Origin in late 14c., "shun (someone), refrain from (something), have nothing to do with (an action, a scandal, etc.), escape, evade," from Anglo-French avoider "to clear out, withdraw (oneself)," partially Englished from Old French esvuidier "to empty out," from es- "out" (see ex-) + vuidier "to be empty," from voide "empty, vast, wide, hollow, waste," from Latin vocivos "unoccupied, vacant," related to vacare "be empty," from PIE *wak-, extended form of root *eue- "to leave, abandon, give out." How can AI enable businesses to "empty, hollow, and/or waste" components of the supply chain? Machines, like humans can determine where waste is located in the supply chain and empty the supply chain of the waste. Machine learning and transportation can be equated to self driving vehicles. Imagine a world where self driving vehicles are delivering your packages, pallets, and containers. Robots delivering packages in your neighborhood. Sounds scary and interesting. Humans use "human learning" to avoid obstacles while driving. Machines will use "machine learning" to accomplish the same goals. Planes use machine learning to fly planes. Ships use machine learning to navigate the waters, avoiding obstacles and disruptions. How about avoiding all obstacles in all industries using machine learning? Sounds like a good plan.
Avoiding the void, maneuvering through trouble, learning the way