The idea that mass extinctions allow many new types of species to evolve is a central concept in evolution, but a new study using artificial intelligence to examine the fossil record finds this is rarely true.
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GoodAI are part of The VeriDream project, an international consortium of six organizations across Europe, that has been awarded €2 million by the European Innovation Council to carry out a research and innovation strategy.
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An opinion on what kind of research framework do we need, if we want the AI to perpetuate some of the values we hold dearest: curiosity, creativity, a need to be surprised, and to marvel.
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Artificial intelligence (AI) research and development company, GoodAI, has awarded a grant to Tomáš Mikolov from the Czech Institute of Informatics, Robotics, and Cybernetics CTU to study how novel behaviors arise in AI.
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AI agents often operate in partially observable environments, where only part of the environment state is visible at any given time. An agent in such an environment needs memory to compute effective actions from the history of its actions.
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Last week GoodAI organized the first Meta-Learning & Multi-Agent Learning Workshop which throughout the week saw over 60 participants from across the world take part including speakers from Google Brain, DeepMind, OpenAI, University of Oxford, Stanford University and MIT.
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One of the properties of the Badger architecture is modularity: instead of using one big neural network, the Badger should be composed of many small Experts which solve the whole task in a collaborative manner.
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GoodAI recently hosted a virtual workshop with a number of external collaborators in order to address some of the crucial open questions related to our Badger Architecture.
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To understand why Badger is hard to train, we need to understand first how Badger learns, using a toy task. We try to understand the plateaus, what happens during this period, and why the plateaus are there.
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Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations.
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