First in our unplugged series live from #NeurIPS2022
We discuss natural language understanding, symbol meaning and grounding and Chomsky with Dr. Andrew Lampinen from DeepMind.
We recorded a LOT of material from NeurIPS, keep an eye out for the uploads.
YT version: https://youtu.be/46A-BcBbMnA
References
[Paul Cisek] Beyond the computer metaphor: Behaviour as interaction
https://philpapers.org/rec/CISBTC
Linguistic Competence (Chomsky reference)
https://en.wikipedia.org/wiki/Linguistic_competence
[Andrew Lampinen] Can language models handle recursively nested grammatical structures? A case study on comparing models and humans
https://arxiv.org/abs/2210.15303
[Fodor et al] Connectionism and Cognitive Architecture: A Critical Analysis
[Melanie Mitchell et al] The Debate Over Understanding in AI's Large Language Models
https://arxiv.org/abs/2210.13966
[Gary Marcus] GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about
[Bender et al] On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
https://dl.acm.org/doi/10.1145/3442188.3445922
[Adam Santoro, Andrew Lampinen et al] Symbolic Behaviour in Artificial Intelligence
https://arxiv.org/abs/2102.03406
[Ishita Dasgupta, Lampinen et al] Language models show human-like content effects on reasoning
https://arxiv.org/abs/2207.07051
REACT - Synergizing Reasoning and Acting in Language Models
https://arxiv.org/pdf/2210.03629.pdf
https://ai.googleblog.com/2022/11/react-synergizing-reasoning-and-acting.html
[Fabian Paischer] HELM - History Compression via Language Models in Reinforcement Learning
https://ml-jku.github.io/blog/2022/helm/
https://arxiv.org/abs/2205.12258
[Laura Ruis] Large language models are not zero-shot communicators
https://arxiv.org/pdf/2210.14986.pdf
[Kumar] Using natural language and program abstractions to instill human inductive biases in machines
https://arxiv.org/pdf/2205.11558.pdf
Juho Kim