One of the longest-running challenges in the logistics industry is finding the shortest routes. First articulated in the 1930s, the "traveling salesman problem" seeks to deduce the shortest route connecting a group of cities to ensure optimal use of time and resources. Karim Beguir, co-founder and CEO of London-based AI startup InstaDeep, told GPU Technology Conference attendees this week that GPU-powered deep learning and reinforcement learning may have the answer. Previous efforts to address the traveling salesman problem include optimization solvers, heuristics and Monte Carlo Tree Search algorithms. But, according to Beguir, these approaches all suffer from the same shortcoming: They don't learn.

There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to handle machine learning inference work, and they have to find new employees in a relatively shallow pool of available AI talent. None of this is easy, but the trend is irreversibly toward AI, machine learning and deep learning, so decisions need to be made, according to Karim Beguir.