Grounding through pure language modeling objectives, the origins or probing, the nature of understanding, the future of system assessment, signs of meaningful progress in the field, and having faith in yourself.

Transcript: https://web.stanford.edu/class/cs224u/podcast/pavlick/

• Ellie's website
• The LUNAR Lab
• MIT Scientist Captures 90,000 Hours of Video of His Son’s First Words, Graphs It
• Michael Frank
• Spot robots
• Dylan Ebert
• Ian Tenney
• What do you learn from context? Probing for sentence structure in contextualized word representations
• BERT Rediscovers the Classical NLP Pipeline
• JSALT: General-Purpose Sentence Representation Learning
• Sam Bowman
• Skip thought vectors
• What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
• Hex
• Charlie Lovering
• Designing and interpreting probes with control tasks
• Jerry Fodor
• Been Kim
• Mycal Tucker
• What if this modified that? Syntactic interventions via counterfactual embeddings
• Yonatan Belinkov
• HANS: Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
• Conceptual pacts and lexical choice in conversation
• Locating and editing factual knowledge in GPT
• Could a purely self-supervised language model achieve grounded language understanding?
• Dartmouth Summer Research Project on Artificial Intelligence (1956)
• Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain