Media, method and system for generating an itinerary using machine learning. To accomplish this, a reinforcement learning model is trained on historical data of past trips taken and their corresponding costs. The reinforcement learning model uses a self-play algorithm to train itself to generate itineraries which minimize the cost. The reinforcement learning model is then used to train a supervised learning model. The trained supervised learning model is given a set of input requirements and generates as an output an itinerary to send to a user.

An annotation system for a neural network and a method thereof are disclosed in the present application. The annotation system comprises a memory and a processor operatively coupled to the memory. The memory is configured for storing instructions to cause the process to receive information comprising a first set of unlabeled instances from at least one source; set a learning target of the information; select a second set of unlabeled instances from the first set of unlabeled instances by executing a software algorithm; and annotate the second set of unlabeled instances for generating labeled data. The software algorithm increases an efficiency of annotation in training neural networks for deep-learning-based video analysis by combining semi-supervised learning and transfer learning via a data augmentation method. The software algorithm can increase the efficiency of annotation by reducing an amount of annotation by an order of one magnitude.

A system for transportation includes a vehicle configured to have a rider located therein or thereon, and an expert system to produce a recommendation for a configuration of the vehicle, wherein the recommendation includes at least one recommended parameter of configuration for the expert system that controls a parameter selected from the group consisting of a vehicle parameter, a rider experience parameter, and combinations thereof.