Home > Uncategorized > Weekly QuEST Discussion Topics and News, 11 Aug

Weekly QuEST Discussion Topics and News, 11 Aug

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.

 

news summary (64)

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