4. With this monopoly power you decide to start censoring speech and filtering search to only give the "proper" response to searches because you are a leftist.
1. Here is a quick example on why censorship doesn't work. Say you created a company around the fact that you had the best search engine. One if used, people could immediately find what they needed.
2. It worked so well everyone flocked to your product.
4. With this monopoly power you decide to start censoring speech and filtering search to only give the "proper" response to searches because you are a leftist.
6. Users are now taking more and more time to find what they really want, if they can find it at all.
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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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Trump is gonna let the Mueller investigation end all on it's own. It's obvious. All the hysteria of the past 2 weeks about his supposed impending firing of Mueller was a distraction. He was never going to fire Mueller and he's not going to
Mueller's officially end his investigation all on his own and he's gonna say he found no evidence of Trump campaign/Russian collusion during the 2016 election.
Democrats & DNC Media are going to LITERALLY have nothing coherent to say in response to that.
Mueller's team was 100% partisan.
That's why it's brilliant. NOBODY will be able to claim this team of partisan Democrats didn't go the EXTRA 20 MILES looking for ANY evidence they could find of Trump campaign/Russian collusion during the 2016 election
They looked high.
They looked low.
They looked underneath every rock, behind every tree, into every bush.
And they found...NOTHING.
Those saying Mueller will file obstruction charges against Trump: laughable.
What documents did Trump tell the Mueller team it couldn't have? What witnesses were withheld and never interviewed?
THERE WEREN'T ANY.
Mueller got full 100% cooperation as the record will show.
BREAKING: President Donald Trump has submitted his answers to questions from special counsel Robert Mueller
— Ryan Saavedra (@RealSaavedra) November 20, 2018
Mueller's officially end his investigation all on his own and he's gonna say he found no evidence of Trump campaign/Russian collusion during the 2016 election.
Democrats & DNC Media are going to LITERALLY have nothing coherent to say in response to that.
Mueller's team was 100% partisan.
That's why it's brilliant. NOBODY will be able to claim this team of partisan Democrats didn't go the EXTRA 20 MILES looking for ANY evidence they could find of Trump campaign/Russian collusion during the 2016 election
They looked high.
They looked low.
They looked underneath every rock, behind every tree, into every bush.
And they found...NOTHING.
Those saying Mueller will file obstruction charges against Trump: laughable.
What documents did Trump tell the Mueller team it couldn't have? What witnesses were withheld and never interviewed?
THERE WEREN'T ANY.
Mueller got full 100% cooperation as the record will show.