Currently, we are experimenting with an experimental setup proposed in our Badger paper. One of the areas of explorations is an evaluation of suitability of various training settings: supervised learning, Deep Reinforcement Learning (RL), and evolutionary optimization.
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The idea of general AI has long captured the imagination of people across the world. But what would society and the economy look like in a world with general AI?
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This guide shows how to use Pytorch’s C++ API to use neural networks in Unity. We can use this with existing Python-based models, by freezing the execution trace into a binary file that is loaded by the library at runtime.
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At GoodAI, we’re interested in multi-agent architectures that can learn to rapidly adapt to new and unseen environments we expect the behavior and adaptation to be learned through communication of homogeneous units inside a single agent, allowing for better generalization.
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Recently the Organisation for Economic Co-operation and Development (OECD) organized the first meeting of OECD Network of Experts on AI (ONE AI) in Paris, where Marek Havrda, GoodAI’s AI Policy and Social Impact Director, presented a plan for practical AI.
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The idea of the Badger architecture is to make a learning agent with increased generality by virtue of allowing task-specific learning to occur in the activations of an extensible pool of ‘experts’ who all share the same weights.
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Last week Marek Havrda of GoodAI, Wendell Wallach of Yale University, together with the City of Prague organized a preparatory workshop in Brussels for the 1st International Congress for the Governance of AI.
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GoodAI was proud to support the Animal-AI Olympics which was organized by the Leverhulme Centre for the Future of Intelligence, Imperial College London, and the University of Cambridge. The project aimed to benchmark AI agents against multiple animal species.
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