16. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states and includes context driven searching, said learning element comprising: means for sequentially receiving objects; long term memory means for storing in sequential context, a plurality of previously learned states, objects contained in said previously learned states, and predetermined types of knowledge relating to the stored previously learned states and said objects contained in said previously learned states, whereby from any stored information in said long term memory means the stored inforamtion which occurs next in context is directly accessible; short term memory means for storing in sequential context said received objects; means for correlating said received objects stored in said short term memory means with information stored in said long term memory means by implementing an nth order Markov process correlating several levels of stored context, said correlation being facilitated by using the context of said objects stored in said short term memory means as a pointer to the context of said information stored in said long term memory means, said correlating means assigning probabilities to possible next states in a sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to said objects stored in said short term memory means and said information stored in said long term memory means, for providing a signal corresponding to a probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence.
16. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states and includes context driven searching, said learning element comprising: means for sequentially receiving objects; long term memory means for storing in sequential context, a plurality of previously learned states, objects contained in said previously learned states, and predetermined types of knowledge relating to the stored previously learned states and said objects contained in said previously learned states, whereby from any stored information in said long term memory means the stored inforamtion which occurs next in context is directly accessible; short term memory means for storing in sequential context said received objects; means for correlating said received objects stored in said short term memory means with information stored in said long term memory means by implementing an nth order Markov process correlating several levels of stored context, said correlation being facilitated by using the context of said objects stored in said short term memory means as a pointer to the context of said information stored in said long term memory means, said correlating means assigning probabilities to possible next states in a sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to said objects stored in said short term memory means and said information stored in said long term memory means, for providing a signal corresponding to a probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence. 17. A probabilistic learning element as described in claim 16, wherein said long term memory means comprises a context organized memory including a plurality of tree structures for storing the various types of stored information in sequential context, each tree includes a plurality of sequentially connected nodes with each node storing an item of information and having a record of the stored item, said record including: the number of occurrences of the stored item in the sequential context of stored items represented by the node, a pointer to a node which represents the same sequential context of stored items without the last item of the sequence, a pointer to a node which represents the same sequential context of stored items with one additional stored item at each end of the sequence, and a pointer to a node which represents the same sequential context of stored items without the first stored item of the sequence, whereby a variable order Markov process is efficiently implemented.