Group items tagged Computer - Artificial Intelligence Research

Recursive Self-Improvement - The Transhumanist Wiki - 2 views

transhumanistwiki.com/...Recursive_Self-Improvement

ai classic AI

shared by Matvey Ezhov on 20 Oct 09 - Cached

True Artificial Intelligence would bypass problems of biological complexity and ethics, growing up on a substrate ideal for initiating Recursive Self-Improvement. (fully reprogrammable, ultrafast, the AI's "natural habitat".) This Artificial Intelligence would be based upon: 1) our current understanding of the central algorithms of intelligence, 2) our current knowledge of the brain, obtained through high-resolution fMRI and delicate Cognitive Science experiments, and 3) the kind of computing hardware available to AI designers.
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Humans cannot conduct any of these enhancements to ourselves; the inherent structure of our biology and the limited level of our current technology makes this impossible.
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Recursive Self-Improvement is the ability of a mind to genuinely improve its own intelligence. This might be accomplished through a variety of means; speeding up one's own hardware, redesigning one's own cognitive architecture for optimal intelligence, adding new components into one's own hardware, custom-designing specialized modules for recurrent tasks, and so on.
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Unfortunately, the neurological structures corresponding to human intelligence are likely to be highly intricate, delicate, and biologically very complex (unnecessarily so; evolution exhibits no foresight, and most of the brain evolved in the absence of human General Intelligence).
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2) advances in Cognitive Science that indicate the complexity of certain brain areas is largely extraneous to intelligence,
- Matvey Ezhov on 20 Oct 09
  
  Очень серьезно допущение, которое может быть ошибочно. Нам известно, что все зоны кортекса участвуют в формировании модели мира индивида, а значит и сознания.
  
  <div class="cArrow"> </div><div class="cContentInner">Очень серьезно допущение, которое может быть ошибочно. Нам известно, что все зоны кортекса участвуют в формировании модели мира индивида, а значит и сознания.</div>
  
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PLoS Computational Biology: Qualia: The Geometry of Integrated Information - 1 views

www.ploscompbiol.org/...10.1371%2Fjournal.pcbi.1000462

qualia consciousness PLoS

shared by Matvey Ezhov on 23 Oct 09 - Cached

Matvey Ezhov on 23 Oct 09

According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale-a shape that completely and univocally characterizes the quality of a conscious experience. Φ- the height of this shape-is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating quali

<div class="cArrow"> </div><div class="cContentInner">According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale-a shape that completely and univocally characterizes the quality of a conscious experience. Φ- the height of this shape-is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating quali</div>

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You can control your Marilyn Monroe neuron - 2 views

www.physorg.com/news175417796.html

shared by Matvey Ezhov on 27 Oct 09 - Cached

Another experiment designed to test how well the subjects could control the single neurons was a fade experiment in which the subject was shown a combined image of two faces: Josh Brolin (star of Goonies) and Marilyn Monroe, and told to think of Josh Brolin. The electrodes sent data on the Josh Brolin and Marilyn Monroe neurons to the computer, which brightened the image of the one causing most neuron firing. As the subject thought of Brolin, the image of Monroe faded out.
- Matvey Ezhov on 27 Oct 09
  
  This points that grandmother's cells correlates for consciousness, right?
  
  <div class="cArrow"> </div><div class="cContentInner">This points that grandmother's cells correlates for consciousness, right?</div>
  
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Volucer Volucer on 27 Oct 09

с нейронами моей бабушки дело обстоит еще сложнее чем думалось

<div class="cArrow"> </div><div class="cContentInner">с нейронами моей бабушки дело обстоит еще сложнее чем думалось</div>

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Volucer Volucer on 14 Nov 09

nope. Nonconsciousness species can control it too

<div class="cArrow"> </div><div class="cContentInner">nope. Nonconsciousness species can control it too</div>

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Is this a unified theory of the brain? (Bayesian theory in New Scientist) - 1 views

reverendbayes.wordpress.com/...yesian-theory-in-new-scientist

bayesian theory of the brain brain neuroscience ai science neuroinformatics

shared by Matvey Ezhov on 27 Oct 09 - Cached

Neuroscientist Karl Friston and his colleagues have proposed a mathematical law that some are claiming is the nearest thing yet to a grand unified theory of the brain. From this single law, Friston’s group claims to be able to explain almost everything about our grey matter.
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Friston’s ideas build on an existing theory known as the “Bayesian brain”, which conceptualises the brain as a probability machine that constantly makes predictions about the world and then updates them based on what it senses.
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A crucial element of the approach is that the probabilities are based on experience, but they change when relevant new information, such as visual information about the object’s location, becomes available. “The brain is an inferential agent, optimising its models of what’s going on at this moment and in the future,” says Friston. In other words, the brain runs on Bayesian probability.
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“In short, everything that can change in the brain will change to suppress prediction errors, from the firing of neurons to the wiring between them, and from the movements of our eyes to the choices we make in daily life,” he says.
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Friston created a computer simulation of the cortex with layers of “neurons” passing signals back and forth. Signals going from higher to lower levels represent the brain’s internal predictions, while signals going the other way represent sensory input. As new information comes in, the higher neurons adjust their predictions according to Bayesian theory.
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Volunteers watched two sets of moving dots, which sometimes moved in synchrony and at others more randomly, to change the predictability of the stimulus. The patterns of brain activity matched Friston’s model of the visual cortex reasonably well.
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Friston’s results have earned praise for bringing together so many disparate strands of neuroscience. “It is quite certainly the most advanced conceptual framework regarding an application of these ideas to brain function in general,” says Wennekers. Marsel Mesulam, a cognitive neurologist from Northwestern University in Chicago, adds: “Friston’s work is pivotal. It resonates entirely with the sort of model that I would like to see emerge.”
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“The final equation you write on a T-shirt will be quite simple,” Friston predicts.
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There’s work still to be done, but for now Friston’s is the most promising approach we’ve got. “It will take time to spin off all of the consequences of the theory – but I take that property as a sure sign that this is a very important theory,” says Dehaene. “Most other models, including mine, are just models of one small aspect of the brain, very limited in their scope. This one falls much closer to a grand theory.”
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Quantum features of consciousness, computers and brain - 1 views

arxiv.org/0910.4300

наука ии consciousnes лженаука Quantum_brain

shared by Volucer Volucer on 02 Nov 09 - Cached

Volucer Volucer on 02 Nov 09

new article about Everett's ideas

<div class="cArrow"> </div><div class="cContentInner">new article about Everett's ideas</div>

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Matvey Ezhov on 02 Nov 09

Yup, I think quantum processes could be important on subcellular level, but we are interested in at least supracellular processes.

<div class="cArrow"> </div><div class="cContentInner">Yup, I think quantum processes could be important on subcellular level, but we are interested in at least supracellular processes.</div>

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Nikolay Sibirtsev on 03 Nov 09

хех, с тегом лженаука - даже не буду открывать )

<div class="cArrow"> </div><div class="cContentInner">хех, с тегом лженаука - даже не буду открывать )</div>

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IEEE Spectrum: IBM Unveils a New Brain Simulator - 2 views

spectrum.ieee.org/...-unveils-a-new-brain-simulator

ieee ai brain neuroscience neuroinformatics news

shared by Matvey Ezhov on 19 Nov 09 - Cached

The number of neurons and synapses in the simulation exceed those in a cat’s brain;
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Nikolay Sibirtsev on 20 Nov 09

Мда, немного демотивируют приведенные цифры о соотношении массы и потребляемой энергии. По их данным получается вычислительные мощности эквивалентные человеческому мозгу на 5 порядков больше потребляют энергии и на столько же больше весят.

<div class="cArrow"> </div><div class="cContentInner">Мда, немного демотивируют приведенные цифры о соотношении массы и потребляемой энергии. По их данным получается вычислительные мощности эквивалентные человеческому мозгу на 5 порядков больше потребляют энергии и на столько же больше весят.</div>

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Matvey Ezhov on 20 Nov 09

Как раз это демотивировать не должно - расчет ведется на современной архитектуре. Упоминавшейся в статье чип ДАРПАы будет потреблять намного меньше.

<div class="cArrow"> </div><div class="cContentInner">Как раз это демотивировать не должно - расчет ведется на современной архитектуре. Упоминавшейся в статье чип ДАРПАы будет потреблять намного меньше.</div>

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Researchers demonstrate a better way for computers to 'see' (w/ Video) - 0 views

www.physorg.com/news178997486.html

AI neuroscience science

shared by Volucer Volucer on 03 Dec 09 - Cached

Volucer Volucer on 03 Dec 09

evolutionary approach to create models of visual cortex

<div class="cArrow"> </div><div class="cContentInner">evolutionary approach to create models of visual cortex </div>

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Nikolay Sibirtsev on 04 Dec 09

Прикольно, но похоже это сводится к полному перебору, на что уйдет нереально много времени.

<div class="cArrow"> </div><div class="cContentInner">Прикольно, но похоже это сводится к полному перебору, на что уйдет нереально много времени.</div>

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cfp communication conference - 0 views

FICC 2018 aims to provide a forum for researchers from both academia and industry to share their latest research contributions and exchange knowledge with the common goal of shaping the future of I...

ai science intelligence artificial technolgy future-technology