# GSMSymbolic paper - Iman Mirzadeh (Apple)

Page: https://stenobird.com/podcast/machine-learning-street-talk/gsmsymbolic-paper-iman-mirzadeh-apple
Text version: https://stenobird.com/podcast/machine-learning-street-talk/gsmsymbolic-paper-iman-mirzadeh-apple.md
Podcast: [Machine Learning Street Talk (MLST)](https://stenobird.com/podcast/machine-learning-street-talk)
Published: 2025-03-19T22:33:28+00:00
Episode link: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/GSMSymbolic-paper---Iman-Mirzadeh-Apple-e30dhvp
Audio file: https://anchor.fm/s/1e4a0eac/podcast/play/100107705/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-2-19%2F22dc98c2-78df-ec59-226d-b38b3fd4bd0e.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/gsmsymbolic-paper-iman-mirzadeh-apple
Duration seconds: 4283

## Resource

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. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** 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-Alph…

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/gsmsymbolic-paper-iman-mirzadeh-apple/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/machine-learning-street-talk/gsmsymbolic-paper-iman-mirzadeh-apple.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.