Weekly QuEST Discussion Topics, 20 Apr

April 20, 2018 Leave a comment

QuEST 20 April 2018

We will have our colleague Prof Bert P provide lead us into a discussion of other approaches to AI (other than neural) that will provide us some of the requirements for our ‘representation’ concerns.  The material from last week that we didn’t get to – see below will be posted.

We want to focus our technical discussions this week on context.  We  have defined some of the characteristics we seek in 3rd wave AI and that included the ability to use/exploit context was listed.  We will start by reviewing previously discussed information about vision systems as an example of perception is all about context, to include Mach bands, negative color after images and also provide the details of what we referred to last week in the experiments that demonstrated the mammalian visual systems uses Gabor function models.  We will also demonstrate how the Limulus visual system can generate similar artifacts, again driving home the point it isn’t about the stimulus in isolation it requires context.

We then want to review material we’ve discussed on ‘context’, specifically material provided by our colleague Mitch Kokar.  This will provide us a path to discuss representations, specifically tools like sematic networks and OWL and statistical relationship learning.

Finally this leads us to a discussion on the difference between semantic Web and semantic interpretation.

  • The Semantic Web is a convention for formal representation languages that lets software services interactwith each other “without needing artificial intelligence.”11
  • The problem of understanding human speech and writing – the semantic interpretation problem-is quite different from the problem of software service interoperability.

–     Semantic interpretation deals with imprecise, ambiguous natural languages, whereas service interoperability deals with making data precise enough that the programs operating on the data will function effectively.

  • Unfortunately, the fact that the word “semantic” appears in both “Semantic Web” and “semantic interpretation“ means that the two problems have often been conflated, causing needless and endless consternation and confusion.

Eventually in later meetings we will planning domain definition language (PDDL) that is used to standardize AI planning language and its relationship to OWL ontological solutions.

 

 

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Weekly QuEST Discussion Topics, 13 Apr

April 12, 2018 Leave a comment

QuEST 13 April 2018

We want to start this week talking about intellectual property and patenting both in and outside the government.  Cap will discuss some experiences in taking ideas through the process associated with his previous Breast Cancer Detection business to include raising money, intellectual property strategy, provisional and full patents, details of how to read a patent.  We will hopefully also have a representative from the government patenting process to answer specific questions for those who are government employees.

We want to focus our technical discussions this week on context.  Last week when we defined some of the characteristics we seek in 3rd wave AI and that included the ability to use/exploit context was listed.  We will start by reviewing previously discussed information about vision systems as an example of perception is all about context, to include Mach bands, negative color after images and also provide the details of what we referred to last week in the experiments that demonstrated the mammalian visual systems uses Gabor function models.  We will also demonstrate how the Limulus visual system can generate similar artifacts, again driving home the point it isn’t about the stimulus in isolation it requires context.

We then want to review material we’ve discussed on ‘context’, specifically material provided by our colleague Mitch Kokar.  This will provide us a path to discuss representations, specifically tools like sematic networks and OWL and statistical relationship learning.

Finally this leads us to a discussion on the difference between semantic Web and semantic interpretation.

  • The Semantic Web is a convention for formal representation languages that lets software services interactwith each other “without needing artificial intelligence.”11
  • The problem of understanding human speech and writing – the semantic interpretation problem-is quite different from the problem of software service interoperability.

–     Semantic interpretation deals with imprecise, ambiguous natural languages, whereas service interoperability deals with making data precise enough that the programs operating on the data will function effectively.

  • Unfortunately, the fact that the word “semantic” appears in both “Semantic Web” and “semantic interpretation“ means that the two problems have often been conflated, causing needless and endless consternation and confusion.

Eventually in later meetings we will planning domain definition language (PDDL) that is used to standardize AI planning language and its relationship to OWL ontological solutions.

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Weekly QuEST Discussion Topics, 6 Apr

QuEST 6 Apr 2018

We want to review an article that has been bouncing around some of us and we’ve yet to be able to discuss – “Deep Learning:  A critical Appraisal” by Gary Marcus of NYU:

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Weekly QuEST Discussion Topics, 23 Mar

March 22, 2018 Leave a comment

QuEST 23 March 2018

We will start this week by having a discussion associated with the Bletchley Park Team (BPT) questions they left with us last week:

1.)  How are you currently documenting (representing) an agent’s interface/API?  How would you like to or plan to do that?

2.)  How are you currently documenting (representing) agent functionality?  How would you like to or plan to do that?

3.)  Will you certify agents’ “quality”?  How would you like to or plan to do that?

4.)  How are “missions” (ie, the goal of some agent composition) described?  How would you like to or plan to do that?

We won’t let the BPT off that easy as what we need them to do is provide us a path towards a solid foundation that we must follow to bring some rigor to the multi-agent ICA (interoperable, composeable, adaptable) solution.  But these conversations are important for the BPT and the algorithmic warfare team (AWT – call sign is still in competition) and the platform team (Colony of Neurons = CoN – call sign is still in competition) to have.  After that discussion we want to review an article that has been bouncing around some of us and we’ve yet to be able to discuss – “Deep Learning:  A critical Appraisal” by Gary Marcus of NYU:

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Weekly QuEST Discussion Topics and News, 16 Mar

March 15, 2018 Leave a comment

QuEST 16 March 2018

 

Last week our ‘Bletchley Park team’ led a discussion on the ideas related to multi-agent systems – specifically how can one agent develop a model of another agent’s representation.   We want to continue this week with that discussion and provide them feedback on the types of ‘models’ and agents we are interested in. 

 

As a reminder of context – we’ve emphasized that ‘flexible AI’ will require these multi-agent systems that are not originally designed to work together be able to interoperate / compose / adapt.  We’ve defined the axes of flexibility to be in terms of Peer (relationships between agents – supervisor, peer, subordinate), task (being able to do multiple tasks) and cognition (flexible representational options). 

 

We’ve often discussed steps along the way to full flexibility to include Peer ~ interoperable, Task ~ composition, Cognition ~ adaptable.   Our team of mathematicians ‘Bletchley Park team’ have been working on these ideas and will continue a discussion on the first two concepts.

 

Title:  Agent representations for interoperability and composition

 

Speakers:  Cybenko, Erdmann, Oxley

 

Abstract:  A concrete multimodal representation for agents is proposed.

The representation is suitable for interoperability and composition, and is based on ontology and machine learning concepts.  Ideas from recent results on data topology and the manifold hypothesis will be presented and related to this representation.

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Weekly QuEST Discussion Topics, 9 Mar

In our recent discussions we’ve converged on the relationship between ‘Autonomy’ to Artificial Intelligence.   Specifically we’ve emphasized the need for ‘flexible AI’.  We’ve defined the axes of flexibility to be in terms of Peer (relationships between agents – supervisor, peer, subordinate), task (being able to do multiple tasks) and cognition (flexible representational options).  We’ve often discussed steps along the way to full flexibility to include Peer ~ interoperable, Task ~ composition, Cognition ~ adaptable.   Our team of mathematicians have been working on these ideas and will lead a discussion on the first two concepts.

 

Title:  Agent representations for interoperability and composition

 

Speakers:  Cybenko, Erdmann, Oxley

 

Abstract:  A concrete multimodal representation for agents is proposed.

The representation is suitable for interoperability and composition, and is based on ontology and machine learning concepts.  Ideas from recent results on data topology and the manifold hypothesis will be presented and related to this representation.

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Weekly QuEST Discussion Topics, 2 Mar

QuEST 2 March 2018

There are several topics we want to hit this week.

There are challenges in artificial intelligence but capturing what is solved and what remains to be solved and what may never be solved is always difficult to communicate.  We are always seeking examples that can help senior leaders understand.  Our recent interest in the DARPA DEFT work provided one view in the text processing area that is worth noting.

ACT3 is focused on ‘Improving every Decision’.  A recent series of discussions with a colleague Reggie B led us into a discussion on Cinefin, an IBM approach to business decision making.  The construct also provides a spring board into discussions relating to our recent ‘Beyond the OODA nonsense’ and cognitive / collaborative EW discussions.  We will briefly discuss their approach to decision making.

Last week we discussed modeling other agents in a pool of agents that may want to work/need together.  One comment from our colleague George C. was modern big-data suggest the answer lies in the data.  So that leads us down two related threads of discussions.  There was our previous discussions on the unreasonable effectiveness of Data, an article by by Alon Halevy, Peter Norvig, and Fernando Pereira, of Google. 

In addition there was the work by George C. entitled:  Matching Conflicts: Functional Validation of Agents. 

  • In most working and proposed multiagent systems, the problems of identifying and locating agents that can provide specific services are of major concern.
  • A broker or matchmaker service is often proposed as a solution.
  • These systems use keywords drawn from application domain ontologies to specify agent services, usually framed within some sort of knowledge representation language.
  • However, we believe that keywords and ontologies cannot be defined and interpreted precisely enough to make brokering or matchmaking among agents sufficiently robust in a truly distributed, heterogeneous, multiagent computing environment.
  • This creates matching conflicts between, a client agent’s requested functionality and a service agent’s actual functionality.
  • We propose a new form of interagent communication, called functional validation, specifically designed to solve such matching conflicts.
  • In this paper we introduce the functional validation concept, analyze the possible situations that can arise in validation problems and formalize the mathematical framework around which further work can be done.

One last topic is in the area of innovation.  In the discussions with Reggie B we also got some vectors to recent models of innovation cycles that are worth discussing.   One topic was the Schumpeterian Cycle of Innovation and Entrepreneurship.

http://www.yourarticlelibrary.com/business/innovation-theory-of-trade-cycle-by-j-a-schumpeter/25996

The innovation theory of a trade cycle is propounded by J.A. Schumpeter. He regards innovations as the originating cause of trade cycles. The term “innovation” should not be confused with inventions. Inventions, in ordinary parlance, are discoveries of scientific novelties. Innovation is the application of such inventions to actual production (i.e., exploiting them).

It is innovations that are subject to cyclical fluctuations, not inventions. Innovation, thus, in economics means the commercial application of inventions like new techniques of production, new methods of organisation, novel products, etc.

Schumpeter regards trade cycles as the offspring of economic progress in a capitalist society. Cyclical fluctuations are inherent in the economic process of industrial production. When there are internal changes taking place on account of innovation, the development process begins…

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