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AIC Seminar Series

Reusable Scalable Intelligent Systems

Carl Hewitt[Home Page]

Notice:  Hosted by Richard Waldinger

Date:  Thursday, December 12th 2019 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

   Abstract

Reusable Scalable Intelligent Systems (RSIS) have the following characteristics:

  • Interactively acquire information from video, Web pages, hologlasses, 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.
  • Reusability so that advances in one area can readily be used elsewhere without having to start over from scratch.
  • Scalability in all important dimensions meaning that there are no hard barriers to continual improvement in the above areas, i.e., system performance continually significantly improves.

Inconsistent and incoherence are the natural result of the enormous amount of information necessary for the operation of a Reusable Scalable Intelligent System. For any empirical proposition, there typically can be inconsistent information. Consequently, possibly inconsistent information is the default.

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.” Their 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 Reusable Scalable Intelligent Systems, e.g., for pain management and dementia management because data available for gradient training is very sparse since operation of a Reusable Scalable Intelligent System depends 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 an 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.

According to [Jordan 2018, Davis and Marcus 2019], overinvestment 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 architecture that provides services for their training, optimizing, debugging, refactoring, testing, and monitoring.

   Bio for Carl Hewitt

Carl Hewitt is an emeritus professor of computer science (MIT) who is best known for his work on the Actor model of computation, which is in widespread use in eBay, Microsoft, Twitter, etc. For the last decade, his work has been in the field of Inconsistency Robustness, which aims to provide practical rigorous foundations for systems dealing with pervasively inconsistent information. He is co-editor, with John Woods assisted by Jane Spurr, of the monograph “Inconsistency Robustness” (Vol. 52 of Studies in Logic).

Hewitt is currently Board Chair of the International Society for Inconsistency Robustness (iRobust™) and also Board Chair of Standard IoT™, an international standards organization for the Internet of Things, which is using the Actor Model to unify and generalize emerging standards for IoT. Also, he has been a Visiting Professor at Keio University and Stanford.

   Note for Visitors to SRI

Photography or broadcast of the event is prohibited unless specifically authorized by SRI. Reporters must coordinate with SRI 24 hours in advance before attending.
Please arrive at least 10 minutes early as you will need to sign in by following instructions by the lobby phone at Building E (or call Wilma Lenz at 650 859 4904, or Eunice Tseng at 650 859 2799). SRI is located at 333 Ravenswood Avenue in Menlo Park. Visitors may park in the parking lots off Fourth Street. Detailed directions to SRI, as well as maps, are available from the Visiting AIC web page. There are two entrances to SRI International located on Ravenswood Ave. Please check the Building E entrance signage.

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