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LLMs - Fancy Autocorrect or can they actually Reason?
Manage episode 469876863 series 3620285
תוכן מסופק על ידי David Such. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי David Such או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
In this episode, we discuss the limitations of Large Language Models (LLMs) in areas like deductive reasoning, analogy-making, and ethical judgment. While today’s AI models excel at recognizing statistical patterns in vast datasets, they lack genuine understanding or an internal model of the world. Researchers are tackling these challenges through innovations such as causal AI, inference-time computing, and neuro-symbolic approaches, all aimed at enabling AI to move beyond mere pattern recognition towards true reasoning.
We explore how these emerging technologies, including causal inference, inference-time computing, and neuro-symbolic integration, are pushing AI closer to human-like, “System 2” reasoning. Will these advancements finally bridge the gap between AI imitation and genuine reasoning? Tune in as we dive into the future of artificial intelligence and explore what it will take for machines to truly think.
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