The Gradient: Perspectives on AI

Jacob Andreas: Language, Grounding, and World Models

17 snips
Oct 10, 2024
Jacob Andreas, an MIT associate professor specializing in language learning and intelligent systems, shares his fascinating insights. He discusses the philosophical challenges of grounding language in real-world contexts and compares human understanding to that of large language models. Jacob reflects on the evolution of language processing, the complexities of word embeddings, and how research paradigms have shifted over time. Additionally, he explores the concept of world models in AI and their critical role in decision-making, enhancing the understanding of language's connection to cognition.
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INSIGHT

Data's Hidden Knowledge

  • Internet-scale text corpora contain more information than previously assumed.
  • This data allows models to learn about the world without explicit grounding.
INSIGHT

Grounding and Meaning

  • Defining "meaning" in AI relies on existing philosophical frameworks.
  • An isomorphic model of the world is sufficient for grounding, creating an empirical question.
ANECDOTE

Structure Prediction Challenges

  • Early structure prediction work required simplifying assumptions due to its complexity.
  • Cognitive science provided support for imposed structures like event boundaries.
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