Iman Mirzadeh from Apple, who recently published the GSM-Symbolic paper discusses the crucial distinction between intelligence and achievement in AI systems. He critiques current AI research methodologies, highlighting the limitations of Large Language Models (LLMs) in reasoning and knowledge representation.
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TRANSCRIPT + RESEARCH:
https://www.dropbox.com/scl/fi/mlcjl9cd5p1kem4l0vqd3/IMAN.pdf?rlkey=dqfqb74zr81a5gqr8r6c8isg3&dl=0
TOC:
1. Intelligence vs Achievement in AI Systems
[00:00:00] 1.1 Intelligence vs Achievement Metrics in AI Systems
[00:03:27] 1.2 AlphaZero and Abstract Understanding in Chess
[00:10:10] 1.3 Language Models and Distribution Learning Limitations
[00:14:47] 1.4 Research Methodology and Theoretical Frameworks
2. Intelligence Measurement and Learning
[00:24:24] 2.1 LLM Capabilities: Interpolation vs True Reasoning
[00:29:00] 2.2 Intelligence Definition and Measurement Approaches
[00:34:35] 2.3 Learning Capabilities and Agency in AI Systems
[00:39:26] 2.4 Abstract Reasoning and Symbol Understanding
3. LLM Performance and Evaluation
[00:47:15] 3.1 Scaling Laws and Fundamental Limitations
[00:54:33] 3.2 Connectionism vs Symbolism Debate in Neural Networks
[00:58:09] 3.3 GSM-Symbolic: Testing Mathematical Reasoning in LLMs
[01:08:38] 3.4 Benchmark Evaluation and Model Performance Assessment
REFS:
[00:01:00] AlphaZero chess AI system, Silver et al.
https://arxiv.org/abs/1712.01815
[00:07:10] Game Changer: AlphaZero's Groundbreaking Chess Strategies, Sadler & Regan
https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreaking-Strategies/dp/9056918184
[00:11:35] Cross-entropy loss in language modeling, Voita
http://lena-voita.github.io/nlp_course/language_modeling.html
[00:17:20] GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in LLMs, Mirzadeh et al.
https://arxiv.org/abs/2410.05229
[00:21:25] Connectionism and Cognitive Architecture: A Critical Analysis, Fodor & Pylyshyn
https://www.sciencedirect.com/science/article/pii/001002779090014B
[00:28:55] Brain-to-body mass ratio scaling laws, Sutskever
https://www.theverge.com/2024/12/13/24320811/what-ilya-sutskever-sees-openai-model-data-training
[00:29:40] On the Measure of Intelligence, Chollet
https://arxiv.org/abs/1911.01547
[00:33:30] On definition of intelligence, Gignac et al.
https://www.sciencedirect.com/science/article/pii/S0160289624000266
[00:35:30] Defining intelligence, Wang
https://cis.temple.edu/~wangp/papers.html
[00:37:40] How We Learn: Why Brains Learn Better Than Any Machine... for Now, Dehaene
https://www.amazon.com/How-We-Learn-Brains-Machine/dp/0525559884
[00:39:35] Surfaces and Essences: Analogy as the Fuel and Fire of Thinking, Hofstadter and Sander
https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475
[00:43:15] Chain-of-thought prompting, Wei et al.
https://arxiv.org/abs/2201.11903
[00:47:20] Test-time scaling laws in machine learning, Brown
https://podcasts.apple.com/mv/podcast/openais-noam-brown-ilge-akkaya-and-hunter-lightman-on/id1750736528?i=1000671532058
[00:47:50] Scaling Laws for Neural Language Models, Kaplan et al.
https://arxiv.org/abs/2001.08361
[00:55:15] Tensor product variable binding, Smolensky
https://www.sciencedirect.com/science/article/abs/pii/000437029090007M
[01:08:45] GSM-8K dataset, OpenAI
https://huggingface.co/datasets/openai/gsm8k
