Demo: Event 3

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Demo: Event 2

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This is example of event with a registration form that sends an email confirmation and an email reminder 1 day before the event.

Demo: Event 1

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This is example of event with a registration form that sends an email confirmation and an email reminder 1 day before the event.

Building machines that think and learn like people

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways.

It’s not what you look at that matters, it’s what you see

People frequently interpret the same information differently, based on their prior beliefs and views. This may occur in everyday settings, as when two friends are watching the same movie, but also in more consequential circumstances, such as when people interpret the same news differently based on their political views. The role of subjective knowledge in altering how the brain processes narratives has been explored mainly in controlled settings.

Structure and flexibility in cortical representations of odour space

The cortex organizes sensory information to enable discrimination and generalization. As systematic representations of chemical odour space have not yet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge.