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Introduction
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Observations
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Common Gen
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Summary
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Deep Learning as a Human
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New Relations
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Language Models
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Descriptive Ethics
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Dynamic Context
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Questions
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Multimodal Information
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Combining Representations
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Question
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Selfsupervised learning
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Unique computational concerns
Description:
Explore cutting-edge research on intuitive reasoning and neural generation in this seminar presented by Yejin Choi at the Massachusetts Institute of Technology. Delve into topics such as common sense reasoning, deep learning from a human perspective, and the role of language models in descriptive ethics. Examine the potential of self-supervised learning and the challenges of combining multimodal information. Gain insights into dynamic context handling and unique computational concerns in AI development. Engage with thought-provoking questions and observations on the future of neural networks and their ability to mimic human-like reasoning.

Intuitive Reasoning as Unsupervised Neural Generation

Massachusetts Institute of Technology
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