Inverse Problems
Determining the underlying causes or parameters from observed data, essentially reversing the usual process of predicting effects from known causes.
Inverse problems are crucial in AI and machine learning because they focus on deducing the parameters or structures that produce observed data. These problems are often ill-posed, meaning that they do not have a unique solution or their solutions are not stable under small perturbations of the data. AI techniques, especially those involving optimization and probabilistic methods, are employed to solve inverse problems by formulating them as inference tasks. For example, in medical imaging, inverse problems are solved to reconstruct images of the inside of a body from X-ray data. AI approaches can include regularization methods to handle ill-posedness and Bayesian frameworks to incorporate prior knowledge and quantify uncertainties.
The concept of inverse problems dates back to the early 19th century with foundational work in areas such as geophysics and medical imaging. The application of AI to solve inverse problems gained traction in the late 20th century with advances in computational power and algorithmic development, particularly in the 1980s and 1990s.
Significant contributors to the field of inverse problems include mathematicians and scientists like Andrey Tikhonov, who introduced regularization techniques, and researchers in AI like Geoffrey Hinton and David MacKay, who advanced Bayesian methods and neural networks applicable to inverse problems. The interdisciplinary nature of this field has seen contributions from diverse areas such as applied mathematics, computer science, and engineering.