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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)
