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Algorithms is a song that explores the idea that Robots can’t experience being in the moment where as humans can and that is the beauty that separates us….. for now Click to pre-save Algorithms:

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KasabianHQ

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34,962 görüntüleme • 3 yıl önce •via X (Twitter)

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It actually sounds really good

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That sound sick !!

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COME TO SPAIN !!

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Will it be a physical release?

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My full AJ Forum speech last week: the common enemy of humanity is THE SYSTEM that has enabled the genocide in Palestine, including the financial capital that funds it, the algorithms that obscure it and the weapons that enable it.

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Former Greek finance minister Yanis Varoufakis says that the conflict in Gaza is being utilised as a laboratory for technology firms, such as Palantir, to train AI and data-gathering products. Beyond the political failure of the “pseudo-ceasefire”, Varoufakis says that global tech giants are exploiting the mass displacement and movement of Palestinians to refine their algorithms. He detailed a conversation with a tech insider, formerly of Palantir, who reportedly described the “Gaza event” as “exciting” for the industry and how high-volume data generated by the bombing of densely populated areas is being harvested to develop AI products. These algorithms are then rebranded as civilian tools for managing emergency logistics and hospital systems in the West.

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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

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"The information that they aggregate and apply algorithms to are designed to divide us and separate us and trigger us," says Frank McCourt on internet safety. "We believe that another protocol called DSMP returns ownership to us as individuals."
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"The information that they aggregate and apply algorithms to are designed to divide us and separate us and trigger us," says Frank McCourt on internet safety. "We believe that another protocol called DSMP returns ownership to us as individuals."

Squawk Box

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There is so much at stake in this election, and, ultimately, the question before us is: What kind of country do we want to live in? The beauty of a democracy, as long as we can hold on to it, is that each of us has the power to answer that question.
0:58

There is so much at stake in this election, and, ultimately, the question before us is: What kind of country do we want to live in? The beauty of a democracy, as long as we can hold on to it, is that each of us has the power to answer that question.

Kamala Harris

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