Intuitive Reasoning as Unsupervised Neural Generation

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.

Introduction
Observations
Common Gen
Summary
Deep Learning as a Human
New Relations
Language Models
Descriptive Ethics
Dynamic Context
Questions
Multimodal Information
Combining Representations
Question
Selfsupervised learning
Unique computational concerns