Home > Uncategorized > Weekly QuEST Discussion Topics and News, 9 June

Weekly QuEST Discussion Topics and News, 9 June

QuEST 9 June 2017

We want to start this week by returning to defining the advancements required in knowledge creation to achieve autonomy, specifically from the perspective of can we define a sequence of steps in advancing complexity of the knowledge being created required to achieve flexibility in peer / task / cognition.  We will have the discussion under the realization that we need a solution that scales, we need to improve every decision and be able to do so without re-engineering the autonomy for each application.  We need a knowledge creation platform!  What will that mean?

An autonomous system, AS, is one that creates the knowledge necessary to remain flexible in its relationships with humans and machines, tasks it undertakes, and how it completes those tasks in order to establish and maintain trust with the humans and machines within the organization the AS is situated in.  

….    

Self – as we mature our discussion on autonomy – we have to address the idea of ‘self’ – and ‘self-simulation’ – from our recent chapter on ‘QuEST for cyber security’

 

4.2 What is consciousness?

Consciousness is a stable, consistent and useful ALL-SOURCE situated simulation that is structurally coherent. [2, 4, 23, 27, 35, 44]  This confabulated cohesive narrative complements the sensory data based experiential representation, the subconscious. [22, 42]  The space of stimuli resulting in unexpected queries for such a representation complements the space of unexpected queries to the experiential based representation that is the focus of the subconscious. (Figure 5)  The vocabulary of the conscious representation is made up of qualia. [6, 7, 8, 17]  Qualia are the units of conscious cognition.  A quale is what is evoked in working memory and is being attended to by the agent as part of its conscious deliberation.  A quale can be experienced as a whole when attended to in working memory by a QuEST agent.  Qualia are experienced based on how they are related to and can interact with other qualia.  When the source of the stimulus that is being attended to is the agent itself the quale of ‘self’ is evoked.  A QuEST agent that has the ability to generate the quale of self can act as an evaluating agent to itself as a performing agent with respect to some task under some range of stimuli.  This is a major key to autonomy.  A QuEST agent that can generate the quale of self can determine when it should continue functioning and give itself its own proxy versus stopping the response and seeking assistance

 

4.3 Theory of Consciousness

Ramachandran suggested there are laws associated with qualia (irrevocable, flexibility on the output, buffering). [29]  Since we use the generation of qualia as our defining characteristic of consciousness we can use his work as a useful vector in devising our Theory of Consciousness.  The QuEST theory of consciousness also has three defining tenets to define the engineering characteristics for artificial conscious representations.  These tenets constrain the implementation of the qualia, working memory vocabulary of the QuEST agents. [43,32]  Tenet 1 states the representation has to be structurally coherent.  Tenet 1 acknowledges that there is minimal awareness acceptable to keep the conscious representation stable, consistent, and useful.  Tenet 2 states the artificially conscious representation is a simulation that is cognitively decoupled. [18, 19] The fact that much of the contents of the conscious representation is inferred versus measured through the sensors provides enormous cognitive flexibility in the representation.  Tenet 3 states the conscious representation is situated. [9,10] It projects all the sensing modalities and internal deliberations of the agent into a common framework where relationships provide the units of deliberations. [25,26,31,45,46]  This is the source of the Edelman imagined present, imagined past, and imagined future. [12]  

4.4 Awareness vs Consciousness

There is a distinction between awareness and consciousness.  Awareness is a measure of the mutual information between reality and the internal representation of some performing agent as deemed by some evaluating agent.  Consciousness is the content of working memory that is being attended to by a QuEST agent.  Figure 8 provides examples of how a system can be aware but not conscious and vice versa.  In the blind sight example the patient has lost visual cortex in both hemispheres and so has no conscious visual representation. [5] Such patients when asked what they see, say they see nothing and that the world is black.  Yet when they are asked to walk where objects have been placed in their path they often successfully dodge those objects.  Verbal asking is responded to based-on information that is consciously available to the patients.  These patients have awareness of the visual information but no visual consciousness.  Similarly, body identity integrity disorder (BIIDs) and alien hand syndrome (AHS) are examples of issues that illustrate low awareness while the patient is conscious of the appendages.  Paraphrasing Albert Einstein “imagination is more important than knowledge,” we state consciousness is often more important than awareness.  There will always be limitations to how much of reality can be captured in the internal representation of the agent, but there are no limits to imagination.

Autonomy requires cognitive flexibility.  Cognitive flexibility requires, at least part of, the internal representation be a simulation (hypothetical). (Figure 9)

Situation awareness (SA) is defined by Endsley to be the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. [13]  The concept of SA is intimately tied to the mutual information between the internal representation, reality, and awareness.  On the other hand, situation consciousness (SC) is a stable, consistent, and useful ALL-SOURCE situated simulation that is structurally coherent.  This last constraint of being structurally coherent requires the SC representation only achieve enough mutual information with reality to maintain stability, consistency, and usefulness.  

Figure 10 captures a desired end state for our work.  We envision teams of agents (humans and computers) that can align since designed with similar architectures.  These solutions are called wingman solutions.  The goal is to generate a theory of knowledge.  Such a theory would estimate the situation complexity of the environment and be able to predict a set of agents, humans, and computers that have a situation representation capacity that matches.

 

 

The second topic – pursuing the thread that we need some means to generate the ‘imagined’ present/past/future – is associated with a relatively recent article on video prediction.  

DEEP MULTISCALE VIDEO PREDICTION BEYOND

MEAN SQUARE ERROR

Michael Mathieu1, 2, Camille Couprie2 & Yann LeCun1,

arXiv:1511.05440v6 [cs.LG] 26 Feb 2016

 

ABSTRACT

Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel-space video prediction may be viewed as a promising avenue for unsupervised feature learning. In addition, while optical flow has been a very studied problem in computer vision for a long time, future frame prediction is rarely approached. Still, many vision applications could benefit from the knowledge of the next frames of videos, that does not require the complexity of tracking every pixel trajectory. In this work, we train a convolutional network to generate future frames given an input sequence. To deal with the inherently blurry predictions obtained from the standard Mean Squared Error (MSE) loss function, we propose three different and complementary feature learning strategies: a multi-scale architecture, an adversarial training method, and an image gradient difference loss function. We compare our predictions to different published results based on recurrent neural networks on the UCF101 dataset

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