Differentiable programming is the latest descendant of deep learning. It has unlocked a series of challenges which were previously seen as “unsolvable” and has paved the way for considerable progress and superior numerical results in the world of Supply Chains. In this episode of LokadTV we tackle this extremely exciting new development and learn how this major breakthrough means we can now perform optimizations, even when faced with high levels of uncertainty.
In supply chains there are a whole series of unsolved problems that the industry has been struggling with for years. We learn how solutions to the big AI problems, such as voice recognition, image analysis and autonomous vehicles, can be applied to specific supply chain products and why the present time has been the better moment to revisit these failures.
We explore how differentiable programming provides a joint resolution to these problems and makes it possible to mix factors that are logically so different. Finally, we discuss what this breakthrough in large-scale numerical optimisation means for future supply chains and how considerable progress and superior numerical results can be made.
But there are still many “wicked” problems that remain unsolvable, despite these technological leaps and bounds. Problems that would “take Skynet to solve”. So how far away are we from a Terminator-esque future with robots taking over the world? Watch and find out!