Xerox PARC Forum: AI and Robotics at an Inflection Point

Self-Aware Systems

On September 18, 2014 Steve Omohundro gave the Xerox PARC Forum on “AI and Robotics at an Inflection Point”. Here’s a PDF file of the slides.

AI and Robotics at an Inflection Point
PARC Forum

18 September 2014
5:00-6:30pm (5:00-6:00 presentation and Q&A, followed by networking until 6:30)
George E. Pake Auditorium, PARC

description

Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating “arms races” in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that…

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Mathematicians have finally figured out how to tell correlation from causation

Will the default mode network (DMN) be the functional explanation for continuity of identity in the brain?

Answer by Armando Vieira:

Very interesting. Any study on how to use this paradigm in Artificial Intelligence? This field is obsessed with seeing the brain as an engineering problem: input->representation->output which I think is fundamentally wrong.

Will the default mode network (DMN) be the functional explanation for continuity of identity in the brain?

Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.

Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism
Surya G. Nurzaman , Xiaoxiang Yu, Yongjae Kim and Fumiya Iida

Entropy 2014, 16(5), 2592-2610

Will deep learning make other Machine Learning algorithms obsolete?

Every once in a while a new algorithms comes and makes all others (in the same domain) seems kind of obsolete when it comes to the same domain.

Will deep learning make that related algorithms (backpropagation NN, GMM, HMM, …)?

Will deep learning make other Machine Learning algorithms obsolete?

The Complete Money Science Reloaded

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Photonic crystals increase solar efficiency

Here it is a Post about my original Technological & Scientific field of Study and Training: Photonics and Optoelectronics. One of most exciting applications in these fields concerns Solar Photovoltaic Cells, its role in Renewable Energy Solutions being crucial. This Video is from Sajeev John, a University Professor at the University of Toronto Canada:

 

 

 

His groundbreaking work in the field of light localization that enables light to be controlled at the microscopic level has earned him an international reputation. He is a pioneering theoretician in photonic band gap (PBG) materials. This new class of optical materials presents exciting possibilities in the fields of physics, chemistry, engineering and medicine. PBG materials could eventually be used for optical communications/information processing, clinical medicine, lighting and solar energy harvesting.

 

 

Quantum Symmetry

This is a reproduction of a post in a Facebook group that I happily belong to: Quantum Physics. The post was submitted by a friend of mine in that Social Media Network: Manel Rosa Martins.

Consequences of quantum symmetry

While it makes sense that symmetries could become exact when applied to very simple objects, the immediate intuition is that such a detail should not affect the physics of such objects in any significant way.

This is in part because it is very difficult to view the concept of exact similarity as physically meaningful. Our mental picture of such situations is invariably the same one we use for large objects: We picture objects or configurations that are very, very similar, but for which if we could “look closer” we would still be able to tell the difference.

However, the assumption that exact symmetries in very small objects should not make any difference in their physics was discovered in the early 1900s to be spectacularly incorrect. The situation was succinctly summarized by Richard Feynman in the direct transcripts of his Feynman Lectures on Physics, Volume III, Section 3.4, Identical particles. (Unfortunately, the quote was edited out of the printed version of the same lecture.)

“… if there is a physical situation in which it is impossible to tell which way it happened, it always interferes; it never fails.”

The word “interferes” in this context is a quick way of saying that such objects fall under the rules of quantum mechanics, in which they behave more like waves that interfere than like everyday large objects.
In short, when an object becomes so simple that a symmetry assertion of the form F(x) = x becomes an exact statement of experimentally verifiable sameness, x ceases to follow the rules of classical physics and must instead be modeled using the more complex, and often far less intuitive, rules of quantum physics.

This transition also provides an important insight into why the mathematics of symmetry are so deeply intertwined with those of quantum mechanics. When physical systems make the transition from symmetries that are approximate to ones that are exact, the mathematical expressions of those symmetries cease to be approximations and are transformed into precise definitions of the underlying nature of the objects.

From that point on, the correlation of such objects to their mathematical descriptions becomes so close that it is difficult to separate the two.

http://en.wikipedia.org/wiki/Symmetry#Quantum_objects”

 

If you please check the Wikipedia Link at the bottom of the post about Symmetry. Symmetry plays a crucial role in various fields of Knowledge, not least in Quantum Physics and Advanced Mathematics. Very good indeed.

Hello World: Welcome to Self-Organized Science & Tech

I knew you would say: that’s completely wrong…. And so I repent: here it is the correct version:

//C hello world example
#include <stdio.h>
 
int main()
{
  printf("Hello world\n");
  return 0;
}

 #include io

       libraries

       var=x

                  print

                             <Hello World>

         if var=x

            then   x= string

         else    Hello World

end