alt Hacker NewsThe inevitable AI angle: researchers are increasingly exploring perception, self-modeling, and grounded interaction as the foundation for the next wave of AI systems, ones that behave in ways more aligned with human-like awareness than LLMs alone can provide (see work like MUSE or situational awareness in vision-language reasoning).
Systems like the electronic nose described here highlight what many think is missing in current AI approaches: continuous physical sensing combined with explicit novelty detection and decision boundaries that let a system say “this is real”, “this is happening now”, or “this is outside what I know”. Human-like behavior is unlikely to emerge from language models in isolation; it appears to come from closed perception-reasoning loops that are causally coupled to the environment. Without sensory grounding, AI tends to optimize for plausibility rather than correctness, and scaling or prompting alone doesn’t seem to address that gap.
LLMs are trained on text about the world, not the world itself. Olfaction is an interesting test case because it's one of the most ancient and direct sensory modalities. no symbolic abstraction layer, just molecular binding triggering pattern recognition.
What's compelling about pairing e-nose hardware with transformer architectures is you get that grounded perception loop you're describing. The sensor array produces high-dimensional response patterns from real physical interactions, and the model learns to classify and reason over patterns it's never been explicitly trained on—genuine novelty detection rather than interpolation over training data.
The "this is outside what I know" capability is critical for real-world deployment. A model that hallucinates a scent classification is potentially dangerous (think: fentanyl detection in law enforcement). You need calibrated uncertainty, not just a softmax score.