Unsymbolic

Unsymbolic is a term used in artificial intelligence and cognitive science to describe approaches that do not rely on explicit symbolic representations and rule-based manipulation. Instead, unsymbolic methods use distributed, sub-symbolic representations and learning processes that extract patterns from data. This stands in contrast to symbolic AI, which uses discrete symbols and logic to represent knowledge and perform reasoning.

Origin and scope: The distinction emerged in debates about the limitations of hand-crafted symbolic systems in perception, learning, and real-world robustness. Unsymbolic approaches, such as neural networks and connectionist models, learn behaviors from examples and adjust parameters through optimization rather than encoding knowledge directly through rules.

Techniques and characteristics: Core techniques include neural networks, deep learning, convolutional and recurrent architectures, unsupervised and self-supervised learning, representation learning, and reinforcement learning. Unsymbolic systems typically operate on raw sensory input and develop latent representations that support a range of tasks. They are known for strengths in pattern recognition and adaptability but often face challenges with interpretability, systematic generalization, and data or compute requirements.

Applications and limitations: Unsymbolic methods are prominent in computer vision, speech recognition, natural language processing, robotics, and other domains involving high-dimensional data. Limitations include opaque decision processes, reliance on large datasets, potential brittleness under distribution shifts, and difficulties in integrating high-level, symbolic reasoning.

Relation to symbolic AI: Many researchers explore hybrid approaches, combining unsymbolic learning with symbolic reasoning in neuro-symbolic AI to leverage the strengths of both paradigms—robust perception and interpretable, rule-based reasoning.

Notable associations: Pioneering work in sub-symbolic learning has been advanced by researchers such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, among others.