This paper investigates how incorporating Theory of Mind (ToM) into language acquisition models can enhance their performance. It uses an image referential game setting to test the effects of ToM on language learning, finding that models with a strong ToM component perform better and produce more fluent utterances.
The Othello-GPT papers involve training transformer models to predict next moves in the game of Othello. These models learn to internally represent the board state, even when not explicitly given. The research demonstrates that such models can achieve high accuracy in predicting legal moves and exhibit emergent world representations, which can be probed and intervened upon.
This paper examines the extent to which language models can represent geographic knowledge, such as understanding the relationship between city and country names. It finds that larger models perform better in encoding geographic information, which can be inferred from higher-order co-occurrence statistics in their training data.
This paper argues that large neural language models, despite their successes, do not truly understand language. It emphasizes the distinction between linguistic form and meaning, suggesting that meaning cannot be learned solely from form. The authors advocate for a clearer understanding of these concepts to guide future research in natural language understanding.
This paper critiques the notion that large neural language models truly 'understand' language, arguing that they primarily learn form rather than meaning. It emphasizes the need for clarity in distinguishing between form and meaning in NLP research to progress towards genuine NLU.
Jane Kelsey's book provides a detailed critique of the economic reforms implemented in New Zealand, examining their social and economic consequences. It offers a sobering perspective on the effects of neoliberal policies.
No information available about this book.
In this influential paper, Fodor and Pylyshyn challenge connectionist theorists by arguing that connectionist explanations, at best, can only inform us about details of the neural substrate but fail to explain the systematicity and complexity of adult human cognition. They contend that classical cognitive architectures, which operate on symbols, are necessary to account for the systematic nature of human thought and behavior. The paper contrasts connectionist models with classical models derived from the structure of Turing and Von Neumann machines, emphasizing the importance of symbolic representations in understanding cognition.
The symbol grounding problem explores how symbols, such as words or abstract representations, acquire meaning by being tied to the physical world. It is a central issue in understanding how mental states become meaningful and is closely related to the problem of consciousness. While there isn't a specific book titled 'The Symbol Grounding Problem', the concept is extensively discussed in academic literature.
The Tao Te Ching is a central text in Taoist philosophy and religion. It consists of 81 brief chapters or sections that discuss the nature of the Tao, which is described as the source and ideal of all existence. The text emphasizes living in harmony with nature, the importance of simplicity, humility, and the interconnectedness of all things. It critiques unnatural actions and social activism based on abstract moralism, advocating for a life of 'nonaction' (wu wei) and spontaneity. The text has been highly influential in Chinese philosophy and has been translated numerous times, making it one of the most translated texts in world literature[2][3][4].
The Little Big Things is Henry Fraser's memoir, detailing his journey after a tragic accident left him paralyzed. The book shares his insights on resilience, gratitude, and the importance of focusing on what can be done rather than what cannot. It offers a powerful message of hope and inspiration for anyone facing obstacles.
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Dr. Raphaël Millière is the 2020 Robert A. Burt Presidential Scholar in Society and Neuroscience in the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. His research draws from his expertise in philosophy and cognitive science to explore the implications of recent progress in deep learning for models of human cognition, as well as various issues in ethics and aesthetics. He is also investigating what underlies the capacity to represent oneself as oneself at a fundamental level, in humans and non-human animals; as well as the role that self-representation plays in perception, action, and memory. In a world where technology is rapidly advancing, Dr. Millière is striving to gain a better understanding of how artificial neural networks work, and to establish fair and meaningful comparisons between humans and machines in various domains in order to shed light on the implications of artificial intelligence for our lives.
https://www.raphaelmilliere.com/
https://twitter.com/raphaelmilliere
Here is a version with hesitation sounds like "um" removed if you prefer (I didn't notice them personally): https://share.descript.com/view/aGelyTl2xpN
YT: https://www.youtube.com/watch?v=fhn6ZtD6XeE
TOC:
Intro to Raphael [00:00:00]
Intro: Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) [00:01:18]
Show Kick off [00:07:10]
LLMs [00:08:37]
Semantic Competence/Understanding [00:18:28]
Forming Analogies/JPG Compression Article [00:30:17]
Compositional Generalisation [00:37:28]
Systematicity [00:47:08]
Language of Thought [00:51:28]
Bigbench (Conceptual Combinations) [00:57:37]
Symbol Grounding [01:11:13]
World Models [01:26:43]
Theory of Mind [01:30:57]
Refs (this is truncated, full list on YT video description):
Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière)
https://nautil.us/moving-beyond-mimicry-in-artificial-intelligence-238504/
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (Bender et al)
https://dl.acm.org/doi/10.1145/3442188.3445922
ChatGPT Is a Blurry JPEG of the Web (Ted Chiang)
https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web
The Debate Over Understanding in AI's Large Language Models (Melanie Mitchell)
https://arxiv.org/abs/2210.13966
Talking About Large Language Models (Murray Shanahan)
https://arxiv.org/abs/2212.03551
Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (Bender)
https://aclanthology.org/2020.acl-main.463/
The symbol grounding problem (Stevan Harnad)
https://arxiv.org/html/cs/9906002
Why the Abstraction and Reasoning Corpus is interesting and important for AI (Mitchell)
https://aiguide.substack.com/p/why-the-abstraction-and-reasoning
Linguistic relativity (Sapir–Whorf hypothesis)
https://en.wikipedia.org/wiki/Linguistic_relativity
Cooperative principle (Grice's four maxims of conversation - quantity, quality, relation, and manner)
https://en.wikipedia.org/wiki/Cooperative_principle
Machine Learning Street Talk (MLST) 
