Imagination Engines, Inc., Home of the Creativity Machine


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Imagination Engines, Inc., Home of the Creativity Machine
Imagination Engines, Inc., Home of the Creativity Machine
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VigillectTM Airport Security

Summary - IEI and its newly formed subsidiary, Vigillect, has worked with Transportation Security Administration (TSA) to develop truly clever and autonomous airport security systems. The unique advantage of these brilliant sensor networks is that they are constantly writing their own computer algorithms to detect the subtlest deviations from normal airport operations. This aspect is especially important when one considers the ever changing nature of the airport environment, as well as the cost and hassle of engaging humans to constantly modify system rules.

Details - Critical to these efforts was IEI’s coveted ability to coerce immense neural networks to knit themselves into brain-like cascades that can detect and classify anomalous objects and scenarios on the airport grounds. One very impressive accomplishment under our Phase I research under TSA was the detection of unusual scenarios at the passenger drop off areas that included identification of over parked vehicles as well as suspicious pedestrian milling behaviors. In similar fashion, our engineers were able to isolate and train especially large neural nets to recognize authorized vehicles and personnel. Introducing weight-in-motion readings from arriving vehicles we were able to detect suspiciously overloaded cars and trucks. Using bi-static radar returns from the parking lot we could also pinpoint unusual pedestrian and vehicular scenarios taking place therein.

Later, in Phase II of this TSA sponsored project, IEI was able to develop a similar sensor network for airport ramps and runways that could autonomously function round the clock, constantly forming combined spatial and temporal models of airport normalcy. While learning to ignore routine activity, the system could readily detect and alarm on unscheduled aircraft arrivals, as well as unauthorized personnel and stray animals on the runways and tarmac. Similarly, unauthorized vehicles and personnel could be identified at key entrance points. Finally, all neural nets from all nodes on the airport perimeter fed a master neural network cascade that constantly learned overall airport routine and could intelligently alert airport administrators to suspicious activities with minimal false alarm rates.

The key contributing factors to our successes on this and related military projects has been our unique ability to recreate many of the brain’s cognitive functions using inventive, self-connecting cascades of artificial neural networks, so-called “Supernets,” that have invented whole new methodologies and capabilities for performing a wide range of security-related functions.

For instance, using our extremely efficient neural networks devised not by humans, but by our inventive neural architectures, we are able to process all million or so bytes from each camera frame of a video stream, while preserving a 20-30 fps frame rate as the underlying networks learn to identify objects, scenarios, and deviations from status quo activity. Such machine-conceived neural nets are capable of developing an environmental normalcy model that is capable of evolving to keep pace with ever changing lighting conditions. Simultaneously, such nets are capable of highlighting, in very brain-like fashion, what doesn’t intrinsically belong to the scene in manner that is vastly superior to rather simplistic frame subtraction techniques and motion detection algorithms currently in use.

Similarly, our creative AI has invented an exceptional methodology for the isolation of newly arriving objects on the scene in a manner that is unusually resistant to environmental lighting fluctuations. This accomplishment has been the long sought after “holy grail” of camera-based anomaly detection efforts. Thereafter, the freshly extracted object or scenario can be passed to other self-organizing neural cascades for classification.

Crucial to a robust classification is a neural architecture and methodology, first conceived by our inventive neural nets, that is called a “Group Membership Filter” or “GMF.” Such GMFs self-organize themselves to recognize a given object or scenario over a wide range of sensor perspectives. This approach is vastly different in philosophy from the standard neural network approach wherein certain attributes are calculated from imagery and then compared against a previously generated database in a process that has become known as “registration.” In a sense, our GMFs become expert at identifying a thing or an action based upon multiple presentations of a given genre to it, without the need for training upon counter-examples. Furthermore, once identifying a target, they may lock on to it and automatically track it, constantly learning all of the alternative forms it may take as a result of changing orientation or illumination.

In the end, the extremely fast and large artificial neural networks we call “Self-Training Artificial Neural Network Objects” (STANNO) were able to interconnect via TCP/IP to create vast, brain-like governing layer that could now develop airport-wide normalcy models, as well as resolve ambiguous sensor inputs from the LAN’s distributed nodes. At the core of this immense neural architecture was a recently completed graphical programming library that allows us to drag and drop our STANNOs into a wiring diagram so that they could interconnect with themselves and supplied device interfaces to form these security-minded cognitive structures. We were thus able to rapidly prototype and refine candidate systems in the course of this TSA activity, and in so doing developed a methodology for tailoring our contemplative artificial intelligence systems to the suite of available sensors and actuators.

The ability of our systems to deal with and adapt to the unexpected, drawing upon our patented “Creativity Machine” neural architectures to cleverly devise the most effective course of action to a potentially harmful scenario. Such systems may either be mentored in advance by humans or bootstrap their competencies via our latest generation of self-correcting Creativity Machines. As is the case with all IEI ventures, the dependency upon human beings has been reduced, leading to lower costs, tireless vigilance, and much broader, human-like capabilities.

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