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

QuEST

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.

 

 

Bio

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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Weekly QuEST Discussion Topics, 10 Nov

November 9, 2017 Leave a comment

QuEST 10 Nov 2017

We will have a meeting on Thursday at noon due to the holiday – and similarly next week due to travel commitments.

This week we have Prof Ox presenting some background material on Dr. Vapnik’s approach to statistical pattern recognition / regression. The reason we need to establish some basic understanding is we are investigating bring Dr. Vapnik on-site to work with us in enhancing our current solutions to building more flexible systems.

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Weekly QuEST Discussion Topics, 3 Nov

November 2, 2017 Leave a comment

A group of us have embarked upon a mission to build some QuEST agents for use in a range of applications (ISR, Air-to-Air Mission Effect chain, Business processes).  What we’ve noticed is people tripping over words (to be expected) but when you are attempting to actually code computers you have to clear up issue / confusion. What I mean is people who code AI solutions say one thing and people who have been in our discussions argue against the instantiation using our terminology concluding that the implementation misses the ‘magic’.  Keep in mind any sufficiently advanced technology will appear as magic (one of Arthur C. Clarke’s 3 adages / sometimes known as Clarke’s 3 laws).

What is our artificial conscious construct look like in a computer.  We will start with a reminder of the key defining characteristics of QuEST agents and specifically focus on the issue of how do you make / code the conscious representation.  What is new /different than all the other AI work going on?

The second topic is to focus on a specific application – captioning what is going on in a video:

  • Video captioning and semantic description is a research area that with the advent of deep learning has gained widespread interest in recent years.
  • Despite the increased number of publications and methods in previous years that address this problem, there is an increasing need for a thorough study and survey of recent methodologies and algorithms dedicated to this problem.
  • In order to mitigate such lack of information, we present a study of video captioning methods throughout recent years and identify current issues and trends of modern techniques focused in this area.
  • We also introduce a novel multiple decoder framework for automatic semantic description of label video sequences
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Weekly QuEST Discussion Topics and News, 27 Oct

October 26, 2017 Leave a comment

QuEST 27 Oct 2017

A group of us have embarked upon a mission to build some QuEST agents for use in a range of applications (ISR, Air-to-Air Mission Effect chain, Business processes).  What we’ve noticed is people tripping over words (to be expected) but when you are attempting to actually code computers you have to clear up issue / confusion. What I mean is people who code AI solutions say one thing and people who have been in our discussions argue against the instantiation using our terminology concluding that the implementation misses the ‘magic’.  Keep in mind any sufficiently advanced technology will appear as magic (one of Arthur C. Clarke’s 3 adages / sometimes known as Clarke’s 3 laws).

What is our artificial conscious construct look like in a computer.  We will start with a reminder of the key defining characteristics of QuEST agents and specifically focus on the issue of how do you make / code the conscious representation.  What is new /different than all the other AI work going on?

The second topic is to focus on a specific application – captioning what is going on in a video:

  • Video captioning and semantic description is a research area that with the advent of deep learning has gained widespread interest in recent years.
  • Despite the increased number of publications and methods in previous years that address this problem, there is an increasing need for a thorough study and survey of recent methodologies and algorithms dedicated to this problem.
  • In order to mitigate such lack of information, we present a study of video captioning methods throughout recent years and identify current issues and trends of modern techniques focused in this area.
  • We also introduce a novel multiple decoder framework for automatic semantic description of label video sequences

news summary (71)

 

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