Both has devoted their life to bring the vedanta to its pristine purity.

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)

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This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/


A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/

https://t.co/uAORIJ5BYX 4/

https://t.co/yOGZ0pLfHG 5/