Join us on The Before AGI Podcast as we explore groundbreaking research, including Apple's "Illusion of Thinking" paper, that challenges our understanding of AI reasoning capabilities. Discover why top AI models, even Large Reasoning Models (LRMs) designed to "show their work," can falter and even "give up" when faced with increasing compositional complexity in novel puzzles.
In this episode, you'll gain insights into:
🧠 LRMs vs. LLMs: The promise and limitations of AI models that generate step-by-step reasoning traces.
🧩 Beyond Benchmarks: Why standard tests often fail to capture true reasoning abilities and how new evaluation methods (like custom puzzles) reveal deeper insights.
💥 Key Research Findings: Unpacking the "accuracy collapse," counterintuitive effort scaling, and specific reasoning flaws (exact computation, inconsistency) identified in recent studies.
🤔 The "Illusion": How impressive surface-level performance can mask fundamental gaps in AI's ability to handle truly complex, novel problems.
💡 Implications: What these findings mean for trusting AI in high-stakes situations and the path towards more robust artificial intelligence.
This deep dive moves beyond the hype to critically examine the current state of AI reasoning, offering a more nuanced perspective on where these powerful technologies truly stand.
Follow Before AGI Podcast for more essential explorations into the frontiers of AI research!
TOOLS MENTIONED:
(AI Model types/concepts, not specific user software tools in this research context)
Large Reasoning Models (LRMs)
Large Language Models (LLMs)
Claude 3.5 Sonnet
GPT-4o / GPT-4 Turbo with Vision
Google Gemini
(Benchmarks like MMLU, GSM8K, VendingBench mentioned)
CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
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