In this episode of our podcast, we dive deep into one of the most talked-about questions in AI and robotics today: Are large language models like GPT really the right foundation for building autonomous robots?
At first glance, the idea sounds compelling. GPT models have shown phenomenal success in text generation, translation, image analysis, and more. It seems only natural to assume that this architecture could revolutionize robotics as well. But a recent research paper we explore in this episode challenges that notion — and offers a strikingly different perspective.
The paper’s central argument is built around a bold comparison: massive transformer models vs. the miniature, yet astonishingly efficient, biological systems like the brain of a bee. While GPTs require hundreds of gigabytes of memory, thousands of GPU hours, and terabytes of data to learn about the world, a bee can learn to fly, navigate using sunlight, find food, and even communicate symbolically — all within about 20 minutes of flight.
We explore why transformer architecture may be inherently ill-suited for building embodied intelligence. The issues range from enormous computational demands and lack of built-in world models (known as inductive biases) to limited metacognition and the tendency to “hallucinate” — generating confident but completely false information.
We pay special attention to the issue of reliability. A language model making an error in text is annoying. A robot making a false move based on a faulty interpretation of the world? That’s potentially dangerous. The article highlights how biological systems like insects outperform even the most advanced AI in areas like efficiency, robustness, and transparency of decision-making.
What makes the insect brain so special? Modularity and structure. Unlike the homogeneous architecture of transformers, insect brains are composed of highly specialized regions — from the protocerebral bridge that acts as an internal compass to the mushroom body responsible for multimodal learning and decision-making. These systems are energy-efficient, fast-learning, and evolutionarily refined.
We also explore alternative approaches to AI that may offer more promise for robotics. These include Objective AI — modular, structured architectures that incorporate explicit models of the world — and neurosymbolic AI, which blends the perception power of neural networks with the reasoning capabilities of symbolic logic.
The key takeaway: Transformers are powerful tools, but perhaps not the ultimate foundation for robust, autonomous robots. Instead, the future may lie in hybrid systems — grounded in biological principles and designed with structure and efficiency in mind.
Final reflection for our listeners: Between data-hungry statistical models and structured, biologically inspired systems — which traits of natural intelligence do you think are most essential for next-gen robotics? Efficiency, adaptability, common-sense reasoning? The answer may shape the entire trajectory of autonomous systems in the years ahead.
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Read more: https://www.nature.com/articles/s44182-025-00025-4
