🌺Hinduism regards all souls as equal & all beings as the manifestation of Supreme Brahman & that's why we see God in everyone of his creation.🌺
Presence of soul in animal bodies may pose some problems to them with regard to their chances of liberation, but it does not..
Although animals enjoy the same spiritual status as humans, they are not well qualified to achieve liberation, since they do not..
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APIs in general are so powerful.
Best 5 public APIs you can use to build your next project:
1. Number Verification API
A RESTful JSON API for national and international phone number validation.
🔗 https://t.co/fzBmCMFdIj
2. OpenAI API
ChatGPT is an outstanding tool. Build your own API applications with OpenAI API.
🔗 https://t.co/TVnTciMpML
3. Currency Data API
Currency Data API provides a simple REST API with real-time and historical exchange rates for 168 world currencies
🔗 https://t.co/TRj35IUUec
4. Weather API
Real-Time & historical world weather data API.
Retrieve instant, accurate weather information for
any location in the world in lightweight JSON format.
🔗 https://t.co/DCY8kXqVIK
Best 5 public APIs you can use to build your next project:
1. Number Verification API
A RESTful JSON API for national and international phone number validation.
🔗 https://t.co/fzBmCMFdIj
2. OpenAI API
ChatGPT is an outstanding tool. Build your own API applications with OpenAI API.
🔗 https://t.co/TVnTciMpML
3. Currency Data API
Currency Data API provides a simple REST API with real-time and historical exchange rates for 168 world currencies
🔗 https://t.co/TRj35IUUec
4. Weather API
Real-Time & historical world weather data API.
Retrieve instant, accurate weather information for
any location in the world in lightweight JSON format.
🔗 https://t.co/DCY8kXqVIK
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)