My parents used to talk about the coup in Iran. How the media made fun of Mosaddegh. They thought it was a disgrace. The US history of interference in sovereign nations is long and bloody
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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)
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So the cryptocurrency industry has basically two products, one which is relatively benign and doesn't have product market fit, and one which is malignant and does. The industry has a weird superposition of understanding this fact and (strategically?) not understanding it.
The benign product is sovereign programmable money, which is historically a niche interest of folks with a relatively clustered set of beliefs about the state, the literary merit of Snow Crash, and the utility of gold to the modern economy.
This product has narrow appeal and, accordingly, is worth about as much as everything else on a 486 sitting in someone's basement is worth.
The other product is investment scams, which have approximately the best product market fit of anything produced by humans. In no age, in no country, in no city, at no level of sophistication do people consistently say "Actually I would prefer not to get money for nothing."
This product needs the exchanges like they need oxygen, because the value of it is directly tied to having payment rails to move real currency into the ecosystem and some jurisdictional and regulatory legerdemain to stay one step ahead of the banhammer.
If everyone was holding bitcoin on the old x86 in their parents basement, we would be finding a price bottom. The problem is the risk is all pooled at a few brokerages and a network of rotten exchanges with counter party risk that makes AIG circa 2008 look like a good credit.
— Greg Wester (@gwestr) November 25, 2018
The benign product is sovereign programmable money, which is historically a niche interest of folks with a relatively clustered set of beliefs about the state, the literary merit of Snow Crash, and the utility of gold to the modern economy.
This product has narrow appeal and, accordingly, is worth about as much as everything else on a 486 sitting in someone's basement is worth.
The other product is investment scams, which have approximately the best product market fit of anything produced by humans. In no age, in no country, in no city, at no level of sophistication do people consistently say "Actually I would prefer not to get money for nothing."
This product needs the exchanges like they need oxygen, because the value of it is directly tied to having payment rails to move real currency into the ecosystem and some jurisdictional and regulatory legerdemain to stay one step ahead of the banhammer.
