Optimal Dynamics, a New York-based startup applying AI to shipping logistics, today announced it has closed an $18.4 million round led by Bessemer Venture Partners. Optimal Dynamics says the funds will be used to more than triple its 25-person team and support engineering efforts, as well as bolstering sales and marketing departments. Last-mile delivery logistics tend to be the most expensive and time-consuming part of the shipping process. According to one estimate, last-mile costs account for 53% of total shipping costs and 41% of total supply chain costs. With the rise of ecommerce in the U.S., retail providers are increasingly focusing on fulfilment and distribution at the lowest cost.

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates. Author's Disclosure: I am not an investor in Optimal Dynamics, either personally or through REFASHIOND Ventures. I have no other financial relationship with Optimal Dynamics. On July 7 I started a series on AI in Supply Chain (#AIinSupplyChain). The first article in the series profiled Optimal Dynamics, a startup that has launched a product to automatically optimize operations for large trucking fleets.

In stochastic systems, numerically sampling the relevant trajectories for the estimation of the large deviation statistics of time-extensive observables requires overcoming their exponential (in space and time) scarcity. The optimal way to access these rare events is by means of an auxiliary dynamics obtained from the original one through the so-called'generalised Doob transformation'. While this optimal dynamics is guaranteed to exist its use is often impractical, as to define it requires the often impossible task of diagonalising a (tilted) dynamical generator. While approximate schemes have been devised to overcome this issue they are difficult to automate as they tend to require knowledge of the systems under study. Here we address this problem from the perspective of deep learning.