As someone who covered climate change a decade ago, I feel like the rest of the world is only just catching up to the following realities:

1. Avoiding truly epic amounts of climate change is technically feasible but economically and politically totally infeasible

2. Accepting that we are going to return our planet's climate to a state not seen in tens of millions of years is important. Because it's the truth. Anyone who wants to tell you differently is trying to sell you their ideology.
3. Knowing the earth is going to undergo multi-degree warming is no excuse not to decarbonize our energy system. We needed to do that anyway. And the less warming, the better.
4. Talking about really serious amounts of adaptation is not accepting defeat, it's grappling with reality. And you'd be surprised how having to actually spend money to deal with the coming warming will help people also accept (3)
5. There are no easy solutions. Geoengineering is extremely problematic. Carbon draw down of any scale is still a fantasy. We are going to have to both adapt and mitigate. Getting serious about it will probably take generational turnover in the power structures of this country.
coda: The insurance industry already understands the dynamics of climate change.

Federal flood insurance and FEMA excepted of course, since that's backed by a government that seems content to subsidize rebuilding in the path of danger.

https://t.co/pIQUQmCt3p

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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इमारत, पर्वत या अट्टालिकाओं पर ज़रूर देखा होगा। देखा है न?

ज़रा सोचिये पीपल या बरगद की बीज कैसे पहुंचे होंगे वहाँ तक? इनके बीज इतने हल्के भी नहीं होते के हवा उन्हें उड़ाके ले जा सके।