89% of the real results are in (not exit polls) -

55.7% Bolsonaro
44.3% Haddad

Jair Bolsonaro will almost certainly be the next President of Brazil. This is a sad day for fans of democracy and human rights all around the world.

You can check that page for final confirmation. But now, the question is a different one - going forward, how much will Congress, the courts, and Brazilian civil society in general limit the aspirations of one of the world's most extreme elected figures? https://t.co/waXhiAVS2q
I have called Bolsonaro a "would-be dictator" and said as a candidate, he is more extreme than Rodrigo Duterte. But the shape of his Presidency is far from determined. It will depend on how much Brazil's institutions, and Brazil's citizens, push back against his worst impulses.
International opinion, and international pressure, will matter. Brazilians have noticed that the world began to fear Bolsonaro. And I beg of you, please, do not call him "The Brazilian Trump." It's just incorrect, and it also invites a third of Americans to sign off on fascism.

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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