If our idea of love and unity is dependent upon my silence and my oppression, then our unity is built to protect your power not to make us more equal. And that’s not love, that’s hate.

Love is not code-word for minimal progress and unity is not performative gestures of charity. If love does not embrace my full humanity and unity not committed to my full equality, I don’t want it. It will make the powerful feel good but it will not change the world we live in.
Love means dismantling every harmful ideology, policy, practice, and belief that devalues, de-centers, disrespects, and destroy the humanity, liberation, and peace of another. Unity means protecting another’s humanity, not profiting off of it.
Love is repairing the harm that has been done to another and creating an environment where those who are harmed live in a place they feel seen, protected, and loved. Unity means committing to a collective story-telling that is honest and helpful in leading us to a just future.
Love is not denial, protection, evasion, or justification of toxic ideologies and unloving practices. Love means liberating us to listen, learn, and live in ways that tell our neighbors that we see them, love them, and stand with them. Unity comes after this, not before.
So, let’s do a better job at talking about and practicing love and unity. Love and unity can’t just be good ideas. They must become the hard work we do to win the world of love, justice, and equality that we all desire for ourselves. It is doing it everyday, again and again.

More from Danté Stewart (Stew)

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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The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.


Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)


There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.


At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?