- published: 12 Dec 2013
- views: 1348
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Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to
AGI.
They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences.
The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use
of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability
distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems.
We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction.
For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines.
The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010
http://agi-conf.org/2010/
Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious
scientific and engineering work aimed directly toward the goal of artificial general intelligence.
This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.
Filmed and Edited by Raj Dye
== Artificial General Intelligence ==
The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks.
In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)."
== AGI-10 ==
Science, Technology & the Future - By Design
http://scifuture.org
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.
Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one?
Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction.
Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior.
Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'.
Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories.
At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer.
The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be.
Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution.
Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem).
- published: 17 Mar 2017
- views: 88
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
- published: 26 Jul 2016
- views: 109
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
- published: 08 Feb 2012
- views: 614
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
- published: 18 Jul 2016
- views: 2849
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
- published: 06 Jul 2013
- views: 693
Ray Solomonoff
Ray Solomonoff (July 25, 1926 – December 7, 2009) was the inventor of algorithmic probability, his General Theory of Inductive Inference (also known as Universal Inductive Inference), and was a founder of algorithmic information theory. He was an originator of the branch of artificial intelligence based on machine learning, prediction and probability. He circulated the first report on non-semantic machine learning in 1956.
Solomonoff first described algorithmic probability in 1960, publishing the theorem that launched Kolmogorov complexity and algorithmic information theory. He first described these results at a Conference at Caltech in 1960, and in a report, Feb. 1960, "A Preliminary Report on a General Theory of Inductive Inference." He clarified these ideas more fully in his 1964 publications, "A Formal Theory of Inductive Inference," Part I and Part II.
Algorithmic probability is a mathematically formalized combination of Occam's razor, and the Principle of Multiple Explanations. It is a machine independent method of assigning a probability value to each hypothesis (algorithm/program) that explains a given observation, with the simplest hypothesis (the shortest program) having the highest probability and the increasingly complex hypotheses receiving increasingly small probabilities.
This page contains text from Wikipedia, the Free Encyclopedia -
https://wn.com/Ray_Solomonoff
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
Kolmogorov complexity
In algorithmic information theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of the shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as descriptive complexity, Kolmogorov–Chaitin complexity, algorithmic entropy, or program-size complexity. It is named after Andrey Kolmogorov, who first published on the subject in 1963.
For example, consider the following two strings of 32 lowercase letters and digits:
The first string has a short English-language description, namely "ab 16 times", which consists of 11 characters. The second one has no obvious simple description (using the same character set) other than writing down the string itself, which has 32 characters.
More formally, the complexity of a string is the length of the shortest possible description of the string in some fixed universal description language (the sensitivity of complexity relative to the choice of description language is discussed below). It can be shown that the Kolmogorov complexity of any string cannot be more than a few bytes larger than the length of the string itself. Strings, like the abab example above, whose Kolmogorov complexity is small relative to the string's size are not considered to be complex.
This page contains text from Wikipedia, the Free Encyclopedia -
https://wn.com/Kolmogorov_complexity
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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15:53Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGIRay Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to AGI. They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences. The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems. We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction. For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines. The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010 http://agi-conf.org/2010/ Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious scientific and engineering work aimed directly toward the goal of artificial general intelligence. This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal. Filmed and Edited by Raj Dye == Artificial General Intelligence == The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)." == AGI-10 == Science, Technology & the Future - By Design http://scifuture.org -
6:37What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction. In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated. Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one? Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction. Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior. Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'. Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories. At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer. The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program. These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be. Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution. Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem). -
4:21Tim Tyler - Solomonoff induction
Tim Tyler - Solomonoff induction -
20:05AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump SizeAGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/). Paper authors: Grace Solomonoff Abstract: These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this. -
25:56Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic ProbabilitySolomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November -
1:09:59Jeff Eldred Introduces Solomonoff Induction
Jeff Eldred Introduces Solomonoff InductionJeff Eldred Introduces Solomonoff Induction
Fermilab, April 10, 2014 -
4:24:22Стрим - Моя первая игровая приставка
Стрим - Моя первая игровая приставка -
1:05:12Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said. Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum. More information: https://www.copernicusfestival.com Photo in the thumbnail by Kamila Zarembska. Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę. -
59:04Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff ProbabilityAlexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred. Abstract. Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity. Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation -
1:09SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
-
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to AGI. They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences. The problems are solved by a "generate and t...
published: 12 Dec 2013 -
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction. In the mathematical formalism used, the observations have the form of finite binary strings, and the universal pri...
published: 17 Mar 2017 -
Tim Tyler - Solomonoff induction
Transcript: http://matchingpennies.com/solomonoff_induction/
published: 01 May 2011 -
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/). Paper authors: Grace Solomonoff Abstract: These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
published: 26 Jul 2016 -
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012 -
-
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
published: 27 May 2017 -
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said. Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer sc...
published: 18 Jul 2016 -
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred. Abstract. Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature ...
published: 06 Jul 2013 -
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
- Order: Reorder
- Duration: 15:53
- Updated: 12 Dec 2013
- views: 1348
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how the...
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to
AGI.
They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences.
The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use
of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability
distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems.
We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction.
For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines.
The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010
http://agi-conf.org/2010/
Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious
scientific and engineering work aimed directly toward the goal of artificial general intelligence.
This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.
Filmed and Edited by Raj Dye
== Artificial General Intelligence ==
The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks.
In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)."
== AGI-10 ==
Science, Technology & the Future - By Design
http://scifuture.org
https://wn.com/Ray_Solomonoff_Paper_Read_By_Marcus_Hutter_Algorithmic_Probability,_Heuristic_Programming_Agi
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to
AGI.
They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences.
The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use
of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability
distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems.
We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction.
For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines.
The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010
http://agi-conf.org/2010/
Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious
scientific and engineering work aimed directly toward the goal of artificial general intelligence.
This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.
Filmed and Edited by Raj Dye
== Artificial General Intelligence ==
The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks.
In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)."
== AGI-10 ==
Science, Technology & the Future - By Design
http://scifuture.org
- published: 12 Dec 2013
- views: 1348
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
- Order: Reorder
- Duration: 6:37
- Updated: 17 Mar 2017
- views: 88
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROB...
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.
Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one?
Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction.
Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior.
Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'.
Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories.
At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer.
The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be.
Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution.
Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem).
https://wn.com/What_Is_Algorithmic_Probability_What_Does_Algorithmic_Probability_Mean
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.
Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one?
Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction.
Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior.
Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'.
Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories.
At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer.
The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be.
Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution.
Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem).
- published: 17 Mar 2017
- views: 88
Tim Tyler - Solomonoff induction
- Order: Reorder
- Duration: 4:21
- Updated: 01 May 2011
- views: 731
Transcript: http://matchingpennies.com/solomonoff_induction/
Transcript: http://matchingpennies.com/solomonoff_induction/
https://wn.com/Tim_Tyler_Solomonoff_Induction
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Order: Reorder
- Duration: 20:05
- Updated: 26 Jul 2016
- views: 109
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-1...
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
https://wn.com/Agi_16_Grace_Solomonoff_A_Few_Notes_On_Multiple_Theories_And_Conceptual_Jump_Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
- published: 26 Jul 2016
- views: 109
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Order: Reorder
- Duration: 25:56
- Updated: 08 Feb 2012
- views: 614
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
- published: 08 Feb 2012
- views: 614
Jeff Eldred Introduces Solomonoff Induction
- Order: Reorder
- Duration: 1:09:59
- Updated: 11 Apr 2014
- views: 603
Fermilab, April 10, 2014
Fermilab, April 10, 2014
https://wn.com/Jeff_Eldred_Introduces_Solomonoff_Induction
Стрим - Моя первая игровая приставка
- Order: Reorder
- Duration: 4:24:22
- Updated: 27 May 2017
- views: 3163
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/c...
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ
Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
https://wn.com/Стрим_Моя_Первая_Игровая_Приставка
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Order: Reorder
- Duration: 1:05:12
- Updated: 18 Jul 2016
- views: 2849
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is...
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
https://wn.com/Beauty_In_Physics,_Mathematics_And_Biology,_Gregory_Chaitin_(Copernicus_Center_Lecture_2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
- published: 18 Jul 2016
- views: 2849
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Order: Reorder
- Duration: 59:04
- Updated: 06 Jul 2013
- views: 693
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI...
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
https://wn.com/Alexey_Potapov_Differences_Between_Kolmogorov_Complexity_And_Solomonoff_Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
- published: 06 Jul 2013
- views: 693
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
- Order: Reorder
- Duration: 1:09
- Updated: 28 Feb 2011
- views: 123
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution to Hal McGee's effort found at archive.org.
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution to Hal McGee's effort found at archive.org.
https://wn.com/Solomonoff_Hoffmannstahl_One_Minute_Elixirs.Mpg
-
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to AGI. They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences. The problems are solved by a "generate and t...
published: 12 Dec 2013 -
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction. In the mathematical formalism used, the observations have the form of finite binary strings, and the universal pri...
published: 17 Mar 2017 -
Tim Tyler - Solomonoff induction
Transcript: http://matchingpennies.com/solomonoff_induction/
published: 01 May 2011 -
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/). Paper authors: Grace Solomonoff Abstract: These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
published: 26 Jul 2016 -
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012 -
-
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
published: 27 May 2017 -
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said. Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer sc...
published: 18 Jul 2016 -
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred. Abstract. Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature ...
published: 06 Jul 2013 -
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
- Order: Reorder
- Duration: 15:53
- Updated: 12 Dec 2013
- views: 1348
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how the...
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to
AGI.
They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences.
The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use
of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability
distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems.
We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction.
For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines.
The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010
http://agi-conf.org/2010/
Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious
scientific and engineering work aimed directly toward the goal of artificial general intelligence.
This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.
Filmed and Edited by Raj Dye
== Artificial General Intelligence ==
The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks.
In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)."
== AGI-10 ==
Science, Technology & the Future - By Design
http://scifuture.org
https://wn.com/Ray_Solomonoff_Paper_Read_By_Marcus_Hutter_Algorithmic_Probability,_Heuristic_Programming_Agi
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is about Algorithmic Probability (ALP) and Heuristic Programming and how they can be combined to achieve AGI. It is an update of a 2003 report describing a system of this kind (Sol03). We first describe ALP, giving the most common implementation of it, then the features of ALP relevant to its application to
AGI.
They are: Completeness, Incomputability, Subjectivity and Diversity. We then show how these features enable us to create a very general, very intelligent problem solving machine. For this we will devise "Training Sequences" - sequences of problems designed to put problem-solving information into the machine. We describe a few kinds of training sequences.
The problems are solved by a "generate and test" algorithm, in which the candidate solutions are selected through a "Guiding Probability Distribution". The use
of Levin's search procedure enables us to e±ciently consider the full class of partial recursive functions as possible solutions to our problems. The guiding probability
distribution is updated after each problem is solved, so that the next problem can profit from things learned in the previously solved problems.
We describe a few updating techniques. Improvements in updating based on heuristic programming is one of the principal directions of current research. Designing training sequences is another important direction.
For some of the simpler updating algorithms, it is easy to "merge" the guiding probabilities of machines that have been educated using different training sequences | resulting in a machine that is more intelligent than any of the component machines.
The Third Conference on AGI - Lugano, Switzerland, March 5-8 (Fri-Mon) 2010
http://agi-conf.org/2010/
Continuing the mission of the first two AGI conferences (AGI-08, that was held at the University of Memphis; and AGI-09, that was held in Washington DC), in March 2010, AGI-10 will gather an international group of leading academic and industry researchers involved in serious
scientific and engineering work aimed directly toward the goal of artificial general intelligence.
This is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.
Filmed and Edited by Raj Dye
== Artificial General Intelligence ==
The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called "narrow AI" -- the production of AI systems displaying intelligence regarding specific, highly constrained tasks.
In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)."
== AGI-10 ==
Science, Technology & the Future - By Design
http://scifuture.org
- published: 12 Dec 2013
- views: 1348
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
- Order: Reorder
- Duration: 6:37
- Updated: 17 Mar 2017
- views: 88
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROB...
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.
Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one?
Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction.
Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior.
Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'.
Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories.
At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer.
The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be.
Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution.
Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem).
https://wn.com/What_Is_Algorithmic_Probability_What_Does_Algorithmic_Probability_Mean
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBABILITY meaning - ALGORITHMIC PROBABILITY definition - ALGORITHMIC PROBABILITY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In algorithmic information theory, algorithmic (Solomonoff) probability is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the prior obtained by this formula, in Bayes' rule for prediction.
In the mathematical formalism used, the observations have the form of finite binary strings, and the universal prior is a probability distribution over the set of finite binary strings. The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated.
Algorithmic probability deals with the questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? How can we predict future data and how can we measure the likelihood of that prediction being the right one?
Four principle inspirations for Solomonoff's algorithmic probability were: Occam's razor; Epicurus' principle of multiple explanations; modern computing theory (e.g. use of a universal Turing machine) and Bayes rule for prediction.
Occam's razor and Epicurus' principle are essentially two different nonmathematical approximations of the universal prior.
Occam's razor means 'among the theories that are consistent with the observed phenomena, one should select the simplest theory'.
Epicurus' principle of multiple explanations proposes that if more than one theory is consistent with the observations, keep all such theories.
At the heart of the universal prior is an abstract model of a computer, such as a universal Turing machine. Any abstract computer will do, as long as it is Turing-complete, i.e. every finite binary string has at least one program that will compute it on the abstract computer.
The abstract computer is used to give precise meaning to the phrase `simple explanation'. In the formalism used, explanations, or theories of phenomena are computer programs that generate observation strings when run on the abstract computer. A simple explanation is a short computer program; a complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
These ideas can be made specific and the probabilities used to construct a prior probability distribution for the given observation. Solomonoff's main reason for inventing this prior is so that it can be used in Bayes' rule when the actual prior is unknown, enabling prediction under uncertainty. It predicts the most likely continuation of that observation, and provides a measure of how likely this continuation will be.
Although the universal probability of an observation (and its extension) is incomputible, there is a computer algorithm, Levin Search, which, when run for longer and longer periods of time, will generate a sequence of approximations which converge to the Universal probability distribution.
Solomonoff proved this distribution to be machine-invariant within a constant factor (called the invariance theorem).
- published: 17 Mar 2017
- views: 88
Tim Tyler - Solomonoff induction
- Order: Reorder
- Duration: 4:21
- Updated: 01 May 2011
- views: 731
Transcript: http://matchingpennies.com/solomonoff_induction/
Transcript: http://matchingpennies.com/solomonoff_induction/
https://wn.com/Tim_Tyler_Solomonoff_Induction
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Order: Reorder
- Duration: 20:05
- Updated: 26 Jul 2016
- views: 109
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-1...
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
https://wn.com/Agi_16_Grace_Solomonoff_A_Few_Notes_On_Multiple_Theories_And_Conceptual_Jump_Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
- published: 26 Jul 2016
- views: 109
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Order: Reorder
- Duration: 25:56
- Updated: 08 Feb 2012
- views: 614
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
- published: 08 Feb 2012
- views: 614
Jeff Eldred Introduces Solomonoff Induction
- Order: Reorder
- Duration: 1:09:59
- Updated: 11 Apr 2014
- views: 603
Fermilab, April 10, 2014
Fermilab, April 10, 2014
https://wn.com/Jeff_Eldred_Introduces_Solomonoff_Induction
Стрим - Моя первая игровая приставка
- Order: Reorder
- Duration: 4:24:22
- Updated: 27 May 2017
- views: 3163
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/c...
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ
Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
https://wn.com/Стрим_Моя_Первая_Игровая_Приставка
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Order: Reorder
- Duration: 1:05:12
- Updated: 18 Jul 2016
- views: 2849
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is...
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
https://wn.com/Beauty_In_Physics,_Mathematics_And_Biology,_Gregory_Chaitin_(Copernicus_Center_Lecture_2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
- published: 18 Jul 2016
- views: 2849
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Order: Reorder
- Duration: 59:04
- Updated: 06 Jul 2013
- views: 693
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI...
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
https://wn.com/Alexey_Potapov_Differences_Between_Kolmogorov_Complexity_And_Solomonoff_Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
- published: 06 Jul 2013
- views: 693
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
- Order: Reorder
- Duration: 1:09
- Updated: 28 Feb 2011
- views: 123
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution to Hal McGee's effort found at archive.org.
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution to Hal McGee's effort found at archive.org.
https://wn.com/Solomonoff_Hoffmannstahl_One_Minute_Elixirs.Mpg
-
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/). Paper authors: Grace Solomonoff Abstract: These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
published: 26 Jul 2016 -
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012 -
-
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred. Abstract. Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature ...
published: 06 Jul 2013 -
Allen King Interview at Solomonoff Memorial
http://www.solomonoff85thmemorial.monash.edu.au/
published: 04 Mar 2013 -
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said. Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer sc...
published: 18 Jul 2016 -
Math Park - 11/01/2014 - Yann Ollivier, Mathématiques, raisonnements inductifs et...
Les problèmes de raisonnement inductif ou d'extrapolation comme « deviner la suite d'une série de nombres », ou plus généralement, « comprendre la structure cachée dans des observations », sont fondamentaux si l'on veut un jour construire une intelligence artificielle. On pourrait avoir l'impression que ces problèmes ne sont pas mathématiquement bien définis, or il existe une théorie mathématique rigoureuse du raisonnement inductif et de l'extrapolation, fondée sur des principes de théorie de la calculabilité. Cette théorie a été définie il y a 50 ans par Ray Solomonoff, mais on commence seulement à avoir des outils mathématiques pour l'appliquer en pratique, grâce à des techniques de probabilités, de compression de données, de géométrie différentielle, de théorie de l'information. On donn...
published: 05 Mar 2014 -
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 2: Context & Networks" - #245
Episode 245: Dr. Hans Utter returns to continue our investigational series into history of FM radio, titled “The [Secret] History of FM Radio, Part 2: Context & Networks, The Radical Left & Negative Intelligence”. Dr. Hans Utter returns for his 11th time. Episode 245 is about guns, self-defense, gun control laws, genocide, and freedom, with weapons expert Norman France, titled “Gun Control Vs. Reality. This episode was recorded January 27 and is being released on Sunday, January 31, 2016”. Chain-Links by Frigyes Karinthy https://djjr-courses.wdfiles.com/loca... Connectivity of Random Nets by Ray Solomonoff and Antol Rapoport http://world.std.com/~rjs/publication... Dr. Hans Utter's latest article: www.gnosticmedia.com/txtfiles/HansUtter_DoWhatThouWilt.pdf Previous episodes wi...
published: 05 Mar 2016 -
Gen Bilimci (Geneticist): Vakko Fashion Center
Monday, October 14, 2013 Wood Auditorium REX Principal Joshua Prince-Ramus, Project Manager David Menicovich, and On-Site Architect Ishtiaq Rafiuddin; Façade Consultant Marc Simmons, Front; and Interior Designer Sefer Çağlar, Autoban Responses by Merve Çağlar, SAHA Contemporary Art, and Galia Solomonoff, GSAPP An x-ray look at one building by key players in its realization. The Vakko Fashion Center, a 40,000 square-foot headquarters for the Turkish fashion house Vakko and its related media company, which was completed in 2012, began in Southern California, with REX's design for a new building at Caltech. A sudden change in the school's administration halted plans and freed them up for a new client in Istanbul, Vakko, which needed a new home within one year. In this unusual story, constr...
published: 17 Oct 2013 -
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
published: 27 May 2017
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Order: Reorder
- Duration: 20:05
- Updated: 26 Jul 2016
- views: 109
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-1...
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
https://wn.com/Agi_16_Grace_Solomonoff_A_Few_Notes_On_Multiple_Theories_And_Conceptual_Jump_Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump Size" at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/).
Paper authors: Grace Solomonoff
Abstract:
These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.
- published: 26 Jul 2016
- views: 109
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Order: Reorder
- Duration: 25:56
- Updated: 08 Feb 2012
- views: 614
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
- published: 08 Feb 2012
- views: 614
Jeff Eldred Introduces Solomonoff Induction
- Order: Reorder
- Duration: 1:09:59
- Updated: 11 Apr 2014
- views: 603
Fermilab, April 10, 2014
Fermilab, April 10, 2014
https://wn.com/Jeff_Eldred_Introduces_Solomonoff_Induction
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Order: Reorder
- Duration: 59:04
- Updated: 06 Jul 2013
- views: 693
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI...
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
https://wn.com/Alexey_Potapov_Differences_Between_Kolmogorov_Complexity_And_Solomonoff_Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI== (this video includes discussion afterwards - though the video is blurred.
Abstract.
Kolmogorov complexity and algorithmic probability are compared in the context of the universal algorithmic intelligence. Accuracy of time series prediction based on single best model and on averaging over multiple models is estimated. Connection between inductive behavior and multimodel prediction is established. Uncertainty as a heuristic for reducing the number of used models without losses of universality is discussed. The conclusion is made that plurality of models is the essential feature of artificial general intelligence, and this feature should not be removed without necessity.
Key words: Universal Agents, Kolmogorov Complexity, Algorithmic Probability, Prediction, Inductive Behavior, Uncertainty
Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_7.pdf
Presented by Alexey Potapov - other authors: Andrew Svitenkov and Yurii Vinogradov from AIDEUS, Russian Federation
- published: 06 Jul 2013
- views: 693
Allen King Interview at Solomonoff Memorial
- Order: Reorder
- Duration: 29:02
- Updated: 04 Mar 2013
- views: 137
http://www.solomonoff85thmemorial.monash.edu.au/
http://www.solomonoff85thmemorial.monash.edu.au/
https://wn.com/Allen_King_Interview_At_Solomonoff_Memorial
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Order: Reorder
- Duration: 1:05:12
- Updated: 18 Jul 2016
- views: 2849
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is...
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
https://wn.com/Beauty_In_Physics,_Mathematics_And_Biology,_Gregory_Chaitin_(Copernicus_Center_Lecture_2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty in physics, mathematics and biology," in which he tried to show what is the beauty in natural sciences. "The theory is beautiful when it easily explains complicated things" - he said.
Gregory John Chaitin is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as Kolmogorov (or Kolmogorov-Chaitin) complexity together with Andrei Kolmogorov and Ray Solomonoff. Today, algorithmic information theory is a common subject in any computer science curriculum.
More information:
https://www.copernicusfestival.com
Photo in the thumbnail by Kamila Zarembska.
Projekt „Copernicus Festival 2016: Piękno. Pasma naukowe: wykłady, debaty, Śniadania Mistrzów” finansowany w ramach umowy 625/P-DUN/2016 ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę.
- published: 18 Jul 2016
- views: 2849
Math Park - 11/01/2014 - Yann Ollivier, Mathématiques, raisonnements inductifs et...
- Order: Reorder
- Duration: 1:26:41
- Updated: 05 Mar 2014
- views: 6590
Les problèmes de raisonnement inductif ou d'extrapolation comme « deviner la suite d'une série de nombres », ou plus généralement, « comprendre la structure cac...
Les problèmes de raisonnement inductif ou d'extrapolation comme « deviner la suite d'une série de nombres », ou plus généralement, « comprendre la structure cachée dans des observations », sont fondamentaux si l'on veut un jour construire une intelligence artificielle. On pourrait avoir l'impression que ces problèmes ne sont pas mathématiquement bien définis, or il existe une théorie mathématique rigoureuse du raisonnement inductif et de l'extrapolation, fondée sur des principes de théorie de la calculabilité. Cette théorie a été définie il y a 50 ans par Ray Solomonoff, mais on commence seulement à avoir des outils mathématiques pour l'appliquer en pratique, grâce à des techniques de probabilités, de compression de données, de géométrie différentielle, de théorie de l'information. On donnera les premières propriétés mathématiques ainsi que quelques exemples inspirés de l'intelligence artificielle.
https://wn.com/Math_Park_11_01_2014_Yann_Ollivier,_Mathématiques,_Raisonnements_Inductifs_Et...
Les problèmes de raisonnement inductif ou d'extrapolation comme « deviner la suite d'une série de nombres », ou plus généralement, « comprendre la structure cachée dans des observations », sont fondamentaux si l'on veut un jour construire une intelligence artificielle. On pourrait avoir l'impression que ces problèmes ne sont pas mathématiquement bien définis, or il existe une théorie mathématique rigoureuse du raisonnement inductif et de l'extrapolation, fondée sur des principes de théorie de la calculabilité. Cette théorie a été définie il y a 50 ans par Ray Solomonoff, mais on commence seulement à avoir des outils mathématiques pour l'appliquer en pratique, grâce à des techniques de probabilités, de compression de données, de géométrie différentielle, de théorie de l'information. On donnera les premières propriétés mathématiques ainsi que quelques exemples inspirés de l'intelligence artificielle.
- published: 05 Mar 2014
- views: 6590
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 2: Context & Networks" - #245
- Order: Reorder
- Duration: 1:46:06
- Updated: 05 Mar 2016
- views: 8149
Episode 245: Dr. Hans Utter returns to continue our investigational series into history of FM radio, titled “The [Secret] History of FM Radio, Part 2: Context &...
Episode 245: Dr. Hans Utter returns to continue our investigational series into history of FM radio, titled “The [Secret] History of FM Radio, Part 2: Context & Networks, The Radical Left & Negative Intelligence”.
Dr. Hans Utter returns for his 11th time.
Episode 245 is about guns, self-defense, gun control laws, genocide, and freedom, with weapons expert Norman France, titled “Gun Control Vs. Reality. This episode was recorded January 27 and is being released on Sunday, January 31, 2016”.
Chain-Links by Frigyes Karinthy
https://djjr-courses.wdfiles.com/loca...
Connectivity of Random Nets by Ray Solomonoff and Antol Rapoport
http://world.std.com/~rjs/publication...
Dr. Hans Utter's latest article: www.gnosticmedia.com/txtfiles/HansUtter_DoWhatThouWilt.pdf
Previous episodes with Dr. Utter:
CCN – UnSpun 013 – Dr. Hans Utter – The Liquid Culture – with Jan Irvin & Joe Atwill
http://www.gnosticmedia.com/UNSPUN_013_DrHansUtter_LiquidCulture
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 1: The Musical Dream - It Became Your Life” – #243
http://www.gnosticmedia.com/HansUtter_HistoryFMRadio_243
Orson Welles’ The War of the Worlds: Prototype of Panic, with Dr. Hans Utter, #239
http://www.gnosticmedia.com/DrHansUtter_WaroftheWorlds_239
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 7 (Conclusion)” – #238
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt7_238
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 6” – #237
http://www.gnosticmedia.com/HansUtter_MusicMindControl_P62_237
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 5” – #236
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt5_236
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 4” – #235
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt4__235
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 3” – #234
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_234
Bonus audio to part 3:
http://www.gnosticmedia.com/podcast/GnosticMedia_PC_234_HansUtter_MusicMindControl_Pt3_BONUS_md.mp3
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 2” – #233
http://www.gnosticmedia.com/HansUtter_MusicMindControl_P2_233
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 1” – #232
http://www.gnosticmedia.com/dr-hans-utter-interview-music-mind-control-and-psychobiology-pt-1-232/
RBN Radio updates to this series:
RBN Gnostic Media Radio 062 – Dr. Hans Utter – Update 5 – The War of the Worlds – 9/22/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-062-dr-hans-utter-update-5-the-war-of-the-worlds-9222015/
RBN Gnostic Media Radio 045 – Dr. Hans Utter – Music and Mind Control – Update 4 – 8/28/2015
http://www.gnosticmedia.com/RBN_028
RBN Gnostic Media Radio 032 – Dr. Hans Utter – Music and Mind Control – Update 3 – 8/11/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-031-dr-hans-utter-music-and-mind-control-update-3-8112015/
RBN Gnostic Media Radio 022 – Dr. Hans Utter – Music and Mind Control – Update 2 – 7/28/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-022-dr-hans-utter-music-and-mind-control-update-2-7282015/
RBN Gnostic Media Radio 015 – Dr. Hans Utter – Music and Mind Control – Update – 7/17/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-015-dr-hans-utter-music-and-mind-control-update-7172015/
Hans Utter interview – “Ethnomusicology” – #099 (Members only)
http://www.gnosticmedia.com/hans-utter-interview-ethnomusicology-099/
Send Bitcoin donations for this episode to:
12EQTDMzU5mxtEd8ZfyrHrxABZi3jtaCCo
Please make other forms of donations here:
http://www.gnosticmedia.com/donate/
Send Bitcoin donations for this episode to:
12EQTDMzU5mxtEd8ZfyrHrxABZi3jtaCCo
Please make other forms of donations here:
http://www.gnosticmedia.com/donate/
Contributions. This episode is brought to you by:
Jose
Debi
Oscar
Casey
Gordon
Steve
Thomas
Randy
Marie
Steve x3
Patrick
John
Bill
Marty
Lyle
Derek
Hanan x2
Masaki
Stephen
Josh
Tyler
Susan
Hector
Matthew
Willem
Wendy
Barry
Adam
Louis
Joseph
Tresor
Marie x2
Heath
Scott
Barry
Carlos
Jeremy
Oscar
Research materials:
Marie
BTC:
Unknown
https://youtu.be/fn2KDu0meAw
https://wn.com/Dr._Hans_Utter_Interview_–_“The_Secret_History_Of_Fm_Radio,_Part_2_Context_Networks_245
Episode 245: Dr. Hans Utter returns to continue our investigational series into history of FM radio, titled “The [Secret] History of FM Radio, Part 2: Context & Networks, The Radical Left & Negative Intelligence”.
Dr. Hans Utter returns for his 11th time.
Episode 245 is about guns, self-defense, gun control laws, genocide, and freedom, with weapons expert Norman France, titled “Gun Control Vs. Reality. This episode was recorded January 27 and is being released on Sunday, January 31, 2016”.
Chain-Links by Frigyes Karinthy
https://djjr-courses.wdfiles.com/loca...
Connectivity of Random Nets by Ray Solomonoff and Antol Rapoport
http://world.std.com/~rjs/publication...
Dr. Hans Utter's latest article: www.gnosticmedia.com/txtfiles/HansUtter_DoWhatThouWilt.pdf
Previous episodes with Dr. Utter:
CCN – UnSpun 013 – Dr. Hans Utter – The Liquid Culture – with Jan Irvin & Joe Atwill
http://www.gnosticmedia.com/UNSPUN_013_DrHansUtter_LiquidCulture
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 1: The Musical Dream - It Became Your Life” – #243
http://www.gnosticmedia.com/HansUtter_HistoryFMRadio_243
Orson Welles’ The War of the Worlds: Prototype of Panic, with Dr. Hans Utter, #239
http://www.gnosticmedia.com/DrHansUtter_WaroftheWorlds_239
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 7 (Conclusion)” – #238
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt7_238
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 6” – #237
http://www.gnosticmedia.com/HansUtter_MusicMindControl_P62_237
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 5” – #236
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt5_236
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 4” – #235
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_pt4__235
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 3” – #234
http://www.gnosticmedia.com/DrHansUtterInterviewMusicMindControl_234
Bonus audio to part 3:
http://www.gnosticmedia.com/podcast/GnosticMedia_PC_234_HansUtter_MusicMindControl_Pt3_BONUS_md.mp3
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 2” – #233
http://www.gnosticmedia.com/HansUtter_MusicMindControl_P2_233
Dr. Hans Utter Interview – “Music, Mind Control, and Psychobiology, Pt. 1” – #232
http://www.gnosticmedia.com/dr-hans-utter-interview-music-mind-control-and-psychobiology-pt-1-232/
RBN Radio updates to this series:
RBN Gnostic Media Radio 062 – Dr. Hans Utter – Update 5 – The War of the Worlds – 9/22/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-062-dr-hans-utter-update-5-the-war-of-the-worlds-9222015/
RBN Gnostic Media Radio 045 – Dr. Hans Utter – Music and Mind Control – Update 4 – 8/28/2015
http://www.gnosticmedia.com/RBN_028
RBN Gnostic Media Radio 032 – Dr. Hans Utter – Music and Mind Control – Update 3 – 8/11/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-031-dr-hans-utter-music-and-mind-control-update-3-8112015/
RBN Gnostic Media Radio 022 – Dr. Hans Utter – Music and Mind Control – Update 2 – 7/28/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-022-dr-hans-utter-music-and-mind-control-update-2-7282015/
RBN Gnostic Media Radio 015 – Dr. Hans Utter – Music and Mind Control – Update – 7/17/2015
http://www.gnosticmedia.com/rbn-gnostic-media-radio-015-dr-hans-utter-music-and-mind-control-update-7172015/
Hans Utter interview – “Ethnomusicology” – #099 (Members only)
http://www.gnosticmedia.com/hans-utter-interview-ethnomusicology-099/
Send Bitcoin donations for this episode to:
12EQTDMzU5mxtEd8ZfyrHrxABZi3jtaCCo
Please make other forms of donations here:
http://www.gnosticmedia.com/donate/
Send Bitcoin donations for this episode to:
12EQTDMzU5mxtEd8ZfyrHrxABZi3jtaCCo
Please make other forms of donations here:
http://www.gnosticmedia.com/donate/
Contributions. This episode is brought to you by:
Jose
Debi
Oscar
Casey
Gordon
Steve
Thomas
Randy
Marie
Steve x3
Patrick
John
Bill
Marty
Lyle
Derek
Hanan x2
Masaki
Stephen
Josh
Tyler
Susan
Hector
Matthew
Willem
Wendy
Barry
Adam
Louis
Joseph
Tresor
Marie x2
Heath
Scott
Barry
Carlos
Jeremy
Oscar
Research materials:
Marie
BTC:
Unknown
https://youtu.be/fn2KDu0meAw
- published: 05 Mar 2016
- views: 8149
Gen Bilimci (Geneticist): Vakko Fashion Center
- Order: Reorder
- Duration: 1:39:17
- Updated: 17 Oct 2013
- views: 483
Monday, October 14, 2013
Wood Auditorium
REX Principal Joshua Prince-Ramus, Project Manager David Menicovich, and On-Site Architect Ishtiaq Rafiuddin; Façade C...
Monday, October 14, 2013
Wood Auditorium
REX Principal Joshua Prince-Ramus, Project Manager David Menicovich, and On-Site Architect Ishtiaq Rafiuddin; Façade Consultant Marc Simmons, Front; and Interior Designer Sefer Çağlar, Autoban
Responses by Merve Çağlar, SAHA Contemporary Art, and Galia Solomonoff, GSAPP
An x-ray look at one building by key players in its realization. The Vakko Fashion Center, a 40,000 square-foot headquarters for the Turkish fashion house Vakko and its related media company, which was completed in 2012, began in Southern California, with REX's design for a new building at Caltech. A sudden change in the school's administration halted plans and freed them up for a new client in Istanbul, Vakko, which needed a new home within one year. In this unusual story, construction began merely four days after REX received the commission, with plans quickly altered to include offices, showrooms, conference rooms, an auditorium, museum, and dining hall, as well as television studios, radio production facilities and a screening hall around the existing concrete skeleton of a former hotel—a striking building-within-a-building that exemplifies quick thinking and adaptive reuse.
Istanbul Week, October 13--19
Interdisciplinary conversation to launch the newest node in the Studio-X Global Network, Studio-X Istanbul, Meclis-i Mebusan Caddesi, No: 37 Salıpazarı, Beyoğlu, on November 8. Istanbul Week is co-organized by the Columbia Global Centers | Turkey.
http://events.gsapp.org/event/gen-bilimci-geneticist-vakko-fashion-center
https://wn.com/Gen_Bilimci_(Geneticist)_Vakko_Fashion_Center
Monday, October 14, 2013
Wood Auditorium
REX Principal Joshua Prince-Ramus, Project Manager David Menicovich, and On-Site Architect Ishtiaq Rafiuddin; Façade Consultant Marc Simmons, Front; and Interior Designer Sefer Çağlar, Autoban
Responses by Merve Çağlar, SAHA Contemporary Art, and Galia Solomonoff, GSAPP
An x-ray look at one building by key players in its realization. The Vakko Fashion Center, a 40,000 square-foot headquarters for the Turkish fashion house Vakko and its related media company, which was completed in 2012, began in Southern California, with REX's design for a new building at Caltech. A sudden change in the school's administration halted plans and freed them up for a new client in Istanbul, Vakko, which needed a new home within one year. In this unusual story, construction began merely four days after REX received the commission, with plans quickly altered to include offices, showrooms, conference rooms, an auditorium, museum, and dining hall, as well as television studios, radio production facilities and a screening hall around the existing concrete skeleton of a former hotel—a striking building-within-a-building that exemplifies quick thinking and adaptive reuse.
Istanbul Week, October 13--19
Interdisciplinary conversation to launch the newest node in the Studio-X Global Network, Studio-X Istanbul, Meclis-i Mebusan Caddesi, No: 37 Salıpazarı, Beyoğlu, on November 8. Istanbul Week is co-organized by the Columbia Global Centers | Turkey.
http://events.gsapp.org/event/gen-bilimci-geneticist-vakko-fashion-center
- published: 17 Oct 2013
- views: 483
Стрим - Моя первая игровая приставка
- Order: Reorder
- Duration: 4:24:22
- Updated: 27 May 2017
- views: 3163
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/c...
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/channel/UCxnD96rPnaAxSLx09AZ01Dg
Alex Diamond https://www.youtube.com/channel/UCz-jsYsxwiD3I-TJhq0hxGQ
Ссылка на второй канал с ПсихоСтримами https://goo.gl/vM32DI
https://wn.com/Стрим_Моя_Первая_Игровая_Приставка
- Playlist
- Chat
15:53
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is abo...
published: 12 Dec 2013
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
- Report rights infringement
- published: 12 Dec 2013
- views: 1348
6:37
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBA...
published: 17 Mar 2017
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
- Report rights infringement
- published: 17 Mar 2017
- views: 88
4:21
Tim Tyler - Solomonoff induction
Transcript: http://matchingpennies.com/solomonoff_induction/
published: 01 May 2011
Tim Tyler - Solomonoff induction
Tim Tyler - Solomonoff induction
- Report rights infringement
- published: 01 May 2011
- views: 731
20:05
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump S...
published: 26 Jul 2016
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Report rights infringement
- published: 26 Jul 2016
- views: 109
25:56
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Report rights infringement
- published: 08 Feb 2012
- views: 614
1:09:59
Jeff Eldred Introduces Solomonoff Induction
Fermilab, April 10, 2014
published: 11 Apr 2014
Jeff Eldred Introduces Solomonoff Induction
Jeff Eldred Introduces Solomonoff Induction
- Report rights infringement
- published: 11 Apr 2014
- views: 603
4:24:22
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/c...
published: 27 May 2017
Стрим - Моя первая игровая приставка
Стрим - Моя первая игровая приставка
- Report rights infringement
- published: 27 May 2017
- views: 3163
1:05:12
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty i...
published: 18 Jul 2016
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Report rights infringement
- published: 18 Jul 2016
- views: 2849
59:04
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between ...
published: 06 Jul 2013
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Report rights infringement
- published: 06 Jul 2013
- views: 693
1:09
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution t...
published: 28 Feb 2011
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
- Report rights infringement
- published: 28 Feb 2011
- views: 123
- Playlist
- Chat
15:53
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Paper: http://agi-conf.org/2010/wp-content/uploads/2009/06/paper_41.pdf
This paper is abo...
published: 12 Dec 2013
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
Ray Solomonoff paper read by Marcus Hutter - Algorithmic Probability, Heuristic Programming & AGI
- Report rights infringement
- published: 12 Dec 2013
- views: 1348
6:37
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean? ALGORITHMIC PROBA...
published: 17 Mar 2017
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
What is ALGORITHMIC PROBABILITY? What does ALGORITHMIC PROBABILITY mean?
- Report rights infringement
- published: 17 Mar 2017
- views: 88
4:21
Tim Tyler - Solomonoff induction
Transcript: http://matchingpennies.com/solomonoff_induction/
published: 01 May 2011
Tim Tyler - Solomonoff induction
Tim Tyler - Solomonoff induction
- Report rights infringement
- published: 01 May 2011
- views: 731
20:05
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump S...
published: 26 Jul 2016
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Report rights infringement
- published: 26 Jul 2016
- views: 109
25:56
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Report rights infringement
- published: 08 Feb 2012
- views: 614
1:09:59
Jeff Eldred Introduces Solomonoff Induction
Fermilab, April 10, 2014
published: 11 Apr 2014
Jeff Eldred Introduces Solomonoff Induction
Jeff Eldred Introduces Solomonoff Induction
- Report rights infringement
- published: 11 Apr 2014
- views: 603
4:24:22
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/c...
published: 27 May 2017
Стрим - Моя первая игровая приставка
Стрим - Моя первая игровая приставка
- Report rights infringement
- published: 27 May 2017
- views: 3163
1:05:12
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty i...
published: 18 Jul 2016
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Report rights infringement
- published: 18 Jul 2016
- views: 2849
59:04
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between ...
published: 06 Jul 2013
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Report rights infringement
- published: 06 Jul 2013
- views: 693
1:09
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
From Volume 6 of IEAAProject These artists hail from the USA. This is their contribution t...
published: 28 Feb 2011
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
SOLOMONOFF & HOFFMANNSTAHL - ONE MINUTE ELIXIRS.mpg
- Report rights infringement
- published: 28 Feb 2011
- views: 123
- Playlist
- Chat
20:05
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
Grace Solomonoff presents her talk "A Few Notes on Multiple Theories and Conceptual Jump S...
published: 26 Jul 2016
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
AGI-16 Grace Solomonoff - A Few Notes on Multiple Theories and Conceptual Jump Size
- Report rights infringement
- published: 26 Jul 2016
- views: 109
25:56
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Held at Monash Uni, Solomonoff Memorial Conference 2011 late November
published: 08 Feb 2012
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
Solomonoff Memorial 2011 - Eray Ozkural - Diverse Consequences of Algorithmic Probability
- Report rights infringement
- published: 08 Feb 2012
- views: 614
1:09:59
Jeff Eldred Introduces Solomonoff Induction
Fermilab, April 10, 2014
published: 11 Apr 2014
Jeff Eldred Introduces Solomonoff Induction
Jeff Eldred Introduces Solomonoff Induction
- Report rights infringement
- published: 11 Apr 2014
- views: 603
59:04
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Differences between ...
published: 06 Jul 2013
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
Alexey Potapov - Differences between Kolmogorov Complexity and Solomonoff Probability
- Report rights infringement
- published: 06 Jul 2013
- views: 693
29:02
Allen King Interview at Solomonoff Memorial
http://www.solomonoff85thmemorial.monash.edu.au/
published: 04 Mar 2013
Allen King Interview at Solomonoff Memorial
Allen King Interview at Solomonoff Memorial
- Report rights infringement
- published: 04 Mar 2013
- views: 137
1:05:12
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
During the 2016 Copernicus Festival Gregory Chaitin delivered a lecture entitled "Beauty i...
published: 18 Jul 2016
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
Beauty in physics, mathematics and biology, Gregory Chaitin (Copernicus Center Lecture 2016)
- Report rights infringement
- published: 18 Jul 2016
- views: 2849
1:26:41
Math Park - 11/01/2014 - Yann Ollivier, Mathématiques, raisonnements inductifs et...
Les problèmes de raisonnement inductif ou d'extrapolation comme « deviner la suite d'une s...
published: 05 Mar 2014
Math Park - 11/01/2014 - Yann Ollivier, Mathématiques, raisonnements inductifs et...
Math Park - 11/01/2014 - Yann Ollivier, Mathématiques, raisonnements inductifs et...
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- published: 05 Mar 2014
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1:46:06
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 2: Context & Networks" - #245
Episode 245: Dr. Hans Utter returns to continue our investigational series into history of...
published: 05 Mar 2016
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 2: Context & Networks" - #245
Dr. Hans Utter interview – “The [Secret] History of FM Radio, Part 2: Context & Networks" - #245
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- published: 05 Mar 2016
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1:39:17
Gen Bilimci (Geneticist): Vakko Fashion Center
Monday, October 14, 2013
Wood Auditorium
REX Principal Joshua Prince-Ramus, Project Manag...
published: 17 Oct 2013
Gen Bilimci (Geneticist): Vakko Fashion Center
Gen Bilimci (Geneticist): Vakko Fashion Center
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4:24:22
Стрим - Моя первая игровая приставка
http://www.donationalerts.ru/r/solomonoff
В гостях Weizel Sergej https://www.youtube.com/c...
published: 27 May 2017
Стрим - Моя первая игровая приставка
Стрим - Моя первая игровая приставка
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AGI-16 Grace Solomonoff - A Few Notes on Multiple ...
Solomonoff Memorial 2011 - Eray Ozkural - Diverse ...
Jeff Eldred Introduces Solomonoff Induction...
Alexey Potapov - Differences between Kolmogorov Co...
Allen King Interview at Solomonoff Memorial...
Beauty in physics, mathematics and biology, Gregor...
Math Park - 11/01/2014 - Yann Ollivier, Mathématiq...
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Teens Who Taunted, Filmed Drowning Man Won't Be Charged In His Death
Edit WorldNews.com 21 Jul 2017
At one point, one of the teen boys can be heard laughing, saying "he dead."The state of Florida currently does not have a law where a citizen is obligated to render aid or call for help for anyone in distress."If there was (a law like that) we would charge them," Cocoa Police Department spokeswoman Yvonne Martinez told CNN by phone."The family is frustrated ... No one deserves to go like that."...
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Mexico City Hit With Drug-War Level Violence and Gun Battle
Edit WorldNews.com 21 Jul 2017
Scenes that were once unthinkable for Mexico City of drastic gun fights and cartel violence are moving in toward the city as Yesterday had a shootout that brought 1,300 police and marines to battle suspects that were carrying assault rifles, according to The Associated Press ... "The narco-blockades come to Mexico City," the newspaper El Universal wrote in a front-page headline Friday ... -WN.com, Maureen Foody....
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Vladimir Putin: 'I may not leave Russian presidency'
Edit The Guardian 21 Jul 2017
A question-and-answer session at a Sochi school was designed to make Putin seem youth-friendly after youth demonstrations. Vladimir Putin. Vladimir Putin. 'I may not leave Russian presidency'. A question-and-answer session at a Sochi school was designed to make Putin seem youth-friendly after youth demonstrations. Asked what he plans to do when he leaves the presidency, Vladimir Putin paused and smiled ... Read more ... Twitter ... ....
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Texas Company Recalls Coffee With Viagra-Like Ingredient
Edit WorldNews.com 21 Jul 2017
A Texas coffee company is recalling one of its roasts because it was making some men a bit too excited, The Associated Press reports. Bestherbs Coffee LLC issued the voluntary recall for its New of Kopi Jantan Tradisional Natural Herbs Coffee after the U.S. Food and Drug Administration found it contains desmethyl carbodenafil, which is similar to the sildenafil used in the erectile dysfunction drug Viagra....
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Pentagon Study Declares American Empire Is 'Collapsing'
Edit Alternet 21 Jul 2017
Report demands massive expansion of military-industrial complex to maintain global "access to resources.". This article was produced in partnership with AlterNet and Insurge Intelligence. Learn more about Nafeez Ahmed and how to support his work ... The solution proposed to protect US power in this new “post-primacy” environment is, however, more of the same ... Collapse ... “In brief, the status quo that was hatched and nurtured by U.S ... To U.S....
Rays hit 3 HRs, but fall in extras to Texas
Edit MLB 22 Jul 2017
PETERSBURG -- Elvis Andrus is the bull that sees red whenever he plays against the Rays. Friday night, his RBI single in the 10th gave the Rangers a 4-3 win over the Rays at Tropicana Field. The Rays (51-46) have now lost two in a row while the Rangers (46-50) snapped a five-game losing streak ... Andrus entered the game hitting .571 against Rays pitching this season ... got the Rays going in the 10th when he singled to lead off the inning....
Rays expect Souza back in starting lineup soon
Edit MLB 22 Jul 2017
"He looked really good running [at Tropicana Field on Friday afternoon]," Rays manager Kevin Cash said ... Souza made a pinch-hit appearance in the 10th inning of the Rays' 4-3 loss, singling to left before being retired on a double-play grounder ... The Rays' center fielder went on the disabled list on June 9 and is eligible to be activated on Aug ... And the Rays have released right-hander Danny Farquhar from Triple-A Durham....
Choo ties it, Elvis wins it for Texas at The Trop
Edit MLB 22 Jul 2017
PETERSBURG -- Elvis Andrus is the bull that sees red whenever he plays against the Rays. Friday night, his RBI single in the 10th gave the Rangers a 4-3 win over the Rays at Tropicana Field. The Rays (51-46) have now lost two in a row while the Rangers (46-50) snapped a five-game losing streak ... Andrus entered the game hitting .571 against Rays pitching this season ... got the Rays going in the 10th when he singled to lead off the inning....
Rays expect Souza back over the weekend
Edit MLB 22 Jul 2017
"He looked really good running [at Tropicana Field on Friday afternoon]," Rays manager Kevin Cash said ... The Rays' center fielder went on the disabled list on June 9 and is eligible to be activated on Aug ... The Rays' shortstop doesn't like speculating about when he might return, but he allowed that they "should have a solid idea" about his return by the middle of August ... He hasn't been on the 40-man roster since the Rays DFA'd him on 29....
Andrus snaps 10th-inning tie, Rangers beat Rays 4-3
Edit This is Money 22 Jul 2017
(AP) - Elvis Andrus homered early, and then snapped a 10th-inning tie with a two-out infield single that gave the struggling Texas Rangers a 4-3 victory over the Tampa Bay Rays on Friday night ... Texas Rangers' Elvis Andrus hits an RBI-single off Tampa Bay Rays reliever Brad Boxberger during the 10th inning of a baseball game Friday, July 21, 2017, in St ... Rays....
Gulf Coast League
Edit Reflector 22 Jul 2017
At A Glance. All Times EDT. East Division. WLPct.GB. Nationals (Nationals)127.632—. Cardinals (Cardinals)118.5791. Astros (Astros)129.5711. Marlins (Marlins)911.4503½. Mets (Mets)514.2637. Northeast Division ... Rays (Rays)815.3486½ ... Orioles 9, Rays 0 ... Orioles at Rays, 10 a.m ... ....
Cobb deals into 9th, but done in by one mistake
Edit MLB 22 Jul 2017
But unfortunately for the Rays, the decision by Rays manager Kevin Cash to leave him in for the ninth backfired, as Cobb allowed the game-tying homer before the Rangers went on to a 4-3 win in extras in the opening game of a weekend series at Tropicana Field ... Not since Matt Andriese threw a complete-game, two-hit shutout against the Athletics on May 14, 2016, has a Rays pitcher gone the distance....
Darvish (12 K's) blows away Rays with heater
Edit MLB 22 Jul 2017
That adjustment paved the way for his aspirations of pitching upstairs to succeed Friday night in the Rangers' 4-3 win over the Rays ... Over eight innings, Darvish fanned 12 batters, the most for a Rangers pitcher since he struck out 12 Rays last September ... Watch all of Yu Darvish's 12 strikeouts against the Rays in 12 seconds ... The Rays are a strong ......
Andrus, Rangers beat Rays in 10
Edit Longview News Journal 22 Jul 2017
— Elvis Andrus homered early, and then snapped a 10th-inning tie with a two-out infield single that gave the struggling Texas Rangers a 4-3 victory over the Tampa Bay Rays on Friday night ... Rays starter ... The Rays built their lead on homers by Brad Miller, Corey Dickerson and Smith, whose eighth-inning shot made it 3-1....
Andrus leads Rangers past Rays in 10 innings
Edit TSN Canada 22 Jul 2017
— Elvis Andrus homered early, and then snapped a 10th-inning tie with a two-out infield single that gave the struggling Texas Rangers a 4-3 victory over the Tampa Bay Rays on Friday night ... Rays starter Alex ......
LEADING OFF: Trout vs Price; JD in desert; Wrigley rivalry
Edit Wtop 22 Jul 2017
___. ONE ON ONE ... WELCOME TO THE DESERT ... X-rays were negative and Martinez was available off the bench Friday night against the Nationals ... X-rays were negative, but Bryant is experiencing soreness and there is some concern about his ability to grip a bat ... Conforto hits 2 HRs, Blevins helps Mets hold off A's 7-5 MLB News D-backs open with back-to-back-to-back HRs MLB News Andrus snaps 10th-inning tie, Rangers beat Rays 4-3 MLB News. ....
Darvish strikes out 12 batters amid trade rumors
Edit AOL 22 Jul 2017
Texas Rangers ace Yu Darvish may be wearing a new uniform very soon but it was all business in MLB on Friday. Darvish struck out 12 batters while allowing three runs in eight innings in a 4-3, 10-inning win against the Tampa Bay Rays ... The July 31 trade deadline is inching closer ... Texas Rangers 4-3 Tampa Bay Rays....
Irish death rate from melanoma higher than Spain
Edit Irish Independent 22 Jul 2017
The death rate from the most dangerous form of skin cancer is higher in Ireland than in sunspots such as Spain and Italy, a new report has revealed ... More men than women die of the disease, according to the report on skin cancer trends from the National Cancer Registry ... This is consistent with the kind of exposure to damaging rays from sunbathing ... The rise has been particularly marked for men ... Some 95pc of UV rays are blocked by cotton; ... ....
Larry Wilson: No Secrets No Lies
Edit All About Jazz 22 Jul 2017
Ray Callendar. " data-original-title="" title="">Ray Callendar's trumpet solo is very strong, he seems to play each note with the attitude of 'You're mine, I own you!' Dickinson bellows in top fashion and Wilson and up-right bassist Mike Perez ... tenor saxophone; Ray Callendar ... Dickinson Joshua Bowlus Ray Callendar Mike Perez Jeremy Sauer Akia Uwanda....
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