The Gradient: Perspectives on AI

Jacob Andreas: Language, Grounding, and World Models

Oct 10, 2024

01:52:43

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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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Quick takeaways

Jacob Andreas discusses his paradigm shift from grounding fundamentalism, realizing large textual datasets can teach semantic relationships without external references.

He emphasizes language as a lens into cognitive processes, positioning NLP as crucial for understanding fundamental aspects of human intelligence and cognition.

Andreas highlights the distinction between task-oriented and language-oriented models, advocating for an approach focusing on language's potential beyond specific tasks.

He envisions future language models that adapt to individual user interactions, enhancing personalized AI performance through nuanced understanding and feedback.

Deep dives

The Shift from Grounding Fundamentalism

Jacob Andreas expresses a significant transition in his research perspective, moving away from being a 'grounding fundamentalist.' Initially, he emphasized the belief that understanding language requires explicit grounding to the external world. Over time, he realized that with the advent of large-scale text corpora, models could learn important semantic relationships without direct external grounding. This shift highlights the potential of language models to capture complex meanings derived from vast amounts of text data, changing the way researchers should approach language understanding.

Rethink Language: From Grounding to World Models

00:32

Language Shapes Reality

00:51

Intro

5min

Exploring Language Processing and AI

25min

The Complexities of Language Learning

12min

Exploring Limitations of Word Embeddings

14min

Shifting Research Paradigms

7min

Exploring Language Models as Agents

12min

Understanding World Models in AI

28min

Exploring Model Editing and Task Vector Dynamics

2min

Navigating AI: Academia Meets Industry

7min

Episode 140

I spoke with Professor Jacob Andreas about:

* Language and the world

* World models

* How he’s developed as a scientist

Enjoy!

Jacob is an associate professor at MIT in the Department of Electrical Engineering and Computer Science as well as the Computer Science and Artificial Intelligence Laboratory. His research aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has received a Sloan fellowship, an NSF CAREER award, MIT's Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.

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Outline:

* (00:00) Intro

* (00:40) Jacob’s relationship with grounding fundamentalism

* (05:21) Jacob’s reaction to LLMs

* (11:24) Grounding language — is there a philosophical problem?

* (15:54) Grounding and language modeling

* (24:00) Analogies between humans and LMs

* (30:46) Grounding language with points and paths in continuous spaces

* (32:00) Neo-Davidsonian formal semantics

* (36:27) Evolving assumptions about structure prediction