The chances are high that they will complete the answer when you say nothing.
The chances are high that they will complete the answer when you say nothing.
offer/opportunity instead of a request.
Anyone will be ready to accept an offer/opportunity.
Don't mention anything about it. It's a good way to make sure that you really look them in the eyes.
To make a person feel very special, remember and repeat their name.
If you continue to speak, you are not speaking in your favor.
You will remember it easily and explore more in the process of teaching.
If you don't know what to talk about, or have awkward silence, just ask them questions.
Mental models are the most important ideas of each science as psychology.
• control the mind
• focus on what matters
• understand how the world works
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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)
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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@NBA @StephenKissler @yhgrad 1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice
@NBA @StephenKissler @yhgrad 2. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2
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https://t.co/9Z4oJmkcKj
@NBA @StephenKissler @yhgrad We adapted a clinical isolate of SARS-CoV-2 by serial passaging in the ... Thus, this mouse-adapted strain and associated challenge model should be ... (B) SARS-CoV-2 genomic RNA loads in mouse lung homogenates at P0 to P6.
https://t.co/I90OOCJg7o
@NBA @StephenKissler @yhgrad 1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice
1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) January 30, 2021
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@NBA @StephenKissler @yhgrad 2. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2
1. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2!
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) January 2, 2021
2 papers:
Human\u2013viral molecular mimicryhttps://t.co/irfH0Zgrve
Molecular Mimicryhttps://t.co/yLQoUtfS6s https://t.co/lsCv2iMEQz
@NBA @StephenKissler @yhgrad B.1.1.7 has an unusually large number of genetic changes, ... found to date in mouse-adapted SARS-CoV2 and is also seen in ferret infections.
https://t.co/9Z4oJmkcKj
@NBA @StephenKissler @yhgrad We adapted a clinical isolate of SARS-CoV-2 by serial passaging in the ... Thus, this mouse-adapted strain and associated challenge model should be ... (B) SARS-CoV-2 genomic RNA loads in mouse lung homogenates at P0 to P6.
https://t.co/I90OOCJg7o