Weekly QuEST Discussion Topics, 2 Feb

February 1, 2018 Leave a comment

QuEST 2 Feb 2018

After last week’s discussion we have spent the week focusing on the ‘knowledge platform’.  How can we take the problems we are addressing and use the resulting advances to mature a ‘knowledge platform.’  The platform provides the business model and the technological framework to in an agile fashion be able to deliver capability for a range of applications.  The key question is “what is the ‘representation’ approach for this knowledge creation platform?”.  The discussions have focused on what is a simulation / situated nature of the representation.

This week we want to return to some of the material that over the years have supported our ideas on simulation / situated nature of the conscious representation.  For example:

620 Barsalou

Annu. Rev. Psychol. 2008.59:617-645. Downloaded from arjournals.annualreviews.org

by EMORY UNIVERSITY on 02/13/08.

Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain’s modal systems for perception, action, and introspection.

Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development.

Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.

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Weekly QuEST Discussion Topics and News, 26 Jan

January 25, 2018 Leave a comment

QuEST 26 Jan 2018

It has been a great week in the QuEST / ACT3 world.  We’ve had the privilege of working with Rauf Izmailov one of the thought leaders in machine learning.  The question we were investigating is how to adopt a diversity of cognitive approaches to enhance our interest in cognitive flexibility specifically impacting our interest in a ‘knowledge platform’ – a general approach that can be applied to our 3 development vectors with a single engineering team.

The discussions have focused around the core QuEST ideas – how to take current best of breed approaches to artificial intelligence and combine them into flexible solutions ~ autonomy.  This all leads to some fundamental philosophical and engineering questions.  We want to spend our QuEST time this week having those questions posed / discussed.  They include how do we share information / knowledge / meaning across ‘agents’ – that leads to transfer learning relationships:

1.)    In our search for ‘peer flexibility’ – how do we define ‘transfer learning’ – we’ve had a series of conversations about this topic – for example:  http://www.technologyreview.com/business/25833/ … The approach is an example of a machine-learning technique dubbed “transfer learning,” says Yang. “Transfer learning tries to learn in one space (text) and then apply the learned model to a very different feature space (such as images),” he says, and also Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher… Andy Ng, and also arXiv:1712.01238v1  [cs.CV]  4 Dec 2017 – Learning by asking questions …andhttps://www.wired.com/2017/03/openai-builds-bots-learn-speak-language/

2.)  Is this useable as a definition of the unexpected query? In the sense that an UQ is a need to have a ft (a target transformation function for a new task)  to acceptably respond to an element from Dt – the ft is a model we don’t have so on our vertical axis we are low! But we are possibly far to the right since we are getting a clear view of the environment or we would be if the features between source and target are the same – BUT keep in mind there is far more to generating the appropriate representation than classification/regression/clustering – we need a cohesive narrative – CHALLENGE – we need to extend this definition beyond classification … – to simulation – we need a simulation of the current environment as the representation – and UQ defined in that space! — Pan survey on transfer learning


We also want to have a discussion on what is a simulation – we want to briefly review the ideas that led to us concluding that consciousness is a simulation that is situated and structurally coherent.  The goal of this discussion is to crystalize ‘what is new with QuEST’.


This all leads to a discussion of our primary interest – representation – what are the constraints in the representation that impact in a quantifiable manner the ability of a solution to respond to a query.  How can we capture a representation of the streaming video that can be used to generate useful captions and also useful for high valued target tracking and event detection.

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Weekly QuEST Discussion Topics, 19 Jan

January 18, 2018 Leave a comment

QuEST 19 Jan 2018:

As a group of QuEST people are focused on diversity of representations for autonomy we will spend the time tomorrow discussing work from some colleagues on Support Vector Machines +.  They’ve been using Vapnik’s teacher/student paradigm wherein the extra context/implicit knowledge comes from the presence and multiplicity in the human generated annotation / notes.

The new SVM+ is being considered as an alternative mechanism (to the current CNN/RNN approach) as a step towards cognitive flexibility.

Prof Oxley will give us a tutorial in preparation for our colleagues coming on-site and working with us next week.

Some relevant links for Vapnik’s newer work.
LUPI paper: http://www.jmlr.org/papers/volume16/vapnik15b/vapnik15b.pdf
SVM+ presentation slides: http://web.mit.edu/zoya/www/SVM+.pdf

3rd party implementation, out of curiosity: https://arxiv.org/abs/1604.01518

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Weekly QuEST Meeting cancelled for 12 Jan

January 11, 2018 Leave a comment

There will be no QuEST meeting this Friday, Jan 12.

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Weekly QuEST Discussion Topics, 5 Jan

January 4, 2018 Leave a comment

Kabrisky Lecture 2018


5 Jan 2018


Dr. Matthew Kabrisky was an Air Force pioneer and innovator.  From Air Force aviator in the 1950s to professor, mentor, and researcher, his discoveries paved the way for many modern technological advancements.  He developed theories of how the human brain processes information to recognize visual objects. This work directly led to the innovation of implanted electrodes for those afflicted with diseases such as epilepsy and injuries that resulted in paralysis. He was the leading international expert on the physiological symptoms of space adaptation sickness, i.e., motion sickness.  His research led NASA to a better understanding and an approach to mitigate the effects of space environments on astronauts.  His research in the area of robust speech recognition laid critical foundations for fostering the development of DoD and private industry products ranging from voice activated controls in advanced tactical aircrafts, to aides for the disabled and handicapped and industrial process control. In the 1990s, he helped lead a team of engineers that developed the world’s most accurate breast cancer detection system.  This highly successful product has helped in the detection of thousands of breast cancers before they would have otherwise been detected. Dr. Kabrisky’s pioneering efforts paved the way for current innovations across the Nation, the Air Force and at the Air Force Institute of Technology.

Every January the QuEST group uses the first lecture of the calendar year to present a ‘state of QuEST’ lecture in his honor as he was a founding member of the QuEST group.  This lecture sometimes takes more than one meeting as it is designed to bring anyone up to speed on how we use terms (for example intuition / consciousness …) and to communicate what we seek to accomplish and how we are pursuing our goals.

n  QuEST is an innovative analytical and software development approach to improve human-machine team decision quality over a wide range of stimuli (handling unexpected queries) by providing computer-based decision aids that are engineered to provide both intuitive reasoning and “conscious” context sensitive thinking.

n  QuEST provides a mathematical framework to understand what can be known by a group of people and their computer-based decision aids about situations to facilitate prediction of when more people (different training) or computer aids are necessary to make a particular decision.

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Weekly QuEST Discussion Topics, 8 Dec

December 7, 2017 Leave a comment

QuEST 8 Dec 2017

Many of our team is focused on ACT3 – Autonomy Capability Team 3.  We will focus this week’s discussion on ‘Events’ – specifically how does QuEST envision event detection.  We will start by defining and explaining in the context of several technical vectors situations and events and then discuss their implications.

n  situation is any part of the agent centric internal representation which can be comprehended as a whole by that agent through defining how it interacts with or is related to other parts of the representation in that agent.

n  We will define comprehended by defining how it interacts or is related to other situations via linking (and types of links).

n  interacting with other things we mean that the situations have properties or relate to other situations.” *** we would say  can and must be linked to other ‘situations’  = ‘other qualila’ = other chunks***

Then we will expand the discussion to events.

Christoffersen, Woods, and Blike (2007) provide a concise, contemporary definition of events in their modified version of Newtson’s unit marking procedure (UMP) (See Newtson, 1973).

          They describe events as a meaningful pattern of change in the environment of an observer, a definition that is grounded in studies of ecological psychology (e.g. Gibson, 1979; Warren and Shaw, 1985; McCabe and Balzano, 1986).

Let’s talk a soccer match – what is an event – note how from a coaches perspective

talking to a defender – versus a fan’s perspective looking at ESPN highlights

Meaningful could be that it results in an action by the agent – that action could be the agent expects to communicate the occurrence of that event to another agent or could be that agent notes the occurrence because it is know to be part of the defining sequence for other events

  • From our Eventstream team:
  • In the context of the EventStream, the underpinning for the conceptualization of “events” is the Christoffersen/Woods description of an event as a “(meaningful) pattern of change over time”. This suggests a definition of events in the PED environment that would read something like: operationally meaningful interpretations of the raw FMV feed data.
  • Given this definition, examples of events for a Vehicle Follow would include:
  • the vehicle beginning or ending transit,
  • a person entering or exiting the vehicle,
  • the driver or passenger of the vehicle interacting with another person outside the vehicle, etc.
  • Thus, an event would be approximately equivalent to a single row in the Excel target log (in some cases).
  • We would just extend the definition to be change over more than time – space and modality … – and can be comprehended (defines how can interacts with and how is related to other parts of world)

An event is any part of the agent centric internal representation which is comprehended as a whole by that agent through experiencing how it is interacting with AND is related to other parts of the representation in that agent and the agent assesses the saliency of this event (which is a situation) may require action / or communication to another agent.

The vocabulary that is used to experience events are qualia since all event detection is the result of type 2 processes – since by our definition above a key aspect of the definition is they may require action/ communication to another agent – ex) this event caused the agent to do that action …, AND the event itself is experienced thus is a Quale itself

We’ve recently found that event based representations are often more valuable than map based – but the real issue is how to formulate information in a manner that together with the human analyst accomplish some mission (sometimes it is object based, sometimes it is activity based but in general we need an approach that can accommodate forms for this operator doing this task at this moment with this data) – overlays are common but we are actively attacking the issue of not just using overlays because although they accelerate a current approach to analysis we are seeking to find what approach (example event based representation / processing) provides greater mission capability – that may or may not be fusing diverse sources/types of information into a map display

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Weekly QuEST Discussion Topics, 16 Nov

November 15, 2017 Leave a comment

QuEST – 16 November 2017


We are honored to have John Launchbury (bio below) speak at QuEST on Thursday – we had exciting discussions with him on representations and our goals in QuEST.




Dr. John Launchbury rejoined Galois in September 2017 as the Chief Scientist focused on collaborating with government and industry leaders to fundamentally improve the security of cyber-physical systems. He also leads Galois’s involvement with industry partners looking to leverage applied formal mathematical techniques to make functional guarantees about the software their teams develop.


Prior to rejoining Galois in 2017, John was the director of the Information Innovation Office (I2O) at DARPA, where he led nation-scale investments in cryptography, cybersecurity for vehicles and other embedded systems, data privacy, and artificial intelligence.


Dr. Launchbury received first-class honors in mathematics from Oxford University in 1985, holds a Ph.D. in computing science from the University of Glasgow and won the British Computer Society’s distinguished dissertation prize. In 2010, Dr. Launchbury was inducted as a Fellow of the Association for Computing Machinery (ACM).

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