Abstract: Artificial intelligence (AI) techniques that map disallowed states and enable access to those states under certain conditions through a search algorithm are disclosed. In other words, scenario boundaries may be crossed by jumping from one scenario that is less desirable or even has no solution to another scenario that is more desirable.

Abstract: An artificial intelligence (AI) system is disclosed that employs windowed cellular automata to create plausible alternatives. A cellular automata-based technique may be utilized to perform pattern recognition and assess the best path available (i.e., “instant improv”). Alternative sequences (i.e., “pattern improv”) may also be used to determine alternative paths. This instant improv and pattern improv may then be used to create completely new, plausible alternative nodes. The subsequent evaluation of the sentiment further creates new, dynamic capabilities. Through the use of windowed memory learning, recall, and interpolation, new plausible structures are generated that predict dynamic systems.

Abstract: Artificial intelligence (AI) techniques that map disallowed states and enable access to those states under certain conditions through a search algorithm are disclosed. In other words, scenario boundaries may be crossed by jumping from one scenario that is less desirable or even has no solution to another scenario that is more desirable.

Abstract: Artificial intelligence (AI) techniques that map disallowed states and enable access to those states under certain conditions through a search algorithm are disclosed. In other words, scenario boundaries may be crossed by jumping from one scenario that is less desirable or even has no solution to another scenario that is more desirable.

Abstract: An architecture and process are provided that encodes information into a cellular automata memory structure such that it can be recalled utilizing unique memory anchors (engrams) in a manner that both identifies and relates each piece of information relative to other data points. The automata may be individually programmable with a limited, local ruleset that activates other cellular automata based on prior patterns that were fed into the array. Deep Learning Neural Network (DLNN) systems may be probed to understand what discriminators are being used to classify the data, which is not possible with conventional DLNN techniques.

Abstract: Artificial intelligence (AI) techniques that map disallowed states and enable access to those states under certain conditions through a search algorithm are disclosed. In other words, scenario boundaries may be crossed by jumping from one scenario that is less desirable or even has no solution to another scenario that is more desirable.

Abstract: An artificial intelligence (AI) system is disclosed that employs windowed cellular automata to create plausible alternatives. A cellular automata-based technique may be utilized to perform pattern recognition and assess the best path available (i.e., “instant improv”). Alternative sequences (i.e., “pattern improv”) may also be used to determine alternative paths. This instant improv and pattern improv may then be used to create completely new, plausible alternative nodes. The subsequent evaluation of the sentiment further creates new, dynamic capabilities. Through the use of windowed memory learning, recall, and interpolation, new plausible structures are generated that predict dynamic systems.

Abstract: An architecture and process are provided that encodes information into a cellular automata memory structure such that it can be recalled utilizing unique memory anchors (engrams) in a manner that both identifies and relates each piece of information relative to other data points. The automata may be individually programmable with a limited, local ruleset that activates other cellular automata based on prior patterns that were fed into the array. Deep Learning Neural Network (DLNN) systems may be probed to understand what discriminators are being used to classify the data, which is not possible with conventional DLNN techniques.

Abstract: Provided are a techniques for monitoring communication packets. A communication packet is received. A communication packet signature of the communication packet is determined. The communication packet signature is compared to one or more site-specific application signatures. In response to determining that the communication packet signature matches at least of the one or more site-specific application signatures, it is determined that the communication packet is to be trusted.