Project Liftoff: Universal Reusable Secure Intelligent Systems by 2030
30 Pages Posted: 8 Aug 2019
Date Written: July 29, 2019
Reusable Secure Intelligent Systems (RSIS) have the following characteristics:
• Interactively acquire information from video, Web pages, hologlasses (electronic glasses with holographic-like overlays), online data bases, sensors, articles, human speech and gestures, etc.
• Real-time integration of massive pervasively inconsistent information
• Self-informative in the sense of knowing its own goals, plans, history, provenance of its information and having some information about its own strengths and weaknesses.
• Close human interaction using hologlasses (electronic glasses with holographic-like overlays) for secure mobile communication. Computers alone cannot implement the above capabilities.
• No closed-form algorithmic solution is possible to implement the above capabilities
• Reusable enabling applications readily be used elsewhere without having to start over from scratch.
• Secure meaning that there are no easy ways to penetrate systems or deceive them into taking inappropriate actions
The goal of Project Liftoff is to achieve universal Reusable Secure Intelligent Systems by 2030..
Hologlasses are electronic glasses that provide holographic-like overlays, which will be used for just about everything including the following: driving (vehicle detection, traffic sign recognition, obstacle avoidance, navigation), warfare (navigation, reconnaissance, hazard avoidance, command and control), exercising ( navigation, timing, goals, repetition counting, advice), walking (obstacle avoidance, navigation, oncoming vehicle detection), reading (privacy, hyperlinks, synonyms and antonyms, annotations), cooking (menus, demonstrations, suggestions), shopping (price comparison, videos of product use, navigation), construction (placement, coordination, scheduling), maintenance (diagnosis, placement, verification), teamwork (shared presentations, agenda, meeting notes), entertainment (privacy, interactive movies, music, games), medical (medication management, diagnosis, pain management, physical therapy, dementia management, record keeping, meditation, visualizations), conversation (facial movements, eye tracking, pupil contraction and dilation), education (tutoring, demonstrating, collaborating)
According to [Shamir, et. al 2019] “given any two classes C1 and C2, along with any point x∈C1, and our goal is to find some nearby y which is inside C2. Our ability to do so suggests that all the classes defined by neural networks are intertwined in a fractal-like way so that any point in any class is simultaneously close to all the boundaries with all the other classes.” The results show gradient classifier can be used against itself to find nearby inputs that are classified differently that one classified by a given input. [Shamir, et. al 2019] results suggests that gradient classifiers (aka "Deep Learning") could remain forever fragile in ways that cannot be remedied by larger training sets.
Consequently, a gradient classifier can be useful for heuristic rule-of-thumb guidance but should not be relied upon as the exclusive basis of consequential judgments especially ones involving life and/or death.
A gradient classifier (regardless of size) is not scalable to implement intelligent systems, e.g., for pain management and dementia management because data available for gradient training is very sparse since operation of such systems depend heavily on history of the case at hand, e.g., what worked previously, progression of ailments, recent interactions, etc. Also, multiplicity of potential actions (e.g. in pain management and dementia management) is very large because at any point interaction can take many different directions, e.g., social interaction, physical therapy, medication management, etc.
A large gradient classifier is not modular. For example, a pain management gradient classifier cannot be incorporated into a dementia management gradient classifier because the pain management classifier does not provide sensory inputs that are usable by the dementia management gradient classifier. Furthermore, in order to incorporate additional knowledge into the dementia management gradient classifier, the knowledge must be illustrated in operation using sensory input to create input-output training data. Creating input-output training data to incorporate additional general knowledge can be extremely difficult because of unknowable interactions with previous input-output training data including history dependence and multiplicity of potential actions. Gradient classifiers for intelligent systems cannot be combined to create a gradient classifier implement a larger intelligent system because of numerous complex interdependencies.
According to [Jordan 2018, Davis and Marcus 2019], over-investment in gradient classifiers (aka “Deep Learning”) is crowding out necessary investment in other technologies for Intelligent Systems
Multitudinous different kinds of classifiers need to be created and operated in an ontology that provides services for their real-time training, optimizing, debugging, refactoring, testing, and monitoring.
Keywords: Reusable Scalable Intelligent Systems, Pain Management, Massive Inference Robust Ontology, Deep Learning, Gradient Classifier
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