arXiv:2606.24953v1 Announce Type: new Abstract: Machine learning (ML) algorithms are known to be opaque. We do not know the reasons for their predictions. The learning process leading to the prediction function is also opaque. We do not fully understand the time evolution of the weight values of neural nets (NN) and related dynamical phenomena. While prediction opacity is widely studied, learning opacity remains largely underexplored. This article studies learning opacity trough the lens of complex dynamical systems. We argue that NN learning is essentially a complex system and that learning o
The increasing complexity and deployment of AI systems necessitate deeper understanding of their internal workings beyond predictive accuracy.
Understanding 'learning opacity' is crucial for developing explainable, trustworthy, and robust AI systems, impacting their adoption in critical applications.
The focus of AI research expands beyond prediction opacity to encompass the fundamental lack of understanding in how AI models learn and evolve.
- · AI explainability researchers
- · Developers of transparent AI architectures
- · Regulators setting AI governance standards
- · AI developers prioritizing speed over interpretability
- · Black-box AI solution providers
Increased research and development into understanding complex dynamical systems within neural networks.
Demand for new theoretical frameworks and diagnostic tools to probe and analyze the learning processes of AI models.
Potential for a new generation of 'interpretable by design' AI architectures that are inherently less opaque during training and deployment.
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Read at arXiv cs.LG