Weekly QuEST Discussion Topics and News, 24 Mar

March 23, 2017 Leave a comment

QuEST March 24 2017

 

This week we will have our colleague Sean M provide us his insights into the topic of Remote Viewing and relate those to the QuEST interest in impacting the ‘subconscious’ of the human teammates to our computer based QuEST agents for more effective group (human-machine) decisions:

 

What engineering advantage can we obtain against the mission of QuEST by taking a serious look at the US government-sponsored “psychic spy” program?  In the words of a former Army unit member, Major David Morehouse, this was “one of the intelligence services’ most controversial, misunderstood, and often feared special access programs.”  In this discussion Sean Mahoney will attempt to demystify the subject.

 

In September of 1995 the public learned of a nearly 23 yearlong intelligence gathering program that utilized a purely human centered ISR technique called ‘remote viewing’.  Remote viewing, as it was used by the military, was developed at the Stanford Research Institute (now SRI International) under contract by the CIA in the 1970s.  It is a teachable, structured pen-and-paper process by which a person can interview their own subconscious mind about a pre-selected target, that their conscious mind is blind to, and report data on that target.

 

Since declassification, many former military remote viewers wrote books and created training programs describing the methodologies they used successfully throughout the life of the government program. A community has sprung up around the practice with thousands of people across the globe actively applying remote viewing to various uses. There is now an International Remote Viewing Association (IRVA), 2 magazines, an annual conference, and a few professional consulting groups that offer remote viewing services for anything from missing persons or property, to legal cases, to research and development efforts. Through books, formal training, conference attendance, and lots of practice, Sean has learned several different methodologies of remote viewing as they have been taught by former military unit members.  Sean will present on his experiences with remote viewing and what he feels it reveals about intuitive cognition and the nature of consciousness. Please join us for this interesting discussion.

news summary (46)

Categories: Uncategorized

Weekly QuEST Discussion Topics and News, 17 Mar

March 16, 2017 Leave a comment

QuEST 17 March 2017

Again there were several interesting email conversation threads going on this week:

We want to hit briefly the article from last week – this thread was initiated by Trevor and Todd from our Sensing dendrite:

Why does deep and cheap learning work so well?
Henry W. Lin and Max Tegmark
Dept. of Physics, Harvard University, Cambridge, MA 02138 and
Dept. of Physics & MIT Kavli Institute, Massachusetts Institute of Technology, Cambridge, MA 02139

arXiv:1608.08225v2 [cond-mat.dis-nn] 28 Sep 2016

  • We show how the success of deep learning depends not only on mathematics but also on physics: although well-knownmathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through “cheap learning” with exponentially fewer parameters than generic ones, because they have simplifying properties tracing back to the laws of physics.
  • The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability, and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings.
  • We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one.
  • We formalize these claims using information theory and discuss the relation to renormalization group procedures. We prove various “no-flattening theorems” showing when such efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss: flattening even linear functions can be costly, and flattening polynomials is exponentially expensive; we use group theoretic techniques to show that n variables cannot be multiplied using fewer than 2^n neurons in a single hidden layer.

A related topic to this first one:

In another email thread this week we were asked about:

http://www.sciencemag.org/news/2017/03/brainlike-computers-are-black-box-scientists-are-finally-peering-inside?utm_campaign=news_daily_2017-03-07&et_rid=54802259&et_cid=1203472

Brainlike computers are a black box. Scientists are finally peering inside

By Jackie SnowMar. 7, 2017 , 3:15 PM

Last month, Facebook announced software that could simply look at a photo and tell, for example, whether it was a picture of a cat or a dog. A related program identifies cancerous skin lesions as well as trained dermatologists can. Both technologies are based on neural networks, sophisticated computer algorithms at the cutting edge of artificial intelligence (AI)—but even their developers aren’t sure exactly how they work. Now, researchers have found a way to “look” at neural networks in action and see how they draw conclusions.

Neural networks, also called neural nets, are loosely based on the brain’s use of layers of neurons working together. Like the human brain, they aren’t hard-wired to produce a specific result—they “learn” on training sets of data, making and reinforcing connections between multiple inputs. A neural net might have a layer of neurons that look at pixels and a layer that looks at edges, like the outline of a person against a background. After being trained on thousands or millions of data points, a neural network algorithm will come up with its own rules on how to process new data. But it’s unclear what the algorithm is using from those data to come to its conclusions.

“Neural nets are fascinating mathematical models,” says Wojciech Samek, a researcher at Fraunhofer Institute for Telecommunications at the Heinrich Hertz Institute in Berlin. “They outperform classical methods in many fields, but are often used in a black box manner.”

In an attempt to unlock this black box, Samek and his colleagues created software that can go through such networks backward in order to see where a certain decision was made, and how strongly this decision influenced the results.Their method, which they will describe this month at the Centre of Office Automation and Information Technology and Telecommunication conference in Hanover, Germany, enables researchers to measure how much individual inputs, like pixels of an image, contribute to the overall conclusion. Pixels and areas are then given a numerical score for their importance. With that information, researchers can create visualizations that impose a mask over the image. The mask is most bright where the pixels are important and darkest in regions that have little or no effect on the neural net’s output.

For example, the software was used on two neural nets trained to recognize horses. One neural net was using the body shape to determine whether it was horse. The other, however, was looking at copyright symbols on the images that were associated with horse association websites.

This work could improve neural networks, Samek suggests. That includes helping reduce the amount of data needed, one of the biggest problems in AI development, by focusing in on what the neural nets need. It could also help investigate errors when they occur in results, like misclassifying objects in an image.

Other researchers are working on similar processes to look into how algorithms make decisions, including neural nets for visuals as well as text. Continued research is important as algorithms make more decisions in our daily lives, says Sara Watson, a technology critic with the Berkman Klein Center for Internet & Society at Harvard University. The public needs tools to be able to understand how AI makes decisions. Algorithms, far from being perfect arbitrators of truth, are only as good as the data they’re given, she notes.

In a notorious neural network mess up, Google tagged a black woman as a gorilla in its photos application. Even more serious discrimination has been called into question in software that provides risk scores that some courts use to determine whether a criminal is likely to reoffend, with at least one study showing black defendants are given a higher risk score than white defendants for similar crimes. “It comes down to the importance of making machines, and the entities that employ them, accountable for their outputs,” Watson says

 

Not attempting to be dismissive but:

Cathy is pulling the technical article – but from the text in the news article this appears to be a rehash of something we invented in 1990:

 

  • Ruck, D. W., Rogers, S., Kabrisky, M., “Feature Selection Using a Multilayer Perceptron”, Journal of Neural Network Computing, Vol 2 (2), pp 40-48, Fall 1990.

 

When you use a supervised learning system with a mean squared error objective function and differentiable nonlinear neurons – then you can solve the partial differential equations to extract ‘saliency’ – that is you can work through any decision and rank order the inputs to decide an ‘order’ to their impact – in 1990 we weren’t doing representational learning (like with deep neural networks – we didn’t have enough data or compute power) but the equations are the same we just put in features extracted with our computer vision algorithms that were suggested by human radiologists – then after trained when we put in a new mammogram we could extract which features dominated the decision to call something cancer or normal

 

We’ve recently in deep neural networks done similar things in our captioning work to decide what aspects of an image or video a particular linguistic expression is evoked from – for example in a dog chasing Frisbee picture we can back project to find where in the image are the pixels that evoked the word Frisbee – this has cracked the black box somewhat also

 

So both of these suggest to me this news article is just stating what we know (although in general a black box these deep systems can provide us some aspects of their ‘meaning’ that we can understand – this will be a focus of the new start at DARPA xAI – for explainable AI) but again I will review the technical article and if there is more there I will provide an addendum to this email

 

We now have the technical article – I don’t think our response above is far off except for the approach is based on Taylor expansion versus our approach – the ideas are the same and the importance of the problem is good – in a very important way they extend our sensitivity analysis as a special case of their more general Taylor approach:

Pattern Recognition 65 (2017) 211–222

Explaining nonlinear classification decisions with deep Taylor

decomposition

Grégoire Montavona,⁎, Sebastian Lapuschkinb, Alexander Binderc, Wojciech Samekb,⁎,

Klaus-Robert Müllera,d,⁎⁎

a Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Marchstr. 23, Berlin 10587, Germany

b Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, Germany

c Information Systems Technology & Design, Singapore University of Technology and Design, 8 Somapah Road, Building 1, Level 5, 487372, Singapore

d Department of Brain & Cognitive Engineering, Korea University, Anam-dong 5ga, Seongbuk-gu, Seoul 136-713, South Korea

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.

 

With respect to applications of deep systems:

Another thread was tied to my prior life in using AI/ML for medical detection / diagnosis – thus the article below:

Detecting Cancer Metastases on
Gigapixel Pathology Images
Yun Liu1?, Krishna Gadepalli1, Mohammad Norouzi1, George E. Dahl1,
Timo Kohlberger1, Aleksey Boyko1, Subhashini Venugopalan2??,
Aleksei Timofeev2, Philip Q. Nelson2, Greg S. Corrado1, Jason D. Hipp3,
Lily Peng1, and Martin C. Stumpe1
fliuyun,mnorouzi,gdahl,lhpeng,mstumpeg@google.com
1Google Brain, 2Google Inc, 3Verily Life Sciences,
Mountain View, CA, USA

  • Each year, the treatment decisions for more than 230; 000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast.
  • Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone.
  • We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels.
  • Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task.
  • At 8 false positives per image, we detect 92:4% of the tumors, relative to 82:7% by the previous best automated approach.
  • For comparison, a human pathologist attempting exhaustive search achieved 73:2% sensitivity.
  • We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.
  • In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal.
  • Our approach could considerably reduce false negative rates in metastasis detection.

 

As another application:

on March 9, 2017http://science.sciencemag.org/ Downloaded from

 

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík,1,2* Martin Schmid,1,2* Neil Burch,1 Viliam Lisý,1,3 Dustin Morrill,1 Nolan Bard,1 Trevor Davis,1 Kevin Waugh,1 Michael Johanson,1 Michael Bowling1

 

  • Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold’em. The approach is theoretically sound and is shown to produce more difficult to exploit strategies than prior approaches.

 

One of our main topics of multiple agents with objective functions commonality – from our sensing and AFIT mathematics dendrite:

How do we know that your “red” looks ** this is a quale statement – what you perceive in your consciousness is what I perceive **  the same as my “red”? For all we know, your “red” looks like my “blue.” In fact, for all we know your “red” looks nothing like any of my colors at all! If colors are just internal labels  ** labels here is not meant to imply the word – it is meant to describe the representation internal in a vocabulary of conscious thought ** , then as long as everything gets labeled, why should your brain and my brain use the same labels?  ** as long as we can align – why would they have to be the same – keep in mind the Kaku view – philosophers waste our time with these thought problems – he speaks to the ‘what is life concern that has disappeared’ – BUT OUR INTEREST IN OBJECTIVE FUNCTIONS THAT START WITH SIMILAR MACHINERY WHAT CAN I SAY ABOUT THE RESULTING REPRESENTATION – CAN I MAKE A STATEMENT ON WHAT CHAREACTERISTICS OF YOUR RED MY RED HAS TO HAVE? – IN THE RED CASE I WOULD CONTEND THAT THE RELATIONSHIPS BETWEEN YOUR RED AND YOUR BLUE HAVE TO BE REPLICATED – AND BY THE TIME I CONSTRAIN THE RELATIONSHIPS WITH SO MANY QUALIA IT CONSTRAINS THE MEANING (probably should say the comprehension – how qualia are related and how they can interact) TO BE THE SAME WHERE THE MEANING IS THE CHANGES TO THE REPRESENTATION RESULTING FROM THE STIMULI  – **

*** CAP POSITS:  THE MEANING / understanding / comprehension THE WAY WE DEFINE IN QUEST OF YOUR RED IS THE SAME AS THE MEANING OF MY RED ** ?understanding and maybe comprehension versus meaning – jared **really your representation / resulting meaning / understanding / resulting comprehension (relationships and ways the situations can interact – Representation is how agent structured knowledge meaning understanding and comprehension – recall we defined comprehension when we were defining qualia / situations – as something that can be comprehended as a whole – comprehended was defined as being able to discern how it is related to or how it can interact with other situations –

 

A 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.

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

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***

 

as I thought about this – and thought about the word doc – is your red my red – and my posit – that the meaning you generate for red is the same as what I generate for my red – your comment on I need to use understanding versus meaning – putting all this together I was forced to own up to what I need to stay ‘alignable’ – we don’t have to have the same exact changes to the representation (use our deep learning metaphor – I don’t care if all the activations are the same) – but what I have to maintain between agents for ‘red’ to be alignable is that the relationships to other situations is maintained between the respective agents – when I went down this path it reminded me of how we defined situations – and how we had to clear up the word comprehension – I’m happy to change the word comprehension to understanding – for now I have restricted comprehension to be understanding associated with the task of relationships / interactions with other situations

 

Another thread has continued to advance this week and related to objective function ‘red’ thread is the with interactions between our Airmen sensors autonomy team and our AFIT autonomy team – with the focus on ‘chat-bots’ – the idea that the future is all about these ‘AI bots’ versus apps – and that QuEST chat-bots might provide an avenue where knowledge of the developing representations that capture aspects of consciousness are key to solving the very tough problem of bots that accomplish the type of meaning-making required for many applications – and might be the key to bot-to-bot communication without have to strictly manually define a communication protocol.

Part of the interest of this thread is multi-modal communications – that is the reason the material below was inserted into the thread:

Snap Makes a Bet on the Cultural Supremacy of the Camera

  • https://www.nytimes.com/2017/03/08/technology/snap-makes-a-bet-on-the-cultural-supremacy-of-the-camera.html?_r=0
  • The rising dependence on cameras is changing the way we communicate. Credit Doug Chayka
  • If you’re watching Snap’s stock ticker, stop. The company that makes Snapchat, the popularphoto-messaging app, has been having a volatile few days after its rocket-fueled initial public offering last week.
  • But Snap’s success or failure isn’t going to be determined this week or even this year. This is a company that’s betting on a long-term trend: the rise and eventual global dominance of visual culture.
  • Snap calls itself a camera company. That’s a bit cute, considering that it only just released an actual camera, the Spectacles sunglasses, late last year. Snap will probably build other kinds of cameras, including potentially a drone.
  • But it’s best to take Snap’s camera company claim seriously, not literally. Snap does not necessarily mean that its primary business will be selling a bunch of camera hardware. It’s not going to turn into Nikon, Polaroid or GoPro. Instead it’s hit on something deeper and more important. Through both its hardware and software, Snap wants to enable the cultural supremacy of the camera, to make it at least as important to our daily lives as the keyboard.  ** profound point – camera / visual media as important as keyboard in communicating with other agents people and machines **

 

arXiv:1605.07736v2 [cs.LG] 31 Oct 2016

29th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain

 

Learning Multiagent Communication
with Backpropagation
Sainbayar Sukhbaatar
Dept. of Computer Science
Courant Institute, New York University
sainbar@cs.nyu.edu
Arthur Szlam
Facebook AI Research
New York
aszlam@fb.com
Rob Fergus
Facebook AI Research
New York
robfergus@fb.com

  • Many tasks in AI require the collaboration of multiple agents. Typically, the

communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

 

 

 

We are always interested in new information on the neuro-physiology that might provide guidance in our engineering endeavors:

 

Dynamics of cortical dendritic membrane potential and spikes in freely behaving rats
Jason J. Moore,1,2* Pascal M. Ravassard,1,3 David Ho,1,2 Lavanya Acharya,1,4 Ashley L. Kees,1,2 Cliff Vuong,1,3 Mayank R. Mehta1,2,3,5

 

  • Neural activity in vivo is primarily measured using extracellular somatic spikes, which provide limited information about neural computation. Hence, it is necessary to record from neuronal dendrites, which generate dendritic action potentials (DAP) and profoundly influence neural computation and plasticity. We measured neocortical sub- and suprathreshold dendritic membrane potential (DMP) from putative distal-most dendrites using tetrodes in freely behaving rats over multiple days with a high degree of stability and sub-millisecond temporal resolution. DAP firing rates were several fold larger than somatic rates. DAP rates were modulated by subthreshold DMP fluctuations which were far larger than DAP amplitude, indicting hybrid, analog-digital coding in the dendrites. Parietal DAP and DMP exhibited egocentric spatial maps comparable to pyramidal neurons. These results have important implications for neural coding and plasticity.news summary (45)
Categories: Uncategorized

Weekly QuEST Discussion Topics and News, 10 Mar

QuEST 10 March 2017

There were several interesting email conversation threads going on this week:

We want to start this week by talking about Kaku’s book on the future of Mind – this thread was initiated by Eric B from our Rome dendrite of the QuEST neuron assembly:

The Future of the Mind

Book by Michio Kaku

 

The Future of the Mind: The Scientific Quest to Understand, Enhance, and Empower the Mind

 

[he lists three levels of consciousness and highlights many discussions on the subject, such as that of a split brain actions]

https://cosmosmagazine.com/social-sciences/mind-michio-kaku

Kaku outlines levels of consciousness that correspond to different degrees of complexity, from the simplest things like plants at Level 0 to we humans on Level III. The big difference with us is that we are self-aware. “Human consciousness is a specific form of consciousness that creates a model of the world and then simulates it in time,” he writes in the book. “This requires mediating and evaluating many feedback loops to make a decision to achieve a goal.”

The mind of Michio Kaku

What is a physicist doing weighing in on the mysteries of the mind? Tim Dean went to find out.

 

 

There are two great mysteries that overshadow all other mysteries in science – the origin of the universe and what sits on your shoulders, says Michio Kaku.

Mark Von Holden/WireImage/Getty images

 

Michio Kaku has an extraordinary mind. It loves nothing better than occupying itself untangling the mathematics of subatomic strings vibrating in 11 dimensions. “My wife always thinks something strange is happening because I stare out the window for hours,” he says. *** I walk my dog ** “That’s all I’m doing. I’m playing with equations in my head.” In his day job, Kaku works at the very fringes of physics. He made a name for himself as co-founder of string field theory, which seeks to complete Einstein’s unfinished business by unifying all the fundamental forces of the universe into a single grand equation. He regales the public with tales of multiverses, hyperspace and visions of a better future built by science.

Hyperbole is part of his style. He once urged humanity to consider escaping our universe, which several billions of years from now will be in its last throes, by slipping through a wormhole. Hardly a pressing concern for today, but such proclamations lasso attention and get people to think big.

Kaku certainly thinks big in his latest book, and there’s plenty of hyperbole. But The Future of the Mind is somewhat of a surprise. What is a theoretical physicist doing writing a book about the mind – a topic usually reserved for neuroscientists, psychologists and philosophers? As a philosopher myself I was curious to see if a physicist could shed some photons on the problem. I took the opportunity when he was in Sydney spruiking his new book to find out.

Comments from our colleague Mike Y on this thread:

A couple of comments…  There is a difference between consciousness and intelligence.  In principle, we can build machines (or zombies) with extreme intelligence.  But that does not make them conscious.  Consciousness, your subjective experience, depends upon the neuronal architecture of your brain and nervous system.  This determines what sensations, perceptions, and cognitions you can experience. (and what behavioral goals you will have).

 

I agree completely that the human neuronal architecture enables humans to represent limited, specific, aspects of the world and to “run” simulations with them.

 

I also agree that all animals developed sensory systems to help them in their evolutionary niche.  For elephants that is a sensory system the processes low intensity sounds that travel a long way through the earth.  For humans that is high resolution color vision that enables us to identify the state of fruit and other foods sources, and to accurately judge the social/emotional state of conspecifics.  And for dogs (and apparently rhinos) a smell based spatial representation of the world.

 

Then we want to hit the article – this thread was initiated by Trevor and Todd from our Sensing dendrite:

Why does deep and cheap learning work so well?
Henry W. Lin and Max Tegmark
Dept. of Physics, Harvard University, Cambridge, MA 02138 and
Dept. of Physics & MIT Kavli Institute, Massachusetts Institute of Technology, Cambridge, MA 02139

arXiv:1608.08225v2 [cond-mat.dis-nn] 28 Sep 2016

  • We show how the success of deep learning depends not only on mathematics but also on physics: although well-knownmathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through “cheap learning” with exponentially fewer parameters than generic ones, because they have simplifying properties tracing back to the laws of physics.
  • The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability, and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings.
  • We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one.
  • We formalize these claims using information theory and discuss the relation to renormalization group procedures. We prove various “no-flattening theorems” showing when such efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss: flattening even linear functions can be costly, and flattening polynomials is exponentially expensive; we use group theoretic techniques to show that n variables cannot be multiplied using fewer than 2^n neurons in a single hidden layer.

 

On our topic of multiple agents with objective functions commonality – from our sensing and AFIT mathematics dendrite:

n  Seems like the below makes the argument of an objective function that might impact all of our perception of ‘red’ – if you will an objective function for color perception:  

To remind you of our interest:

This statement is completely consistent with our view of qualia.

Is there a reasonable argument that if two agents get exposed to the same set of

stimuli and have the same objective function that you can make some statement on the relationships

of their resulting representations? 

n   

n  Mark Changizi, Ph.D. Neuroscientist, Author of ‘Harnessed’ & ‘Vision Revolution’ 

n  How do we know that your “red” looks the same as my “red”? For all we know, your “red” looks like my “blue.” In fact, for all we know your “red” looks nothing like any of my colors at all! If colors are just internal labels, then as long as everything gets labeled, why should your brain and my brain use the same labels?

n  Richard Dawkins wrote a nice little piece on color, and along the way he asked these questions.

n  He also noted that not only can color labels differ in your and my brain, but perhaps the same color labels could be used in non-visual modalities of other animals. Bats, he notes, use audition for their spatial sense, and perhaps furry moths are heard as red, and leathery locusts as blue. Similarly, rhinoceroses may use olfaction for their spatial sense, and could perceive water as orange and rival male markings as gray.

n  … see next slide

n  The entirety of these links is, I submit, what determines the qualitative feel of the colors we see. If you and I largely share the same “perceptual network,” then we’ll have the same qualia. And if some other animal perceives some three-dimensional color space that differs radically in how it links to the other aspects of its mental life, then it won’t be like our color space… its perceptions will be an orange of a different color.

n  In fact, in my research I have provided evidence that our primate variety color vision evolved for seeing the color changes occurring on our faces and other naked spots. Our primate color vision is peculiar in its cone sensitivities (with the M and L cones having sensitivities that are uncomfortably close), but these peculiar cone sensitivities are just right for sensing the peculiar spectral modulations hemoglobin in the skin undergoes as the blood varies in oxygenation. Also, the naked-faced and naked-rumped primates are the ones with color vision; those primates without color vision have your typical mammalian furry face.

n  In essence, I have argued elsewhere that our color-vision eyes are oximeters like those found in hospital rooms, giving us the power to read off the emotions, moods and health of those around us.

n  On this new view of the origins of color vision, color is far from an arbitrary permutable labeling system. Our three-dimensional color space is steeped with links to emotions, moods and physiological states, as well as potentially to behaviors. For example, purple regions within color space are not merely a perceptual mix of blue and red, but are also steeped in physiological, emotional and behavioral implications — in this case, perhaps of a livid male ready to punch you.

n  http://www.huffingtonpost.com/mark-changizi-phd/perceiving-colors-differently_b_988244.html

a

 

http://www.newyorker.com/magazine/2017/02/27/why-facts-dont-change-our-minds?mbid=social_facebook

Why Facts Don’t Change Our Minds

New discoveries about the human mind show the limitations of reason.

 

From Adam a related thought on objective functions:  What I liked, that I thought was unique but also in agreement somewhat with the polyvagal theory I’ve been working through

 

If reason is designed to generate sound judgments, then it’s hard to conceive of a more serious design flaw than confirmation bias. Imagine, Mercier and Sperber suggest, a mouse that thinks the way we do. Such a mouse, “bent on confirming its belief that there are no cats around,” would soon be dinner. To the extent that confirmation bias leads people to dismiss evidence of new or underappreciated threats—the human equivalent of the cat around the corner—it’s a trait that should have been selected against. The fact that both we and it survive, Mercier and Sperber argue, proves that it must have some adaptive function, and that function, they maintain, is related to our “hypersociability.”

Mercier and Sperber prefer the term “myside bias.” Humans, they point out, aren’t randomly credulous. Presented with someone else’s argument, we’re quite adept at spotting the weaknesses. Almost invariably, the positions we’re blind about are our own.

 

10/02/2011 05:21 am ET | Updated Dec 02, 2011

Another thread has advanced this week with interactions between our Airmen sensors autonomy team and our AFIT autonomy team – with the focus on ‘chat-bots’ – the idea that the future is all about these ‘AI bots’ versus apps – and that QuEST chat-bots might provide an avenue where knowledge of the developing representations that capture aspects of consciousness are key to solving the very tough problem of bots that accomplish the type of meaning-making required for many applications

 

In another email thread this week initiated by our colleague Morley from our senior leader dendrite:

http://www.sciencemag.org/news/2017/03/brainlike-computers-are-black-box-scientists-are-finally-peering-inside?utm_campaign=news_daily_2017-03-07&et_rid=54802259&et_cid=1203472

Brainlike computers are a black box. Scientists are finally peering inside

By Jackie SnowMar. 7, 2017 , 3:15 PM

Last month, Facebook announced software that could simply look at a photo and tell, for example, whether it was a picture of a cat or a dog. A related program identifies cancerous skin lesions as well as trained dermatologists can. Both technologies are based on neural networks, sophisticated computer algorithms at the cutting edge of artificial intelligence (AI)—but even their developers aren’t sure exactly how they work. Now, researchers have found a way to “look” at neural networks in action and see how they draw conclusions.

Neural networks, also called neural nets, are loosely based on the brain’s use of layers of neurons working together. Like the human brain, they aren’t hard-wired to produce a specific result—they “learn” on training sets of data, making and reinforcing connections between multiple inputs. A neural net might have a layer of neurons that look at pixels and a layer that looks at edges, like the outline of a person against a background. After being trained on thousands or millions of data points, a neural network algorithm will come up with its own rules on how to process new data. But it’s unclear what the algorithm is using from those data to come to its conclusions.

“Neural nets are fascinating mathematical models,” says Wojciech Samek, a researcher at Fraunhofer Institute for Telecommunications at the Heinrich Hertz Institute in Berlin. “They outperform classical methods in many fields, but are often used in a black box manner.”

In an attempt to unlock this black box, Samek and his colleagues created software that can go through such networks backward in order to see where a certain decision was made, and how strongly this decision influenced the results.Their method, which they will describe this month at the Centre of Office Automation and Information Technology and Telecommunication conference in Hanover, Germany, enables researchers to measure how much individual inputs, like pixels of an image, contribute to the overall conclusion. Pixels and areas are then given a numerical score for their importance. With that information, researchers can create visualizations that impose a mask over the image. The mask is most bright where the pixels are important and darkest in regions that have little or no effect on the neural net’s output.

For example, the software was used on two neural nets trained to recognize horses. One neural net was using the body shape to determine whether it was horse. The other, however, was looking at copyright symbols on the images that were associated with horse association websites.

This work could improve neural networks, Samek suggests. That includes helping reduce the amount of data needed, one of the biggest problems in AI development, by focusing in on what the neural nets need. It could also help investigate errors when they occur in results, like misclassifying objects in an image.

Other researchers are working on similar processes to look into how algorithms make decisions, including neural nets for visuals as well as text. Continued research is important as algorithms make more decisions in our daily lives, says Sara Watson, a technology critic with the Berkman Klein Center for Internet & Society at Harvard University. The public needs tools to be able to understand how AI makes decisions. Algorithms, far from being perfect arbitrators of truth, are only as good as the data they’re given, she notes.

In a notorious neural network mess up, Google tagged a black woman as a gorilla in its photos application. Even more serious discrimination has been called into question in software that provides risk scores that some courts use to determine whether a criminal is likely to reoffend, with at least one study showing black defendants are given a higher risk score than white defendants for similar crimes. “It comes down to the importance of making machines, and the entities that employ them, accountable for their outputs,” Watson says

 

Not attempting to be dismissive but:

Cathy is pulling the technical article – but from the text in the news article this appears to be a rehash of something we invented in 1990:

 

  • Ruck, D. W., Rogers, S., Kabrisky, M., “Feature Selection Using a Multilayer Perceptron”, Journal of Neural Network Computing, Vol 2 (2), pp 40-48, Fall 1990.

 

When you use a supervised learning system with a mean squared error objective function and differentiable nonlinear neurons – then you can solve the partial differential equations to extract ‘saliency’ – that is you can work through any decision and rank order the inputs to decide an ‘order’ to their impact – in 1990 we weren’t doing representational learning (like with deep neural networks – we didn’t have enough data or compute power) but the equations are the same we just put in features extracted with our computer vision algorithms that were suggested by human radiologists – then after trained when we put in a new mammogram we could extract which features dominated the decision to call something cancer or normal

 

We’ve recently in deep neural networks done similar things in our captioning work to decide what aspects of an image or video a particular linguistic expression is evoked from – for example in a dog chasing Frisbee picture we can back project to find where in the image are the pixels that evoked the word Frisbee – this has cracked the black box somewhat also

 

So both of these suggest to me this news article is just stating what we know (although in general a black box these deep systems can provide us some aspects of their ‘meaning’ that we can understand – this will be a focus of the new start at DARPA xAI – for explainable AI) but again I will review the technical article and if there is more there I will provide an addendum to this email

 

We now have the technical article – I don’t think our response above is far off except for the approach is based on Taylor expansion versus our approach – the ideas are the same and the importance of the problem is good – in a very important way they extend our sensitivity analysis as a special case of their more general Taylor approach:

Pattern Recognition 65 (2017) 211–222

Explaining nonlinear classification decisions with deep Taylor

decomposition

Grégoire Montavona,⁎, Sebastian Lapuschkinb, Alexander Binderc, Wojciech Samekb,⁎,

Klaus-Robert Müllera,d,⁎⁎

a Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Marchstr. 23, Berlin 10587, Germany

b Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, Germany

c Information Systems Technology & Design, Singapore University of Technology and Design, 8 Somapah Road, Building 1, Level 5, 487372, Singapore

d Department of Brain & Cognitive Engineering, Korea University, Anam-dong 5ga, Seongbuk-gu, Seoul 136-713, South Korea

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.

news summary (44)

Categories: Uncategorized

Weekly QuEST Discussion Topics and News, 3 Mar

QuEST 3 March 2017

We need to continue our interactions on the idea of embodied cognition for example for issues we face in cyberspace (also there was a thread of discussion on the implications of the no free lunch theorem and its application to supervised learning and its implications for cyber defense that we might want to discuss).

.. paper on machine learning by David Wolpert extends directly to cyber defense interest. Theorem 2 on page 12 proves that “all heuristics that people have come up with for supervised learning … fail as often as they succeed” when they try to extend the knowledge obtained from a training set onto an unknown off-training-set future (examples on page 13).

 

..many efforts … violate Wolpert’s theorem by attempting to project a known past onto an unknown future. Not only are …these … approaches as likely to fail as to succeed, …

 

 

Different person on the thread: The “No Free Lunch” (NFL) Theorems of Wolpert and Macready have no place in any practical discussion of algorithms. The NFL Theorems state that, on average, no algorithm can perform equally well on all problems. As it is fair to say that every algorithm .. is tailored to the problem at hand, the NFL Theorems are irrelevant in the real world.

In their 1997 paper, Wolpert and Macready themselves describe their theorems:

“Intuitively, the NFL theorem illustrates that if knowledge of [the problem], perhaps specified through [the problem’s probability distribution], is not incorporated into [the algorithm], then there are no formal assurances that [the algorithm] will be effective. Rather, in this case effective optimization relies on a fortuitous matching between [the problem] and [the algorithm].

An excellent paper by Ho and Pepyne (“Simple Explanation of the No Free Lunch Theorem of Optimization,” 2001) states the same result this way:

“The No Free Lunch Theorem of Optimization (NFL T) is an impossibility theorem telling us that a general-purpose universal optimization strategy is impossible, and the only way one strategy can outperform another is if it is specialized to the structure of the specific problem under consideration.”

There are more detailed arguments, but I think the words of the authors themselves (Wolpert and Macready) refute any applicability of the NFL Theorems to any real-world algorithmic development, whether it is for machine learning, cyber defense, or any other problem domain.

 

Another person on the thread: ….  Basically, that statement says (independent of the Cyberspace application domain): If there is ZERO overlap between the training distribution and the test distribution than there should be little to no expectation that the ML algorithm should perform well (barring any side information).  In fact it should be expected to perform as well as random.

However, in practice, it is often the case that there is some level of overlap between data distributions or side-information available (i.e. both malware classes operate on an information system, made up of code, etc.).  The challenge in ML then is to develop appropriate models that are able to capture and exploit this overlap (however small) and generalize to different but overlapping domains (however small).  This challenge is one of the focus areas of ML.

I think it is true that providing provable guarantees on performance may not be possible in the general learning setting.  But that is OK, if the heuristics and developed models improve the state-of-the-practice.  However, I do think it is possible to provide guarantees as a function of the data distribution and overlap.  This type of work shows up in statistical machine learning relating to domain adaptation – where performance is bounded by the domain overlap.

Wolpert and Macready are very careful in defining the problem and applicability.  In fact follow-on papers they show where the NFL does not apply to ML cases:

1) http://www.cs.cmu.edu/~matts/Research/mstreeter_gecco_2003.pdf

2) https://ipvs.informatik.uni-stuttgart.de/mlr/papers/04-igel-NFL.pdf

3) https://pdfs.semanticscholar.org/7022/541216c2f6f894781bcd55a4cb545dbf81e9.pdf

 

 

Cap insertion into the thread – this is where if anyone has insights I should amend to my inserts I would be thrilled to hear them / discuss them:

Great discussion – attached sorry for the size is a chapter that documents the plenary talk I gave at the World congress on Computational Intelligence in 2001 – in it we prove a version of the NFL (no free lunch theorem) associated with intelligence amplification versus AI – the idea is in most of the interesting applications there are humans that consume/use the machine learning recommendation thus intelligence amplification versus replacing the human as in artificial intelligence –

The statements above are completely correct as usual … – we (my opinion) cannot abandon machine learning but have to use it with clear understanding of its limitations– using AI/ML is never a bad idea unless those using it do not understand the implications of how it was developed with respect to its capabilities and limitations

Just a reminder of the thread on embodied cognition and cyber:  The idea that instead of envisioning a central ‘brain’ for generating the understanding of the cyber environment a model along the lines of the Octopus might be more appropriate.  I only bring this back up to see if there are additional ideas that people came up with we need to consider.  We were also reminded of the discussions we’ve had on ‘split-brain’ people – people who have had their corpus callosum severed surgically so we want to bring that into the discussion.

This idea of embodied cognition is also relevant to our interest in organizations like ISIS using the web/social media to extend their impact.  A brief review of data analysis of that activity in a recent work will also be presented.

https://www.technologyreview.com/s/603626/data-mining-reveals-the-rise-of-isis-propaganda-on-twitter/?set=603629

  • Data Mining Reveals the Rise of ISIS Propaganda on Twitter
  • Twitter has closed 25,000 accounts that supported the terrorist organization ISIS.
  • An analysis of these tweets shows how ISIS emerged with a message of extreme violence.

  • One factor behind this rapid rise to power was ISIS’s use of social media, and Twitter in particular, to spread its ideas. And that raises some interesting questions: what have ISIS members and sympathizers been talking about on Twitter?  And why did that message prove so infectious?
  • Today we get an answer thanks to the work of Adam Badawy and Emilio Ferrara at the University of Southern California in Los Angeles.
  • These guys have analyzed some two million messages posted on Twitter by 25,000 members of ISIS. They say their analysis reveals important insights into the way the radical militant groups use social media and why its message spread so rapidly.

This is all part of our continuing discussion on representation – we’ve suggested this is the key to autonomy – to have agents with a representation or set of representations to facilitate deliberation / decision making for robustness and for a common framework when part of a human-machine joint cognitive solution.

Representation:  how the agent structures what it knows about the world – so for example its knowledge (what it uses to generate meaning of an observable) 

Reasoning:  how the agent can change its representation – the manipulation of the representation for example for the generation of meaning

Understanding:  application of relevant parts of the representation to complete a task – the meaning generated by an agent relevant to accomplishing a task in the estimation of an evaluating agent

Can a better representation be the missing link for autonomy – instead of the representation being generated by optimizing an objective function tied to MSE on classification – imagine the objective function tied to stability, consistency and usefulness –

Mean square error for classification as the objective function does not necessarily generate a representation that replicates the characteristics we consider critical for consciousness – we believe the constraints used by nature are

Stability

Consistency

Usefulness

We think we can achieve the first by using streaming video and enforcing the features extracted to not be bouncing / varying too much (assuming stabilized video) – unsupervised learning

We think we can achieve the second by doing video of similar locations and reward consistency – supervised learning

We think effectors / interactions with the environment – we have to close the loop around the representation to achieve the last – reinforcement learning

With respect to our interest in representation and objective functions we’ve asked the question recently and there has been some back channel conversation on if multiple agents use a related objective function and related machinery for acquisition of knowledge what can we say about their respective representations – so we want to have the opportunity to answer questions along these lines – the idea is to do fusion or multiple agent decision making will require some shared framework and we want to wrap our minds around the alternatives.

One of our main goals was to design a joint cognitive social media system focused on ‘mindfulness’ and thus a ‘context aware feed’ that provides some value –  how can QuEST agents facilitate the human getting into the ‘zone’ – the illusory apparent slowdown in time – we conjecture that the conscious perception of time is associated with the efficient ‘chunking’ of experiences

This topic came up this week in the QuEST news we posted – for example note specifically the last sentence in the excerpt below:

https://www.theregister.co.uk/2017/02/22/facebook_ai_fail/

Facebook scales back AI flagship after chatbots hit 70% f-AI-lure rate

‘The limitations of automation’

So it begins.

Facebook has scaled back its ambitions and refocused its application of “artificial intelligence” after its AI bots hit a 70 per cent failure rate. Facebook unveiled a bot API for its Messenger IM service at its developer conference last April. Facebook CEO Mark Zuckerberg had high hopes.

TenCent’s WeChat was the model. Although WeChat began life as an instant-messaging client, it rapidly evolved into a major platform for e-commerce and transactions in China. But it largely keeps any AI guesswork away from real users.  *** see second article below **

With Facebook’s bot API, Zuckerberg had joined a “chatbot arms race” with Microsoft CEO Satya Nadella. For Nadella, chatbots were “Conversations as a Platform,” or even the “third run-time” – as important to humanity as the operating system or the web browser.

Some experts fretted that if China opened up a lead in AI, the West would be doomed to lose World War 3. Others suggested that whichever superpower lost the AI arms race would relapse into a state of primitive technology feudalism.

However, as we reminded you recently, the reality of “artificial intelligence” is far from impressive, once it’s made to perform outside carefully stage managed and narrow demos. As stage one, Facebook’s AI would parse the conversation and insert relevant external links into Messenger conversations. So how has the experiment fared?

In tests, Silicon Valley blog The Information reports, the technology “could fulfil only about 30 per cent of requests without human agents.” And that wasn’t the only problem. “The bots built by outside developers had issues: the technology to understand human requests wasn’t developed enough. Usage was disappointing,” we’re told. Now it’s simply trying to make sense of the conversation. ** always the hole – meaning making **

There’s even a phrase you won’t have seen in many mainstream thinkpieces about AI, predicting a near future of clever algorithms taking middle-class jobs. Brace yourselves, dear readers. Facebook engineers will now focus on “training [Messenger] based on a narrower set of cases so users aren’t disappointed by the limitations of automation.”

Ah.

“Their discussions are much more grounded in reality now compared to last year,” said another person close to the Messenger developers. “The team in there now is finding ways to activate commercial intent inside Messenger. It’s much less about, ‘We’ll dominate the world with AI.'”

Analyst Richard Windsor describes Facebook as “the laggard in AI,” failing to match the results Google. “The problems that it has had with fake news, idiotic bots and Facebook M, all support my view that when Facebook tries to automate its systems, things always go wrong. The problem is not that Facebook does not have the right people but simply that it has not been working on artificial intelligence for nearly long enough,” he wrote recently.

In its exclusive, The Information also notes that Facebook has been grappling with what we call the “Clippy The Paperclip problem”: the user views the contribution by the agent, or bot, as intrusive.

———-  exactly where we are focused!

This reminds us of the prior discussions we’ve had on chunking – so we want to review the work of Cowan:

The magical number 4 in short-term
memory: A reconsideration
of mental storage capacity V3
Nelson Cowan

  • BEHAVIORAL AND BRAIN SCIENCES (2000) 24, 87–185
  • Abstract: Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described.

 

– thus a QuEST ‘wingman’ agent that helps the human formulate, recognize and exploit chunks would provide the insights to better respond to what may seem without it to be an overwhelming set of stimuli and evoke the zone illusion – thus our comment – a conscious recommender system facilitates the human decision maker getting into the zone

As part of the discussion on representation cap will present information from a recent article that we’ve not made it to yet:

Brain-Computer Interface-Based
Communication in the Completely Locked-In
State
Ujwal Chaudhary

Chaudhary U, Xia B, Silvoni S, Cohen LG,

Birbaumer N (2017) Brain±Computer Interface±

Based Communication in the Completely Locked-In

State. PLoS Biol 15(1): e1002593. doi:10.1371/

journal.pbio.1002593

  • Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS).
  • Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure.
  • Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)Ðtwo of them in permanent CLIS and two entering the CLIS without reliable means ofcommunicationÐlearned to answer personal questions with known answers andopen questions all requiring a “yes” or “no” thought using frontocentral oxygenation changes measured with fNIRS.
  • Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions.
  • Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%.
  • Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. ** EEG not work **
  • However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.

There was a related thread between some of us on ‘pain’ –

  • “Pain” – describes the unpleasant sensory and emotional experience associated with actual or potential tissue damage.
  • Includes – pricking, burning, aching, stinging and soreness
  • Assume serves important protective function
  • Some children born with insensitive to pain – severe injuries go unnoticed –
  • Some differences with other qualia – sense of urgency associated with it a sort of primitive quality associated with it – both affective and emotional components
  • Perception of pain influenced by many factors – identical stimuli in same agent can produce different ‘pain’ –
  • Anecdotes of wounded soldiers not feeling pain – until removed from battle field – injured athletes not experience the pain until after the competition
  • THERE IS NO ‘PAINFUL’ STIMULI – a stimuli that will produce the quale of pain in every agent independent of operating conditions
  • PAIN as in all qualia is NOT a direct expression of the sensory event – it is a product of elaborate processing

Some may have seen the news this week:

  • Forget the drugs, the answer to back pain may be Tai chi, massage

http://www.usatoday.com/story/news/nation-now/2017/02/14/forget-drugs-answer-back-pain-may-tai-chi-massage/97887446/

news-summary-43

Categories: Uncategorized

Weekly QuEST Discussion Topics and News, 17 Feb

February 16, 2017 Leave a comment

QuEST 17 Feb 2017

Lots of topics pass through the team this week.  We had a great interaction on the idea of embodied cognition in cyberspace.  We will start this week with a digest of the idea that instead of envisioning a central ‘brain’ for generating the understanding of the cyber environment a model along the lines of the Octopus might be more appropriate.  This idea of embodied cognition is also relevant to our interest in organizations like ISIS using the web/social media to extend their impact.  A brief review of data analysis of that activity in a recent work will also be presented.  We were also reminded of the discussions we’ve had on ‘split-brain’ people – people who have had their corpus callosum severed surgically so we want to bring that into the discussion.

This is all part of our continuing discussion on representation – we’ve suggested this is the key to autonomy – to have agents with a representation or set of representations to facilitate deliberation / decision making for robustness and for a common framework when part of a human-machine joint cognitive solution.

Representation:  how the agent structures what it knows about the world – so for example its knowledge (what it uses to generate meaning of an observable) 

Reasoning:  how the agent can change its representation – the manipulation of the representation for example for the generation of meaning

Understanding:  application of relevant parts of the representation to complete a task – the meaning generated by an agent relevant to accomplishing a task

Can a better representation be the missing link for autonomy – instead of the representation being generated by optimizing an objective function tied to MSE on classification – imagine the objective function tied to stability, consistency and usefulness – if one of our main goals was to design a joint cognitive social media system focused on ‘mindfulness’ and thus a ‘context aware feed’ that provides some value –  how can QuEST agents facilitate the human getting into the ‘zone’ – the illusory apparent slowdown in time – we conjecture that the conscious perception of time is associated with the efficient ‘chunking’ of experiences

This reminds us of the prior discussions we’ve had on chunking – so we want to review the work of Cowan:

The magical number 4 in short-term
memory: A reconsideration
of mental storage capacity V3
Nelson Cowan

  • BEHAVIORAL AND BRAIN SCIENCES (2000) 24, 87–185
  • Abstract: Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described.

 

– thus a QuEST ‘wingman’ agent that helps the human formulate, recognize and exploit chunks would provide the insights to better respond to what may seem without it to be an overwhelming set of stimuli and evoke the zone illusion – thus our comment – a conscious recommender system facilitates the human decision maker getting into the zone

As part of the discussion on representation cap will present information from a recent article that we’ve not made it to yet:

Brain-Computer Interface-Based
Communication in the Completely Locked-In
State
Ujwal Chaudhary

Chaudhary U, Xia B, Silvoni S, Cohen LG,

Birbaumer N (2017) Brain±Computer Interface±

Based Communication in the Completely Locked-In

State. PLoS Biol 15(1): e1002593. doi:10.1371/

journal.pbio.1002593

  • Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS).
  • Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure.
  • Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)Ðtwo of them in permanent CLIS and two entering the CLIS without reliable means ofcommunicationÐlearned to answer personal questions with known answers andopen questions all requiring a ªyesº or ªnoº thought using frontocentral oxygenation changes measured with fNIRS.
  • Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions.
  • Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%.
  • Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. ** EEG not work **
  • However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.

There was a related thread between some of us on ‘pain’ –

  • “Pain” – describes the unpleasant sensory and emotional experience associated with actual or potential tissue damage.
  • Includes – pricking, burning, aching, stinging and soreness
  • Assume serves important protective function
  • Some children born with insensitive to pain – severe injuries go unnoticed –
  • Some differences with other qualia – sense of urgency associated with it a sort of primitive quality associated with it – both affective and emotional components
  • Perception of pain influenced by many factors – identical stimuli in same agent can produce different ‘pain’ –
  • Anecdotes of wounded soldiers not feeling pain – until removed from battle field – injured athletes not experience the pain until after the competition
  • THERE IS NO ‘PAINFUL’ STIMULI – a stimuli that will produce the quale of pain in every agent independent of operating conditions
  • PAIN as in all qualia is NOT a direct expression of the sensory event – it is a product of elaborate processing

Some may have seen the news this week:

  • Forget the drugs, the answer to back pain may be Tai chi, massage

http://www.usatoday.com/story/news/nation-now/2017/02/14/forget-drugs-answer-back-pain-may-tai-chi-massage/97887446/

news-summary-42

Categories: Uncategorized

Weekly QuEST Discussion Topics and News, 10 Feb

February 9, 2017 Leave a comment

QuEST 10 Feb 2017

Will probably not discuss it (really down in the weeds) but will post on the site an article / presentation we’ve been banging on this week on reinforcement learning.

LEARNING TO REINFORCEMENT LEARN
JX Wang  arXiv:1611.05763v3 [cs.LG] 23 Jan 2017

The goal is to attack the task flexibility issue and the large onerous amount of data issue for RL:

  • In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown thatrecurrent networks can support meta-learning in a fully supervised context.
  • We extend this approach to the RL setting. What emerges is a system that istrained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.

We will start the open discussion this week by discussing topics that the group suggest should be included in an upcoming presentation by Cap – “Artificial Intelligence and Machine Learning – Where are we?  How did we get here?  Where do we need to go?” – Cap will present a strawman version to get comments on flow and content and opinions on the ‘big rocks’ that should be included.

Next we need to discuss representation – we’ve suggested this is the key to autonomy – to have agents with a representation or set of representations to facilitate deliberation / decision making for robustness and for a common framework when part of a human-machine joint cognitive solution.

Representation:  how the agent structures what it knows about the world – so for example its knowledge (what it uses to generate meaning of an observable) 

Reasoning:  how the agent can change its representation – the manipulation of the representation for example for the generation of meaning

Understanding:  application of relevant parts of the representation to complete a task – the meaning generated by an agent relevant to accomplishing a task

This is all part of our continuing discussion of the Kabrisky lecture – What is QueST? – can a better representation be the missing link for recommender systems – instead of the representation being generated by optimizing an objective function tied to MSE on classification – imagine the objective function tied to stability, consistency and usefulness – this may be an approach to lead to  systems systems ‘appreciate’ the information in the data or the context (meaning) of the human’s environment / thoughts and current focus – thus they become an approach to overcome the  ‘feed’ – the human is sucking on the firehose data feed – social media example – but people can’t seem to disconnect (they don’t have the will power to disconnect) – if we design a joint cognitive social media system focused on ‘mindfulness’ and thus a ‘context aware feed’ that provides some value –  how can QuEST agents facilitate the human getting into the ‘zone’ – the illusory apparent slowdown in time – we conjecture that the conscious perception of time is associated with the efficient ‘chunking’ of experiences – thus a QuEST ‘wingman’ agent that helps the human formulate, recognize and exploit chunks would provide the insights to better respond to what may seem without it to be an overwhelming set of stimuli and evoke the zone illusion – thus our comment – a conscious recommender system facilitates the human decision maker getting into the zone

As part of the discussion on representation cap will present information from a recent article:

Brain-Computer Interface-Based
Communication in the Completely Locked-In
State
Ujwal Chaudhary

Chaudhary U, Xia B, Silvoni S, Cohen LG,

Birbaumer N (2017) Brain±Computer Interface±

Based Communication in the Completely Locked-In

State. PLoS Biol 15(1): e1002593. doi:10.1371/

journal.pbio.1002593

  • Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS).
  • Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure.
  • Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)Ðtwo of them in permanent CLIS and two entering the CLIS without reliable means ofcommunicationÐlearned to answer personal questions with known answers andopen questions all requiring a ªyesº or ªnoº thought using frontocentral oxygenation changes measured with fNIRS.
  • Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions.
  • Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%.
  • Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a ªyesº or ªnoº response. ** EEG not work **
  • However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS

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communicating-with-locked-in-patients

journal-pbio-1002593

 

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Weekly QuEST Discussion Topics and News, 3 Feb.

February 2, 2017 Leave a comment

QuEST 3 Feb 2017

We will start this week with discussing two articles from our colleague Teresa H – just a note – I really appreciate when our colleagues send us these sorts of links – if you come across something we should discussion send it along and as always anyone can present material – the first article is on the octopus  (article is related to our ongoing focus on embodied cognition) and a second article is on ‘Blindsight’ related to our ongoing discussion on subconscious and conscious processing/representation:

 

The Mind of an Octopus Eight smart limbs plus a big brain add up to a weird and wondrous kind of intelligence – Sci American Mind – Jan 2017:

  • Octopuses and their kin (cuttlefish and squid) stand apart from other invertebrates, having evolved with much larger nervous systems and greater cognitive complexity.
  • The majority of neurons in an octopus are found in the arms, which can independently taste and touch and also control basic motions without input from the brain.
  • Octopus brains and vertebrate brains have no common anatomy but support a variety of similar features, including forms of short- and long-term memory, versions of sleep, and the capacities to recognize individual people and explore objects through play.

Amygdala Activation for Eye Contact Despite Complete Cortical Blindness
Nicolas Burra,2,3 Alexis Hervais-Adelman,3,4 Dirk Kerzel,2,3 Marco Tamietto,5,7 Beatrice de Gelder,5,6
and Alan J. Pegna1,2,3

The Journal of Neuroscience, June 19, 2013 • 33(25):10483–10489 • 10483

  • Cortical blindness refers to the loss of **conscious ** vision that occurs after destruction of the primary visual cortex. Although there is no sensory cortex and hence no conscious vision, some cortically blind patients show amygdala activation in response to facial or bodily expressions of emotion. Here we investigated whether direction of gaze could also be processed in the absence of any functional visual cortex.
  • A well-known patient with bilateral destruction of his visual cortex and subsequent cortical blindness was investigated in an fMRI paradigm during which blocks of faces were presented either with their gaze directed toward or away from the viewer.
  • Increased right amygdala activation was found in response to directed compared with averted gaze. Activity in this region was further found to be functionally connected to a larger network associated with face and gaze processing. The present study demonstrates that, in human subjects, the amygdala response to eye contact does not require an intact primary visual cortex.

We also then want to continue our discussion of the Kabrisky lecture – What is QueST? – specifically this week a recent thread of email discussions have focused on the missing link for recommender systems – they can’t ‘appreciate’ the information in the data or the context of the human’s environment / thoughts and current focus – thus they become a ‘feed’ – the human is sucking on the firehose feed – social media example – but people can’t seem to disconnect (they don’t have the will power to disconnect) – if we design a joint cognitive social media system focused on ‘mindfulness’ and thus a context aware ‘feed’ that provides some value –  how can QuEST agents facilitate the human getting into the ‘zone’ – the apparent slowdown in time – we conjecture that the conscious perception of time is associated with the efficient ‘chunking’ of experiences – thus a QuEST ‘wingman’ agent that helps the human recognize and exploit chunks would provide the insights to better respond to what may seem without it to be an overwhelming set of stimuli – thus our comment – a conscious recommender system facilitates the human decision maker getting into the zone

The other item still on the agenda is Cap has to give several talks coming up – we will post the FAQ on Autonomy, AI and Human machine teaming – Cap has also been asked to generate some material on historical perspectives in neural science and computational models associated with machine learning and artificial intelligence so we will have some discussion along those lines and once the material is cleared for posting we will post it also.  “Artificial Intelligence and Machine Learning:  Where are we?  How did we get here?  Where do we need to go?  Does that destination require ‘artificial consciousness’?”

Specifically – in one recent study cap presented at it was concluded that:

Operationally AI, it can be defined as those areas of R&D practiced by computer scientists who identify with one or more of the following academic sub-disciplines: Computer Vision, Natural Language Processing (NLP), Robotics (including Human-Robot Interactions), Search and Planning, Multi-agent Systems, Social Media Analysis (including Crowdsourcing), and Knowledge Representation and Reasoning (KRR).  In contradistinction to artificial general intelligence:

  • Artificial General Intelligence (AGI) is a research area within AI, small as measured by numbers of researchers or total funding, that seeks to build machines that can successfully perform any task that a human might do. Where AI is oriented around specific tasks, AGI seeks general cognitive abilities. On account of this ambitious goal, AGI has high visibility, disproportionate to its size or present level of success, among futurists, science fiction writers, and the public.

We will want to pull on these threads with respect to the breakthroughs in deep learning and the promise of other approaches to include unsupervised learning, reinforcement learning …

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PDF file for FAQ on Autonomy, Artificial Intelligence, and Human-machine teaming.

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