I asked my audience:

“What productivity tip do you find genuinely works?”

Here are 10 of the most popular replies:

1 - Three key tasks a day

Whatever happens you get 3 core things completed each day.
2 - Productive time scheduling

Find your most productive time in the day (eg early morning, late at night etc) and plan accordingly.
3 - Say no

Doing productive work means focusing on a few things that matter.
4 - The 2-minute rule

If it takes less than 2 minutes, do it now.
5 - Time block

If it’s not on your calendar, it won't happen.
6 - Take breaks

Sometimes the most productive thing you can do is to do nothing at all.
7 - Remove distractions

It's impossible to focus when your phone / friends / notifications are diverting your attention away from the things you need to do.
8 - Break it down

Turn big tasks into smaller and more manageable tasks.
9 - Prioritise

Start with the biggest task or the most important task first.
10 - Sleep

You won't work well unless you're well rested.
What else would you add?

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)

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