Embedded AI - Intelligence at the Deep Edge
“Intelligence at the Deep Edge” is a podcast exploring the fascinating intersection of embedded systems and artificial intelligence. Dive into the world of cutting-edge technology as we discuss how AI is revolutionizing edge devices, enabling smarter sensors, efficient machine learning models, and real-time decision-making at the edge.
Embedded AI - Intelligence at the Deep Edge
LLMs - Fancy Autocorrect or can they actually Reason?
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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