1. A method on a computer for aiding in the analysis of a legal matter using an artificial neural network and a Bayesian network, comprising: generating an artificial neural network representing relationships between a plurality of legal inquiries and a plurality of legal conclusions, wherein the artificial neural network comprises a plurality of nodes, wherein each node is associated with one or more weights and a function that calculates a legal conclusion based on one or more input values and the weights; generating a Bayesian network representing probabilistic relationships between a plurality of legal inquiries and a plurality of legal conclusions, wherein the Bayesian network comprises a plurality of nodes and a plurality of edges connecting the nodes, wherein each node is associated with a variable that represents either an answer to a legal inquiry or a legal conclusion, and an edge represents a conditional dependency between variables of nodes; associating a probability function with each node, wherein a probability function takes as input one or more values of variables from one or more parent nodes and gives a probability of a child node's variable; providing to a user, via a graphical user interface, the plurality of legal inquiries; receiving from the user, via the graphical user interface, answers to at least a portion of the legal inquiries; calculating one or more input values for the plurality of nodes of the artificial neural network based on the answers provided by the user; replacing a variable of each node of the Bayesian network corresponding to an answer provided by the user with a value representing the answer; executing the artificial neural network based on the one or more input values, so as to generate a resulting legal conclusion based on the answers provided by the user; executing the probability function of each node of the Bayesian network, thereby calculating the probability of each legal conclusion based on the answers provided by the user; displaying for the user, via the graphical user interface, the legal conclusion calculated via the artificial neural network and the probability of each legal conclusion from the Bayesian network; storing a record associated with the user, including the legal conclusion calculated via the artificial neural network, the probability of each legal conclusion from the Bayesian network, the plurality of legal inquiries and the plurality of answers; receiving a legal update comprising a change in law that affects how the legal conclusions are reached in the artificial neural network and how the probability of each legal conclusion is reached in the Bayesian network; modifying the artificial neural network and the Bayesian network in light of the legal update; executing the modified artificial neural network based on the one or more input values, so as to generate a new resulting legal conclusion based on the answers provided by the user; executing the probability function of each node in the modified Bayesian network, thereby re-calculating the probability of each legal conclusion based on the answers provided by the user; and wherein if the legal conclusion in the modified artificial neural network does not match the legal conclusion in the record that was stored, or if the probability of each legal conclusion in the modified Bayesian network does not match the probability of each legal conclusion in the record that was stored, sending a message to the user.