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The arrival of ChatGPT, Google Bard, and other highly advanced artificial intelligence model show us just how brilliantly tasks can be reproduced by those engines. So, it's legitimate to wonder how our field (or any fields) might be affected in the future. We've already seen the beginnings of the possibilities, notably with LensNet [1], which provides optical designers with starting points for common cases; we can also study a solution space of certain type of lenses using deep learning [2]; and more recently, papers on the use of deep learning to simulate the entire chain of an optical system from object to final image processing, including tasks such as recognition. These latest end-to-end simulations have shown that in some cases, it is even necessary to redefine the optical optimization criteria to maximize certain computer tasks. In short, the computer doesn't necessarily need a good image in terms of MTF to perform its task. In this context, how the future will be affected or enhanced by these new AI approaches.
In this presentation, I will first give a brief history of how AI has impacted optical system design since 40 years. Then I will use examples to discuss the extraordinary acceleration in works over the past 5 years, the choices that have or haven't been made, and the importance of having access to source code from publications. Finally, I will conclude with some thoughts on what may or may not lie ahead, and how we can introduce these new technologies into the training of future optical system designers.
[1] Geoffroi Côté, Jean-François Lalonde, and Simon Thibault, "Deep learning-enabled framework for automatic lens design starting point generation," Opt. Express 29, 3841-3854 (2021).
[2] Geoffroi Côté, Yueqian Zhang, Christoph Menke, Jean-François Lalonde, and Simon Thibault, "Inferring the solution space of microscope objective lenses using deep learning," Opt. Express 30, 6531-6545 (2022).
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