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Weekly QuEST Discussion Topics and News, 18 Aug

August 17, 2017 Leave a comment

QuEST Aug 18 2017

Inferences about Consciousness Using Subjective Reports of Confidence
Maxine Sherman

  • Metacognition, or “knowing that you know”, is a core component of consciousness. ** to many this is part of the definition – introspection ** Insight into a perceptual or conceptual decision permits us to infer perceptual or conscious knowledge underlying that decision. ** seem to distinguish decisions made based on what is being sensed and what or what is being thought about perceptual / conceptual – we would suggest that even perceptual decisions that are done consciously are put into the conceptual representation **
  • However when assessing metacognitive performance care must be taken to avoid confounds from decisional and/or confidence biases. There has recently been substantial progress in this area and there now exist promising approaches
  • In this chapter we introduce type I and II signal detection theory (SDT), and describe and evaluate signal detection theoretic measures of metacognition. We discuss practicalities for empirical research with these measures, for example, alternative methods of transforming extreme data scores and of collecting confidence ratings, with the aim of encouraging the use of SDT in research on metacognition. We conclude by discussing metacognition in the context of consciousness.

 

Cross-modal prediction changes the timing of conscious access
during the motion-induced blindness
Acer Y …

Consciousness and Cognition 31 (2015) 139–147

  • Metacognition, or “knowing that you know”, is a core component of consciousness. ** to many this is part of the definition – introspection ** Insight into a perceptual or conceptual decision permits us to infer perceptual or conscious knowledge underlying that decision. ** seem to distinguish decisions made based on what is being sensed and what or what is being thought about perceptual / conceptual – we would suggest that even perceptual decisions that are done consciously are put into the conceptual representation **
  • However when assessing metacognitive performance care must be taken to avoid confounds from decisional and/or confidence biases. There has recently been substantial progress in this area and there now exist promising approaches

Post-decision wagering objectively measures awareness
Navindra Persaud, Peter McLeod & Alan Cowey

2007 Nature Publishing Group http://www.nature.com/natureneuroscience

NATURE NEUROSCIENCE VOLUME 10 [ NUMBER 2 [ FEBRUARY 2007

  • The lack of an accepted measure of awareness consciousness has made claims that accurate decisions can be made without awareness consciousness controversial. Here we introduce a new objective measure of awareness consciousness, post-decision wagering.
  • We show that participants fail to maximize cash earnings by wagering high following correct decisions in blindsight, the Iowa gambling task and an artificial grammar task.
  • This demonstrates, without the uncertainties associated with the conventional subjective measures of awareness consciousness(verbal reports and confidence ratings), that the participants were not aware that their decisions were correct.
  • Post-decision wagering may be used to study the neural correlates of consciousness.

We had some exciting work this summer and we want to give people quick overviews of some of the efforts – we had two deep learning efforts of particular notes by our colleagues Oliver and Washington and then there were at least four projects that might be good to bring up:

*Context-Learning Deep Neural Networks for Kinematic Prediction by Dr. Kyle Tarplee (Anderson Univ.)  – Combines a DNN that learns patterns in traffic to a mixture density network (MDN) that is a DNN designed for motion prediction (c.g. Kalman filters).

Estimating Posteriors from Deep Learning Networks by Nicole Eikmeier (Purdue) – Exploits the stochasticity of drop-out regularization to compute “confidence” values for the network’s performance.

Predictive Simulation for UAV Flight Planning by Saniyah Shaikh (UPenn) – Uses Monte-Carlo Tree Search (MCTS) a la AlphaGo to plan UAV flight paths.

Practical Applications of Graph Convolutional Neural Networks in Sensor Exploitation by Mela Hardin (ASU) – She presented highlights from some recent work in the hot field of graph CNNs, i.e. bringing the power of CNNs to relational data.

 

For those that have VDL access we have wiki pages with details for following up

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Weekly QuEST Discussion Topics and News, 11 Aug

August 10, 2017 Leave a comment

QuEST 11Aug 2017

A recent study by the Harvard Kennedy School – Felfer Center for Science and International Affairs – “Artificial Intelligence and National Security” done for IARPA concluded among other things:

“By looking at four prior cases of transformative military technology—nuclear,

aerospace, cyber, and biotech—we develop lessons learned and recommendations for national security policy toward AI.

Future progress in AI has the potential to be a transformative

national security technology, on a par with nuclear weapons,

aircraft, computers, and biotech.

 

−− Each of these technologies led to significant changes in the

strategy, organization, priorities, and allocated resources of the

U.S. national security community.

−− We argue future progress in AI will be at least equally

impactful.”

 

—- that is an amazing statement – some discussion is warranted.  For example take the lessons from the AlphaGo and extrapolate to multi-domain Command and Control implications?

 

A second set of topics from our Colleague Teresa H:

 

How We Save Face—Researchers Crack the Brain’s Facial-Recognition Code

A Caltech team has deciphered the way we identify faces, re-creating what the brain sees from its electrical activity

 By Knvul Sheikh | Scientific American August 2017 Issue

 

The brain has evolved to recognize and remember many different faces. We can instantly identify a friend’s countenance among dozens in a crowded restaurant or on a busy street. And a brief glance tells us whether that person is excited or angry, happy or sad.

Brain-imaging studies have revealed that several blueberry-size regions in the temporal lobe—the area under the temple—specialize in responding to faces. Neuroscientists call these areas “face patches.” But neither brain scans nor clinical studies of patients with implanted electrodes explained exactly how the cells in these patches work.

The Code for Facial Identity in the Primate Brain

Authors

Le Chang, Doris Y. Tsao

Correspondence

lechang@caltech.edu (L.C.),

dortsao@caltech.edu (D.Y.T.)

In Brief

Facial identity is encoded via a remarkably simple neural code that relies

on the ability of neurons to distinguish facial features along specific axes in face space, disavowing the long-standing assumption that single face cells encode individual faces.

 

Cross-modal prediction changes the timing of conscious access
during the motion-induced blindness
Acer Y …

Consciousness and Cognition 31 (2015) 139–147

  • Metacognition, or “knowing that you know”, is a core component of consciousness. ** to many this is part of the definition – introspection ** Insight into a perceptual or conceptual decision permits us to infer perceptual or conscious knowledge underlying that decision. ** seem to distinguish decisions made based on what is being sensed and what or what is being thought about perceptual / conceptual – we would suggest that even perceptual decisions that are done consciously are put into the conceptual representation **
  • However when assessing metacognitive performance care must be taken to avoid confounds from decisional and/or confidence biases. There has recently been substantial progress in this area and there now exist promising approaches

 

Inferences about Consciousness Using Subjective Reports of Confidence
Maxine Sherman

 

Metacognition, or “knowing that you know”, is a core component of consciousness. Insight into a perceptual or conceptual decision permits us to infer perceptual or conscious knowledge underlying that decision. However when assessing metacognitive performance care must be taken to avoid confounds from decisional and/or confidence biases. There has recently been substantial progress in this area and there now exist promising approaches. In this chapter we introduce type I and II signal detection theory (SDT), and describe and evaluate signal detection theoretic measures of metacognition. We discuss practicalities for empirical research with these measures, for example, alternative methods of transforming extreme data scores and of collecting confidence ratings, with the aim of encouraging the use of SDT in research on metacognition. We conclude by discussing metacognition in the context of consciousness.

 

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Weekly QuEST Meeting Discussion Topics and News, 4 Aug

August 3, 2017 Leave a comment

QuEST 4 Aug 2017:

This week we will have a guest lecture by colleagues from UCLA to discuss the paper by Achille / Soatto UCLA, arXiv:1706.01350v1 [cs.LG] 5 Jun 2017

On the emergence of invariance and disentangling in deep representations

Lots of interesting analysis in this article but what caught my eye was the discussion on properties of representations:

  • In many applications, the observed data x is high dimensional (e.g., images or video), while the task y is low-dimensional, e.g., a label or a coarsely quantized location. ** what if the task was a simulation – that was stable, consistent and useful – low dimensional?**
  • For this reason, instead of working directly with x, we want to use a representation z that captures all the information the data x contains about the task y, while also being simpler than the data itself.  ** and are there a range of tasks y that can be serviced by a representation z – how do we address the tension between the representation and the tasks – how do we define what tasks can be serviced by a given representation?**
  • Ideally, such a representation should be
  • (a) sufficient for the task y, i.e. I(y; z) = I(y; x), so that information about y is not lostamong all sufficient representations, it should be
  • (b) minimal, i.e. I(z; x) is minimized, so that it retains as little about x as possible, simplifying the role of the classifier; finally, it should be
  • (c) invariant to the effect of nuisances I(z; n) = 0, so that decisions based on the representation z will not overfit to spurious correlations between nuisances n and labels y present in the training dataset
  • Assuming such a representation exists, it would not be unique, since any bijective function preserves all these properties.
  • We can use this fact to our advantage and further aim to make the representation
  • (d) maximally disentangled, i.e., TC(z) is minimal, where disentanglement is often measured as the correlation of the network weights… the paper appears to use total correlation, which is the (presumably one-sided) KL divergence between the joint PDF of the weights and the Naïve Bayes estimate à KL(f(w1, w2, …, wn), f(w1)f(w2)…f(wn))
  • This simplifies the classifier rule, since no information is present in the complicated higher-order correlations between the components of z, a.k.a. “features.”
  • In short, an ideal representation of the data is a minimal sufficient invariant representation that is disentangled.
  • Inferring a representation that satisfies all these properties may seem daunting. However, in this section we show that we only need to enforce (a) sufficiency and (b) minimality, from which invariance and disentanglement follow naturally.
  • Between this and the next section, we will then show that sufficiency and minimality of the learned representation can be promoted easily through implicit or explicit regularization during the training process.

As we mature our view of how to work to these rich representation it brings up the discussion point of QuEST as a platform:

 

I would like to think through a QuEST solution that is a platform that uses existing front ends (application dependent by observation vendors) and existing big-data back ends like standard Big Data Solutions such Amazon Web services … , and possibly a series of knowledge creation vendors  – It is helpful here to consider the Cross Industry Standard Process for Data Mining (commonly known by its acronym CRISPDM, is a data mining process model that describes commonly used steps data mining experts use to tackle data mining problems) to show how QuEST fits within, and can enable, all aspects of the CRISP-DM process.

Independent of the representation used by a front end system that captures the observables and provides them to the QuEST agent – it becomes the quest agent’s job to take them and create two uses for them – the first is put them in a form usable by a big-data solution (following CRISP-DM, this would entail the Data Understanding, and Data Preparation), but do so based on an understanding of the relevant QuEST model (CRISP-DM, Modeling), and in a way that supports CRISP-DM Business Understanding (e.g., perhaps infer it based on its ‘Sys2 Artificial Consciousness’ – the next piece) to find if there exists experiences stored – something close enough to them to provide the appropriate response when in the CRISP-DM Deployed phase – and the second form has to be consistent with our situated / simulation tenets – so they are provided to a ‘simulation’ system that attempts to ‘constrain’ the simulation that will generate the artificially conscious ‘imagined’ present that can complement the ‘big-data’ response – in fact the simulated data might be fed as ‘imagined observables’ into the back end, infer gaps in CRISP-DM Business Understanding that then also feed the big-data response, and offer more valuable contributions to users in CRISP-DM Deployment– I would like to expand on this discussionnews summary (63)

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