test page

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Most modern AI scales with the availability of data – to improve its performance or behavior requires more and more data taken from the world. The promise of Badger is to give scalable computational resources, as increasing numbers of experts could be added at runtime. So how do we turn that into an advantage, if data is the limit? One kind of method that does scale with compute is AI that integrates some sort of search component over a model of the world – for example, the successes of Monte-Carlo Tree Search and AlphaGo. However, in those games, a perfect model of the world can be provided. For AI that will generalize and scale to real-world, formerly unseen problems, we need to become good at learning such models.

The above videos are examples from a model learned from videos of a particle simulation. In particular, this is a model that can generate not just one future but an entire distribution of possible futures – a distribution that could be searched in parallel by multiple nodes, with their discoveries then networked together via communication in order to formulate a decision.

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Most modern AI scales with the availability of data – to improve its performance or behavior requires more and more data taken from the world. The promise of Badger is to give scalable computational resources, as increasing numbers of experts could be added at runtime. So how do we turn that into an advantage, if data is the limit? One kind of method that does scale with compute is AI that integrates some sort of search component over a model of the world – for example, the successes of Monte-Carlo Tree Search and AlphaGo. However, in those games, a perfect model of the world can be provided. For AI that will generalize and scale to real-world, formerly unseen problems, we need to become good at learning such models.

The above videos are examples from a model learned from videos of a particle simulation. In particular, this is a model that can generate not just one future but an entire distribution of possible futures – a distribution that could be searched in parallel by multiple nodes, with their discoveries then networked together via communication in order to formulate a decision.

Most modern AI scales with the availability of data – to improve its performance or behavior requires more and more data taken from the world. The promise of Badger is to give scalable computational resources, as increasing numbers of experts could be added at runtime. So how do we turn that into an advantage, if data is the limit? One kind of method that does scale with compute is AI that integrates some sort of search component over a model of the world – for example, the successes of Monte-Carlo Tree Search and AlphaGo. However, in those games, a perfect model of the world can be provided. For AI that will generalize and scale to real-world, formerly unseen problems, we need to become good at learning such models.

The above videos are examples from a model learned from videos of a particle simulation. In particular, this is a model that can generate not just one future but an entire distribution of possible futures – a distribution that could be searched in parallel by multiple nodes, with their discoveries then networked together via communication in order to formulate a decision.