Weekly QuEST Discussion Topics and News, 10 Oct

October 10, 2014 Leave a comment

QuEST 10 Oct 2014

Since the discussion last week on the unexpected query – where we were attempting to define where will QuEST solutions bring value and how to define that – we’ve had a series of virtual interactions – the material below captures a snapshot of the points that we will discuss:

What is an agent?

  • We will use the term agent in a broad manner – to include human and computer agents
  • The common functionality of agents is they have sensors that allow them to capturestimuli from the environment
  • Once the stimuli are captured by an agent’s sensors and brought into the agent that is what we call data (note you cannot say data without saying with respect to which agent)
  • The agent then updates some aspect of its internal representation with that data thus reducing some uncertainty in that representation – so we now call that information (again note that information is agent centric as is data)
  • The agent might generate some action using its effectors or the effectors might just output stimuli into the environment for use by other agents.

What is a query?

  • Let’s define Query – as the act of a stimulus being provided to an agent – the stimulus has characteristics that completely capture the salient axes (keep in mind what is salient in a stimulus is agent centric) of the stimuli – some of those axes are captured by an agent in its conversion of that stimuli into data (agent centric internal representation of the stimuli)
  • We use the term query instead of stimuli to capture the idea that a given agent must take the stimuli and appropriately respond (thus an action)

–     that response may be to just update its representation or not or may actually be taking an action through an agent’ effectors

What is an unexpected query?

  • That is the goal of this discussion to bring some specifics to the idea of anunexpected query – but for now realize we use that term with some localization (maybe with respect to an agent – or maybe with respect to some process within an agent= performing agent – that is an unexpected query could be unexpected to some process within an agent or to the agent or collection of agents as a whole) – it is deemedunexpected to the performing agent and that label ‘unexpected’ is from the perspective of an external ‘evaluating agent’

–     But the point of the word ‘unexpected’ is to capture the idea that a process that takes in stimuli and responds (again could just be updating a representation or could be the response is some action taken) has some assumptions built into its design that may or may not be violated by a given stimulus (and the violation is from the perspective of an external agent)

  • When the assumptions that are key for the acceptable response are violated we will term that stimuli as being unexpected to that process/agent (performing agent) – and the violation is from the perspective of the evaluating agent
  • Note – the ‘unacceptable’ nature of any response is determined from the perspective of some other agent {evaluating agent} / process

–     so where one evaluating agent might take the response to a stimuli as unacceptable another may deem it perfectly acceptable – thus the unexpected nature of a stimuli is agent centric (note an agent – the evaluating agent – different than the one reacting to the stimulus the performing agent)

–     Note the evaluating agent has access to additional stimuli AND also has a model of the performing agent – thus can assess that the performing agent has an unacceptable response from its perspective

So why do we care:

  • So we posit that the type 2 processes (consciousness results from type 2 processing) that are situated and simulation based and include a model for the type 1 processes (that is what intuition provides us – at the conscious level it is our model of the evaluation of the Type 1 system projected into a conscious form) can be evaluation agents and detect UQs for the Type 1 processes
  • I suspect similarly the Type 1 processes might receive inputs from Type 2 stimuli and can possibly update their models – a late set of interactions with colleagues Robert P / Mike Y convince me this is a defendable position
  • Bottom line – a simulation based / situated representation can generate solutions to queries where the solutions don’t have to based on the stimuli – they are based on inferences from the simulation/ situation based representation – and from the evaluation agent called evolution perspective that provided more acceptable responses in leading to more reproduction …

What is consciousness?

  • Stable, consistent and useful situated simulation that is structurally coherent

–     The space for the unexpected query for such a representation complements the experiential representation which is the focus of Type 1 processes

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Weekly QuEST Discussion Topics and News, 3 Oct

October 2, 2014 Leave a comment

QuEST 3 Oct 2014:

1.)  We want to spend some bandwidth this week reviewing the questions asked by Mike R / Sandy V on formulating qualia.  It will also provide us as a group an opportunity to discuss any fall-out / vectors associated with our colleague Sandy’s dissertation prospectus defense.  Her abstract: Thisprospectus  examines how a computational model of a Qualia Generating Agent (QGA) can be applied to decisionsupport and AI systems for improved deci- sion making. The principal goal of the research is to develop the QGA ina Cognitive Modeling Architecture (CMA) to improve decision making in simulated event-driven environments andprovide a fundamental cognitive  component  for further research in computational modeling of narratives. Thespecific problem focus is to develop a computational formalism that allows a CMA to store, recall and process withagent- centric, context-dependent, conceptual knowledge, i.e., qualia, more in line with human cognition.  Themethodology will  model and simulate Stanovich’s Tripartite Framework, capture stimuli as episodic memory, aggregate and relate episodic memory into conceptual  knowledge and context, model working memory, andsimulate cognitive decoupling and mental simulation in working memory to compute a set of best explanations based on an abductive process.

2.)  Sandy led me to an article ‘ the importance of cognitive architectures: an analysis based on CLARION’ by Ron Sun.  Abstract: Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad, multiple-level, multiple-domain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will be undertaken, and an example cognitive architecture (CLARION) will be described.

3.)  These discussions led us last week to discuss how our view of the Link game can/should play in implementations – we might review those ideas to help the discussion – our previous discussion along this axes led us to talk about the memory competitions and how people create elaborate memory palaces to ‘store’ away the data.

4.)  Those discussions lead us back to what do we expect is the value added for QuEST solutions – What are the axes for improvement in performance for QuEST solutions – is it accuracy or in reduction in computational resources necessary or is it the ability to respond to the unexpected query – For a set of processes that are fragile and designed to provide very specific information – how can a conscious representation take those building blocks and construct a flexible response mechanism to respond to the unexpected query.  To help stimulate thoughts along these lines I reviewed this week a prior discussion we’ve had associated with ‘Good judgments do not require complex cognition’ v2 by Julian N. Marewski • Wolfgang Gaissmaier • Gerd Gigerenzer — in that work they make the point that – Widespread belief in psychology and beyond that complex judgment tasks require complex solutions.  Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true – although the article uses this point to suggest this is the purpose of Type 1 processes (heuristics) I think we can use the same argument to suggest the bit reduced (from a reality perspective) conscious part of the internal representation uses this approach

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Weekly QuEST Discussion Topics and News, 26 Sept

September 26, 2014 Leave a comment

QuEST 26 Sept 2014:

1.)  We want to start making a few remaining comments we didn’t get to last week –referencing the article from our Google colleagues ‘The unreasonable effectiveness of data’ by Halevy, Norvig, and Pereira.  This week we would like to emphasize choices that have to be made in engineering solutions:  Many people now believe there are only two approaches to:

  1. a deep approach that relies on hand coded grammars and ontologies, represented as complex networks of relations; and
  2. a statistical approach that relies on learning n-gram statistics from large corpora.
  3. When in fact there are three orthogonal problems:
  4.         • choosing a representation language,
  5.       • encoding a model in that language,

iii.      • performing inference on the model

  1. This is where our discussion we were having last week with our cyber colleagues Sandy V and Mike R. –

2.)  The second topic we want to hit maybe this week is related and associated with the above topic – the generation of symbolic representations – for QuEST we are talking the vocabulary of working memory, Qualia.  We want to review an article provided to our colleague Sandy V by Prof Ron Sun.  Autonomous generation of symbolic representations through subsymbolic activities  Ron Sun Version of record first published: 04 Sep 2012. …This paper explores an approach for autonomous generation of symbolic representations from an agent’s subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology.

3.)  Also I would like to point out an article we reviewed this week associated with the use of Google Glass for physiological parameter estimation – BioGlass: Physiological Parameter Estimation Using a Head-mounted Wearable Device – by Hernandez et al.

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Weekly QuEST Discussion Topics and News, 19 Sept

September 19, 2014 Leave a comment

QuEST 19 Sept 2014:

1.) We want to start making a few remaining comments we didn’t get to last week – the discussion was prompted by our colleague Qing W from Rome on Semantic Web efforts and specifically how they relate to ‘big-data’ and how they both relate to QuEST. Recall our Rome colleagues have been interacting with James Hendler of RPI. This also led us to an article from our Google colleagues ‘The unreasonable effectiveness of data’ by Halevy, Norvig, and Pereira. The best way to capture where all this fits versus what we are seeking in QuEST is a section in that article that draws the distinction between Semantic Web and Semantic Interpretation (if you will meaning – thanx Laurie F for keeping us focused on this key). Semantic web is a convention for formal representation languages that lets software services interact with each other without needing AI (or any of the meaning making we’ve discussed in QuEST). Services interact because they use the same standard OR known translations into a chosen standard – it is for ‘comprehending’ appropriately constructed semantic documents / data NOT understanding human speech / writings that haven’t been so constructed – that is the semantic interpretation problem which requires imprecise, ambiguous natural language. ** I clearly have issues with their use of the term ‘comprehending’ in that it is a form of rigidly defining pieces of documents and/or data so they can be combined in a rigorously defined manner and I don’t consider that ‘comprehension’ by the software that code embodies the comprehension of a predefined set of activities that should be allowed with these entries** The semantics in Semantic web is in the code that implements the services in accordance with the pre-wired specifications expressed by accepted ontologies and documentation on appropriate / acceptable manipulation of entries. The semantics in semantic interpretation is associated with meaning to a human as embodied in human cognitive and cultural processes…the goal of QuEST is to engineer computer agents that capture some of the ‘comprehension’ characteristics of human agents to include both intuitive (Type 1) and conscious (Type 2) aspects. We do NOT restrict the semantic aspects to the type 2 and our discussions on big data has captured what we can expect it to provide along the Type 1 axes.

2.) The second topic we want to hit maybe this week is the generation of symbolic representations – for QuEST we are talking the vocabulary of working memory, Qualia. We want to review an article provided to our colleague Sandy V by Prof Ron Sun. Autonomous generation of symbolic representations through subsymbolic activities Ron Sun Version of record first published: 04 Sep 2012. …This paper explores an approach for autonomous generation of symbolic representations from an agent’s subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology.

3.) Also I would like to point out an article we reviewed this week associated with the use of Google Glass for physiological parameter estimation – BioGlass: Physiological Parameter Estimation Using a Head-mounted Wearable Device – by Hernandez et al.

4.) Also we would like to revisit our discussion on Qualia based tracking and extend the ideas discussed to include Qualia based representations for Cyber Operations – we would like to take the proposed work by our colleague Mike R and brainstorm where / how a qualia based representation could play similar to our previous tracking discussion.

Weekly QuEST Discussion Topics and News, 12 Sept

September 11, 2014 Leave a comment

QuEST 12 Sept 2014:

1.) We want to start by addressing a comment made at the end of last week by our colleague Qing W from Rome on Semantic Web efforts and specifically how they relate to ‘big-data’ and how they both relate to QuEST. I’ve spent some time this week updating our Big data and QuEST slides to include capturing up front many of the walk-aways. We have previously (several years ago) gone down this semantic web path but it is worth revisiting where semantic web work fits. Our Rome colleagues have been interacting with James Hendler of RPI. We want to hit some of his presentations and discuss – this also led us to an article from our Google colleagues ‘The unreasonable effectiveness of data’ by Halevy, Norvig, and Pereira. The best way to capture where all this fits versus what we are seeking in QuEST is a section in that article that draws the distinction between Semantic Web and Semantic Interpretation (if you will meaning – thanx Laurie F for keeping us focused on this key). Semantic web is a convention for formal representation languages that lets software services interact with each other without needing AI (or any of the meaning making we’ve discussed in QuEST). Services interact because they use the same standard OR known translations into a chosen standard – it is for ‘comprehending’ appropriately constructed semantic documents / data NOT understanding human speech / writings that haven’t been so constructed – that is the semantic interpretation problem which requires imprecise, ambiguous natural language. ** I clearly have issues with their use of the term ‘comprehending’ in that it is a form of rigidly defining pieces of documents and/or data so they can be combined in a rigorously defined manner and I don’t consider that ‘comprehension’ by the software that code embodies the comprehension of a predefined set of activities that should be allowed with these entries** The semantics in Semantic web is in the code that implements the services in accordance with the pre-wired specifications expressed by accepted ontologies and documentation on appropriate / acceptable manipulation of entries. The semantics in semantic interpretation is associated with meaning to a human as embodied in human cognitive and cultural processes…the goal of QuEST is to engineer computer agents that capture some of the ‘comprehension’ characteristics of human agents to include both intuitive (Type 1) and conscious (Type 2) aspects. We do NOT restrict the semantic aspects to the type 2 and our discussions on big data has captured what we can expect it to provide along the Type 1 axes.

2.) The second topic we want to hit maybe this week is the generation of symbolic representations – for QuEST we are talking the vocabulary of working memory, Qualia. We want to review an article provided to our colleague Sandy V by Prof Ron Sun. Autonomous generation of symbolic representations through subsymbolic activities Ron Sun Version of record first published: 04 Sep 2012. …This paper explores an approach for autonomous generation of symbolic representations from an agent’s subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology.

3.) Also I would like to point out an article we reviewed this week associated with the use of Google Glass for physiological parameter estimation – BioGlass: Physiological Parameter Estimation Using a Head-mounted Wearable Device – by Hernandez et al.

news summary (1)

Weekly QuEST Discussion Topics and News, 5 Sept

September 4, 2014 Leave a comment

1.)  The first topic has to do with the current debate on ISIS / ISIL – we want to wrap in a few extra points that came up last week – and integrate them into the ‘think piece’ – specifically the connection to things like Ferguson Mo and also Rik W brought up the mechanisms used in Bee Hives to keep them healthy (how they eliminate the old / sick / weak.  So I again want to discuss is there a common theme that is consistent with our think piece –  Fighting an Adaptable foe.  Specifically the common issues in fighting in cyber, fighting the war on cancer and the fight against terrorism – now adding the mechanisms nature uses to maintain healthy societies like beehives and also social unrest like Ferguson Mo.  Recall our interest in this topic started by an article in the area of the fight against cancer the basic idea — still in the experimental stages — is that cancer cells cannot turn into a lethal tumor without the cooperation of other cells nearby. That may be why autopsies repeatedly find that most people who die of causes other than cancer have at least some tiny tumors in their bodies that had gone unnoticed.  *** in fact confirms matt’s brothers observation – and the lung cancer observation – that found as many lung cancers in nonsmokers although clearly more smokers die from lung cancer *** According to current thinking, the tumors were kept in check, causing no harm. … It also may mean that cancers grow in part because normal cells surrounding them allowed them to escape. It also means that there might be a new way to think about treatment: cancer might be kept under control by preventing healthy cells around it from crumbling*** this is the provide security and safety strategy approach to asymmetric war ***…“Think of it as this kid in a bad neighborhood,” said Dr. Susan Love, a breast cancer surgeon and president of the Dr. Susan Love Research Foundation. “You can take the kid out of the neighborhood and put him in a different environment and he will behave totally differently.” We also had to point of using insecticides attack pests in agriculture.

2.)   The second topic is we’ve recently had an open technical workshop on Sensing as a Service.  As part of that workshop we developed a list of actionable characteristics of SaaS solutions.  A discussion of where QuEST can impact these efforts would be interesting. 

3.)  The last topic, if we make it to it, is associated with our recent discussions on ‘big data’.  Over the last couple of weeks we’ve developed a  position of where/how QuEST fits with respect to big data efforts.  I would like to have a discussion on the walk-aways on big-data in general and specifically on QuEST and big data.  To have that discussion I would like to review ‘big data’ material we’ve reviewed over the last couple of years and pull it all together so people can catch up and comment.

Weekly QuEST Discussion Topics and News 5 Sept

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Weekly QuEST Discussion Topics and News, 29 Aug

August 28, 2014 Leave a comment

QuEST 29 Aug 2014:

1.)  The first topic has to do with the current debate on ISIS / ISIL.  It reminded me of a set of discussions that have happened over the last couple of years in the QuEST meetings and resulted in us putting together a ‘think piece’ on Fighting an Adaptable foe.  Specifically the common issues in fighting in cyber, fighting the war on cancer and the fight against terrorism.  In the area of the fight against cancer the basic idea — still in the experimental stages — is that cancer cells cannot turn into a lethal tumor without the cooperation of other cells nearby. That may be why autopsies repeatedly find that most people who die of causes other than cancer have at least some tiny tumors in their bodies that had gone unnoticed.  *** in fact confirms matt’s brothers observation – and the lung cancer observation – that found as many lung cancers in nonsmokers although clearly more smokers die from lung cancer ***According to current thinking, the tumors were kept in check, causing no harm. … It also may mean that cancers grow in part because normal cells surrounding them allowed them to escape. It also means that there might be a new way to think about treatment: cancer might be kept under control by preventing healthy cells around it from crumbling*** this is the provide security and safety strategy approach to asymmetric war ***…“Think of it as this kid in a bad neighborhood,” said Dr. Susan Love, a breast cancer surgeon and president of the Dr. Susan Love Research Foundation. “You can take the kid out of the neighborhood and put him in a different environment and he will behave totally differently.” … if there is interest we can revisit this ‘think piece’ to see if our newer QuEST ideas can impact differently now.

2.)   The second topic has to do with the discussion last week on how we do NOT think the solution to a general purpose artificial intelligence that can respond acceptably to the unexpected query is to learn the representation it is to learn/adapt the parameters of a simulation.  We are reminded in our interactions with Prof Geman – We have these quotes from his presentations he gave us:

The mind’s eye

  • The brain simulates
  • Representations must be nearly literal
  • We don’t learn representations; we learn the parameters of simulation (“strong priors”)

And

 

  • Nonparametric learning may have little or nothing to do with biological learning (ontogenetic & phylogenetic)
  • The advantages of simulation would explain the striking growth of the neocortex
  • The homogeneity of the cortex suggests repeatable and scalable rules of composition
  • Image understanding might be more a matter of constructing a scene model than of computing a classification

 

And with the work we discussed last week at QuEST that is all about classification / localization being where the big boys (google / facebook) are focused I think we are on an interesting path with QuEST … what I would like to discuss is finding relevant publications that attempt to attack the issue of the difference in learning the parameters for a simulation versus learning a representation?  How does this solve the Biederman problem?  And the answer to the Jared question – what are the parameters of the simulation? (to me they are the qualia – the vocabulary of conscious thought)

 

3.)  That brings us to the third topic – another Prof we’ve interacted with that inspired us to continue down the path of the conscious representation is a simulation versus a projection of sensory data – Prof Barsalou – a key attribute of simulation is the pattern completion inferencing – I would like to present his work that provides an interesting explanation of mirror neurons related to simulation – Mirroring as Pattern Completion Inferences within Situated Conceptualizations – … The classic account of mirroring is that it results from mirror neurons, namely, neurons that have both motor and perceptual tunings. Mirror neurons not only become active when an action is performed, but also when it is perceived.  Because these neurons become active during the perception of an action, they ground the perception in action simulation. An alternative account constitutes the thesis developed here: Mirroring is a special case of a basic cognitive process common across species, namely, Pattern Completion Inferences …  within Situated Conceptualizations (PCIwSC). According to PCIwSC, the brain is a situation processing architecture (Barsalou, 2003, 2009; Barsalou et al., 2003; Wilson-Mendenhall et al., 2011; Yeh and Barsalou, 2006).

Weekly QuEST Discussion Topics and News 29 Aug

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