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Case-Based Reasoning - AI 101
 
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You can support the making of these videos through the AI and Games Patreon page: http://www.patreon.com/ai_and_games Like us on Facebook: http://www.facebook.com/AIandGames Follows us on Twitter: http://www.twitter.com/AIandGames -- In this AI 101 video we take a moment to explore the rationale, requirements and application of the Case-Based Reasoning technique. How do we use it? Why do we use it? And how does it relate to more traditional aspects of human cognitive behaviour? -- Music in this Video: 'Happy Go Lucky ChipTune' Written and Performed by 'Teknoaxe': http://www.youtube.com/user/teknoaxe http://www.teknoaxe.com http://www.patreon.com/teknoaxe
Views: 9428 AI and Games
Data Mining: Carvana Lemon Car Prediction using SAS Enterprise Miner
 
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Business Case: To predict if the car purchased at the Auction is a bad buy, using car related and purchase related data. Methods: Logistic regression, Decision Trees, Memory Based Reasoning, Neural Networks using SAS Enterprise Miner.
Views: 1601 Sachin's Tech Corner
What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning
 
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What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning - CASE-BASED REASONING definition - CASE-BASED REASONING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deeply explored in cognitive science. Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: 1. Retrieve: Given a target problem, retrieve from memory cases relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.
Views: 4334 The Audiopedia
Reasoning Tricks || Based on Letter Series ||SSC CGL,BANK PO, IBPS, Railway,CPO, UPSC ||
 
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In This video you learn the tricks to solve letter series questions easily. Like our facebook page https://www.facebook.com/pandorasbox013/
Views: 4352892 Pandora's Box
DATA INTERPRETATION SHORTCUT TECHNIQUES | IBPS PO PRE 2016 D I QUESTION | MEMORY BASED
 
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DATA INTERPRETATION SHORTCUT TECHNIQUES | IBPS PO PRE 2016 D I QUESTION | MEMORY BASED
Views: 172 PREP INDIA
Contrast with Case-Based Reasoning - Georgia Tech - KBAI: Part 4
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-1934948585/m-1945549504 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 400 Udacity
A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data
 
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Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data Authors: Zhuoning Yuan (University of Iowa); Xun Zhou (University of Iowa); Tianbao Yang (University of Iowa) Abstract: Predicting traffic accidents is a crucial problem to improving transportation and public safety as well as safe routing. The problem is also challenging due to the rareness of accidents in space and time and spatial heterogeneity of the environment (e.g., urban vs. rural). Most previous research on traffic accident prediction conducted by domain researchers simply applied classical prediction models on limited data without addressing the above challenges properly, thus leading to unsatisfactory performance. A small number of recent works have attempted to use deep learning for traffic accident prediction. However, they either ignore time information or use only data from a small and homogeneous study area (a city), without handling spatial heterogeneity and temporal auto-correlation properly at the same time. In this paper we perform a comprehensive study on the traffic accident prediction problem using the Convolutional Long Short-Term Memory (ConvLSTM) neural network model. A number of detailed features such as weather, environment, road condition, and traffic volume are extracted from big datasets over the state of Iowa across 8 years. To address the spatial heterogeneity challenge in the data, we propose a Hetero-ConvLSTM framework, where a few novel ideas are implemented on top of the basic ConvLSTM model, such as incorporating spatial graph features and spatial model ensemble. Extensive experiments on the 8-year data over the entire state of Iowa show that the proposed framework makes reasonably accurate predictions and significantly improves the prediction accuracy over baseline approaches. More on http://www.kdd.org/kdd2018/
Views: 596 KDD2018 video
Universal Schema for Knowledge Representation from Text and Structured Data
 
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Entities and the relations among them are central for representing our knowledge about the world. My work concentrates on discovering relations from information sources available to us, including unstructured text corpora and structured data. Previous work for relation extraction can be classified into two categories in terms of how they represent relations. The first is based on supervised learning, representing relations using pre-defined types from knowledge bases. This approach relies on human efforts to define relation types. The second generalizes the Open IE style relation extraction, representing relations as clusters of textual patterns. This assumes semantic equivalence among patterns falling into the same cluster, failing to represent the diversity and ambiguity of the patterns. I will present a new approach, Universal Schema – the union of all relations seen among surface patterns and available structured knowledge bases. This representation preserves the diversity and ambiguity of textual patterns and allows us to generalize among them. In this talk, I will explain how to perform relation extraction in universal schema. We formalize the task as matrix completion and employ matrix factorization to learn implications among relations. Experiments demonstrate that using universal schema for relation extraction provides new state-of-the-art accuracy. We also extend universal schema to entity type extraction.
Views: 769 Microsoft Research
Mining the Crowd - Tova Milo - Technion Computer Engineering lecture
 
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Mining the Crowd Lecture by Prof. Tova Milo, Head of the Computer Science Department, Tel Aviv University Harnessing a crowd of Web users for data collection has recently become a wide-spread phenomenon. A key challenge is that the human knowledge forms an open world and it is thus difficult to know what kind of information we should be looking for. Classic databases have addressed this problem by data mining techniques that identify interesting data patterns. These techniques, however, are not suitable for the crowd. This is mainly due to properties of the human memory, such as the tendency to remember simple trends and summaries rather than exact details. Following these observations, we develop here a novel model for crowd mining. We will consider in the talk the logical, algorithmic, and methodological foundations needed for such a mining process, as well as the applications that can benefit from the knowledge mined from crowd.
Views: 830 Technion
Rule Based Systems
 
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Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 9162 nptelhrd
On the Anonymization of Sparse High-Dimensional Data
 
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Title: On the Anonymization of Sparse High-Dimensional Data Domain: Data Mining Description: 1, Privacy preservation is the most focussed issue in information publication, on the grounds that the sensitive data shouldn't be disclosed. For this regard, several privacy preservation data mining algorithms are proposed. 2, Generalisation, Bucketisation and Anatomisation techniques are used as a part of this regard. They ensure the privacy of the user,either by modifying quasi identifier values or by including noise. 3, These techniques are well suited for low dimensional data and they expel the most valuable information from the dataset.In this work,we concentrate on protection against identity disclosure in the publication of sparse high dimensional data. 4, The sparse dataset which is scanty has less information distributed in the entire dataset.So,in the first phase we transform the dataset into a band matrix framework by coordinating Genetic algorithm with Cuckoo search algorithm.This makes the nearest rows associated and makes the non zero components near to the diagonal and lessens the search space and also memory. 5, In the other phase a novel anatomisation technique based on disassociation is introduced to safeguard privacy.This technique isolates the quasi identifier values with sensitive attributes and publishes quasi identifiers straightforwardly.Then density based clustering is employed to anonymise the underlying data,ands protects against identity disclosure and increases data utility The adversary cannot relate the sensitive value with high probability.Experimental results demonstrate that this technique decreases information loss, reconstruction error and increases data utility. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
NIPS 2011 Learning Semantics Workshop: Towards More Human-like Machine Learning of Word Meanings
 
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Learning Semantics Workshop at NIPS 2011 Invited Talk: Towards More Human-like Machine Learning of Word Meanings by Josh Tenenbaum Josh Tenenbaum is a Professor in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology. Him and his colleagues in the Computational Cognitive Science group study one of the most basic and distinctively human aspects of cognition: the ability to learn so much about the world, rapidly and flexibly. Abstract: How can we build machines that learn the meanings of words more like the way that human children do? I will talk about several challenges and how we are beginning to address them using sophisticated probabilistic models. Children can learn words from minimal data, often just one or a few positive examples (one-shot learning). Children learn to learn: they acquire powerful inductive biases for new word meanings in the course of learning their first words. Children can learn words for abstract concepts or types of concepts that have no little or no direct perceptual correlate. Children's language can be highly context-sensitive, with parameters of word meaning that must be computed anew for each context rather than simply stored. Children learn function words: words whose meanings are expressed purely in how they compose with the meanings of other words. Children learn whole systems of words together, in mutually constraining ways, such as color terms, number words, or spatial prepositions. Children learn word meanings that not only describe the world but can be used for reasoning, including causal and counterfactual reasoning. Bayesian learning defined over appropriately structured representations — hierarchical probabilistic models, generative process models, and compositional probabilistic languages — provides a basis for beginning to address these challenges.
Views: 2514 GoogleTechTalks
Digital Reforms in India: Payment & Governance – Major Computer Terminologies
 
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Dr. Manishika Jain explains the various digital reforms and key terminologies in computers Data Analytics Explore facts Give specific answer to specific questions Has test hypothesis framework Data visualization tools are required Requires languages like Python & R Rooted in business analytics & business intelligence models Data Mining Generate new information & unlock insights • Descriptive: Information about existing data • Predictive: Forecast based on the data Explore new trends Requires classical and advanced artificial intelligence, pattern distribution and traditional statistics Done without any preconceived hypothesis - information from the data is not used to answer specific questions of organisation. Is close to machine learning & use scientific and mathematical techniques For paper 1 postal course - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm Data Analytics @0:10 Data Mining @0:12 Prepaid Instruments @2:42 Types @5:18 Bitcoin @7:37 Worldwide Payment Systems @7:41 NPCI @8:00 NFS @8:19 UPI @8:51 BHIM @10:22 IMPS @10:36 AEPS (Aadhar Enabled Payment System) @10:47 *99# @11:28 Less Case Townships @12:51 Bharat Bill Payment System #13:30 Bharat QR @14:03 RuPay @14:37 Micro ATM @14:58 Digital India @15:22 9 Pillars @15:56 E-Sign: Online Digital Signature Service @17:54 DigiLocker @19:08 E-Hospital @19:48 Digitize India Platform (DIP) @20:01 PRAGATI @20:24 National Cloud – MeghRaj @21:02 Public Wi-Fi Hotspots @21:19 National Optical Fibre Network @21:42 BharaNet @22:07 Computer Malware @22:50 Endpoint Security Solutions – by – C-DAC @23:09 Secure e-Mail @24:52 #Remittance #Merchant #Financial #Instruments #Consumer #Instrument #Descriptive #Hypothesis #Visualization #Terminologies #Manishika #Examrace Examrace is number 1 education portal for competitive and scholastic exam like UPSC, NET, SSC, Bank PO, IBPS, NEET, AIIMS, JEE and more. We provide free study material, exam & sample papers, information on deadlines, exam format etc. Our vision is to provide preparation resources to each and every student even in distant corners of the globe. Dr. Manishika Jain served as visiting professor at Gujarat University. Earlier she was serving in the Planning Department, City of Hillsboro, Hillsboro, Oregon, USA with focus on application of GIS for Downtown Development and Renewal. She completed her fellowship in Community-focused Urban Development from Colorado State University, Colorado, USA.
Views: 14904 Examrace
NIPS 2011 Learning Semantics Workshop: From Machine Learning to Machine Reasoning
 
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Learning Semantics Workshop at NIPS 2011 Invited Talk: From Machine Learning to Machine Reasoning by Léon Bottou Léon Bottou is a research scientist with broad interests in practical and theoretical machine learning. His work on large scale learning and stochastic gradient algorithms has received attention in the recent years. Léon is also known for the DjVu document compression system. Abstract: A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labeled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated "all-purpose" inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.
Views: 844 GoogleTechTalks
Identifying Suspicious URLs: An Application of Large-Scale Online Learning
 
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Google Tech Talk May 5, 2010 ABSTRACT Presented by Justin Ma. We explore online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving daily classification accuracies up to 99% over a balanced data set. Slides: http://cseweb.ucsd.edu/~jtma/google_talk/jtma-google10.pdf Justin Ma is a PhD candidate at UC San Diego advised by Stefan Savage, Geoff Voelker and Lawrence Saul. His research interests are in systems and networking with an emphasis on network security, and his current focus is the application of machine learning to problems in security. He will be joining UC Berkeley as a postdoc after graduation. [Home page: http://www.cs.ucsd.edu/~jtma/ ]
Views: 10105 GoogleTechTalks
Data Analysis & Interpretation OF  PO  Exam
 
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ANIL AGGARWAL BANK VIDEO/ IBPS EXAM/HINDI/ENGLISH
Views: 616 Anil Aggarwal
mod02lec07
 
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Views: 3476 Data Mining - IITKGP
Q&A with BitcoinABC and Others
 
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Bitcoin ABC developers Amaury Sechet, Shammah Chancellor, Jason B. Cox and Antony Zegers are joined by Chris Pacia, Jonathan Toomim, Juan Garavaglia and Guillermo Paoletti to discuss Canonical/Lexical Transaction Ordering, OpCheckDataSig, 100 byte limit for transactions and Block Size www.thefutureofbitcoin.cash
Views: 1877 The Future of Bitcoin
Data Interpretation Set 2 on Maximisation & Minimisation for CAT/XAT/NMAT/SNAP/CMAT/IIFT
 
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Views: 2331 Study IQ education
Tune in for Microsoft Connect(); 2018
 
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Learn to build the apps of tomorrow, today with Azure and Visual Studio.
Provable Non-convex Projections for High-dimensional Learning Problems - Part1
 
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Typical high-dimensional learning problems such as sparse regression, low-rank matrix completion, robust PCA etc can be solved using projections onto non-convex sets. However, providing theoretical guarantees for such methods is difficult due to the non-convexity in projections. In this talk, we will discuss some of our recent results that show that non-convex projections based methods can be used to solve several important problems in this area such as: a) sparse regression, b) low-rank matrix completion, c) robust PCA. In this talk, we will give an overview of the state-of-the-art for these problems and also discuss how simple non-convex techniques can significantly outperform state-of-the-art convex relaxation based techniques and provide solid theoretical results as well. For example, for robust PCA, we provide first provable algorithm with time complexity O(n 2 r) which matches the time complexity of normal SVD and is faster than the usual nuclear+L 1 -regularization methods that incur O(n 3 ) time complexity. This talk is based on joint works with Ambuj Tewari, Purushottam Kar, Praneeth Netrapalli, Animashree Anandkumar, U N Niranjan, and Sujay Sanghavi.
Views: 423 Microsoft Research
Facial Dynamics Interpreter Network
 
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Demo Video for "Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?" ECCV 2018.
Views: 89 Lab IVY
SBI PO Main 2017 Analysis of Mock Test 1- Data Analysis and Interpretation
 
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This video deals with the analysis of Data Analysis and Interpretation section of the SBI PO Main Mock Test-1 conducted by Gradeup. This is part 2 of the complete discussion. In this video, we get a chance to understand the nature of questions in the SBI PO Main Exam and we also understand a good test taker's strategy. Want to practice full-length Mock test on SBI PO 2017 Mains for FREE? Click on this link: (https://gradeup.co/online-test-series/banking-insurance) Download Gradeup app for SBI PO 2017 Mains Exam Preparation here(https://play.google.com/store/apps/de...) Visit (https://gradeup.co/banking-insurance/... ) for articles, quizzes, videos and other content required to crack SBI PO 2017 Mains.You can post your queries on SBI PO 2017 Mains and get them resolved too by our expert mentors.
Views: 823 Gradeup
"A New Understanding of Prediction Markets Via No-Regret Learning" (CRCS Lunch Seminar)
 
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CRCS Lunch Seminar (Wednesday, April 21, 2010) Speakers: Jenn Wortman Vaughan Title: Title: A New Understanding of Prediction Markets Via No-Regret Learning Abstract: Suppose that you are interested in estimating the probability that Google will reinstate its Chinese search engine within the next two years. You might choose to spend hours digging through news articles, reading commentaries, and weighing various opinions against each other, eventually coming up with a reasonably well-informed guess. But you might be able to save yourself a lot of hassle (and potentially obtain a better estimate) by appealing to the wisdom of crowds. A prediction market is a financial market designed to aggregate information. A typical binary prediction market allows bets along a single dimension, for example, for or against Google reinstating its Chinese search engine by the end of 2011. In this case, bettors might trade securities that pay $1 if and only if Google moves back to China by the specified date. If the current market price of a share of this security is $p, then a rational, risk-neutral bettor should be willing to buy shares if he believes the true probability is greater than p. Conversely, he should be willing to sell shares if he believes the true probability is lower. In this sense, the current price per share provides an estimate of the population's collective belief about how likely it is that Google will reinstate its search engine. These estimates have proved quite accurate in practice in a wide variety of domains. Equilibrium theory offers some insight into why prediction markets should converge to accurate prices, but is plagued by strong assumptions and no-trade theorems. Furthermore, this theory says nothing of why particular prediction market mechanisms, such as Hanson's increasingly popular Logarithmic Market Scoring Rule, might produce more accurate estimates than others in practice. In this talk, I will describe some recent work aimed at understanding the learning power of particular market mechanisms by examining the deep mathematical connections that exist between prediction market mechanisms and common algorithms for "no-regret" learning. I will then describe how this synergy between prediction markets and machine learning can be leveraged to run an efficient market when the space of possible outcomes is complex. This talk is based primarily on joint work with Yiling Chen. It additionally includes ideas from earlier work with Lance Fortnow, Nicolas Lambert, and David Pennock. Bio: Jenn Wortman Vaughan is a Computing Innovation Fellow at Harvard University. She completed her Ph.D. at the University of Pennsylvania in 2009. Her research interests are in machine learning, computational aspects of economics, social network theory, and algorithms, all of which she studies using techniques from theoretical computer science. Her recent research has won several best student paper awards, as well as Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology. In her spare time, she is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which will be held for the fifth time this year. In the fall, Jenn will join the Computer Science Department at UCLA as an assistant professor. http://people.seas.harvard.edu/~jenn/
Views: 737 Harvard's CRCS
Lecture 15: Coreference Resolution
 
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Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference resolution and neural coreference resolution. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Force Field Analysis 1
 
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via YouTube Capture
Views: 401 Joan Thorvaldson
What is SIMILARITY SEARCH? What does SIMILARITY SEARCH mean? SIMILARITY SEARCH meaning & explanation
 
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What is SIMILARITY SEARCH? What does SIMILARITY SEARCH mean? SIMILARITY SEARCH meaning - SIMILARITY SEARCH definition - SIMILARITY SEARCH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects. Nearest neighbor search and range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex. The most general approach to similarity search that allows construction of efficient index structures use the mathematical notion of metric space. A popular approach for similarity search is locality sensitive hashing – LSH. hashes input items so that similar items map to the same "buckets" in memory with high probability (the number of buckets being much smaller than the universe of possible input items). It is often applied in nearest neighbor search on large scale high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases.
Views: 455 The Audiopedia
Last Minute Important Tips For IBPS SO Exam 2017------Strategy For IBPS IT Officer Exam
 
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In this video i explain the complete strategy to tackle the ibps it officer exam easily and also provide a model test paper for english,reasoning and math LINK BELOW.I provide you also some mcq of MIS,data mining and detail theory of important topics asked in SBI SO Exam.Also Read important notes from my website. ----------------------------------------------------------------------------------------------------------- Our Website is: www.studyregular.in ---------------------------------------------------------------------------------------------------------- Join Our Facebook Group For discussion: https://www.facebook.com/groups/372637659746460/ ---------------------------------------------------------------------------------------------------------- English,Reasoning and Math Model Test Paper: https://drive.google.com/open?id=0Bx5dlc6w4ab2SWFLRGhjMDdabDg ----------------------------------------------------------------------------------------------------------- MCQ For Data Mining: https://drive.google.com/open?id=0Bx5dlc6w4ab2ejNyNmxaVUc5RVk ----------------------------------------------------------------------------------------------------------- MCQ For MIS(Management Information System): https://drive.google.com/open?id=0Bx5dlc6w4ab2Q082eENHYnB6TU0 ----------------------------------------------------------------------------------------------------------- Detail Theory of Important Topics Asked in SBI SO Exam: https://drive.google.com/open?id=0Bx5dlc6w4ab2NzB6WTdXWjVjTkk ----------------------------------------------------------------------------------------------------------- Please Like ,Subscribe and Comment ......Thank You
Views: 11466 Study Regular
Seattle Conference on Scalability: CARMEN: A Scalable Scienc
 
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Google Tech Talks June 14, 2008 ABSTRACT CARMEN is a $9M project building a scalable science cloud. Its focus is on supporting neuroscientists who will use it to store, share and analyze 100s of TBs of data. Understanding how the brain works is a major scientific challenge which will benefit medicine, biology and computer science. Globally, over 100,000 neuroscientists are working on this problem. However, the data that forms the basis for their work is rarely shared even though it is difficult and expensive to produce. The CARMEN project (www.carmen.org.uk) is addressing these challenges by developing a scalable cloud architecture to enable data sharing, integration, and analysis supported by metadata. An expandable range of services are provided in the cloud to extract value from raw and transformed data. This promotes the sharing of analysis services as well as data, and allows services to execute close to the data on which they operate. This is essential to avoid having to ship vast quantities (TBs) of data out of the cloud to the user's machine for analysis. Internally, the CARMEN cloud is built as a set of Web Services. Through experience of a wide variety of e-scientific projects over the past 8 years, we have identified a core set of generic services that we believe are needed to support science. These services, their scalability issues and novel features are: - Data repository. Most of the primary data is time series signal data. Searching for patterns (such as neuronal spikes) is a key requirement. CARMEN uses a novel parallel search infrastructure to find patterns quickly, even in vast quantities of data. - Metadata repository. Users need to be able to quickly search metadatametdata describing tens of thousands of datasets in order to locate data that is of interest. Ontologies are used to structure experimental metadata, and techniques are needed to quickly search this type of data. - Service repository and dynamic deployment. A novel feature of the architecture is that the analysis services are stored in a repository in the cloud. Users can write services in a variety of languages, package them as web services and then upload them into the cloud. These are then dynamically deployed on compute nodes as required to meet user requests. - Workflow Enactment Engine. Users can build workflows from the available services in order to orchestrate the entire process of analysis. These are then executed in the cloud. - Security. Scientists wish to control precisely who has access to their data and services. This service ensures that these desires are met. The talk will describe the design of the CARMEN system and show how it addresses the key scalability issues. It will cover the cloud services, explaining how each is designed to scale up to support thousands of users analysing TBs of data. We will present results from the CARMEN prototype to illustrate solutions and issues. Speaker: Paul Watson Paul Watson is Professor of Computer Science and Director of the North East Regional e-Science Centre. He graduated in 1983 with a BSc (I) in Computer Engineering from Manchester University, followed by a PhD in 1986. In the 80s, as a Lecturer at Manchester University, he was a designer of the Alvey Flagship and Esprit EDS systems. From 1990-5 he worked for ICL as a system designer of the Goldrush MegaServer parallel database server, which was released as a product in 1994. In August 1995 he moved to Newcastle University, where he has been an investigator on research projects worth over $20M. His research interests are in scalable information management, in particular parallel database systems and data-intensive e-science. Slides for this talk are available at http://groups.google.com/group/seattle-scalability-conference
Views: 5191 GoogleTechTalks
Knowledge Management - Write short note on “KM Life Cycle”.
 
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Need Answer Sheet of this Question paper, contact [email protected] www.mbacasestudyanswers.com ARAVIND – 09901366442 – 09902787224 Knowledge Management Multiple Choices: Q1. UCC stands for: a. Universal Commercial Code b. Uniform Commercial Code c. Unique Commercial Code d. United Commercial Code Q2. E-business connects critical business systems and constituencies directly via: a. Internet b. Extranet c. Intranet d. All of the above Q3. Unusable rule are also called as: a. User rule b. Conflicting rule c. Subsumed rule d. None of the above Q4. Fact in knowledge codification refers to: a. Value of an object or a slot b. Codification scheme c. Both (a) & (b) d. Filling of slots Q5. An individual with skills & solutions that work some of the time but not all of the time is: a. Scribe b. Validity c. Novice d. None of the above Q6. CBR is: a. Case based reasoning b. Case based reliability c. Case based repository d. None of the above Q7. An unskilled employee trying to learn or gain some understanding of the captures knowledge is a: a. Pupil user b. Tutor user c. People user d. None of the above Q8. A rule of thumb based on years of experience is called: a. Procedural rule b. Tacit knowledge c. Heuristic d. None of the above Q9. Episodic knowledge is: a. The knowledge based on the fundamentals structure functions & behavior of objects b. The knowledge based on experimental information or episodes c. The knowledge based on the unrelated facts d. None of the above Q10. A directory that points to people, documents and repositories is: a. Knowledge map b. Knowledge codification c. Rapid prototyping d. None of the above Part Two: Q1. Write short note on “KM Life Cycle”. Q2. Write short note on “The Knowing Doing Gap”. Q3. What is Nominal Group Techniques (NGT)? Q4. What do you mean by Delphi Method? Q5. Which factors contributed to motivate the troops to go ahead for such a difficult task as recovering a damaged vehicle from such a difficult and treacherous terrain and getting it repaired in such a short time? Q6. Which incidents indicate the importance of good interpersonal relationships with juniors, peers and superiors and what is the importance of good interpersonal relationships? Q7. What other data-driven promotions could Carrier come up with using other data mining techniques? Q8. What manufacturing-driven applications can Carrier implement using data mining? Q9. Explain the concept of Tacit Knowledge. List the different techniques of capturing Tacit Knowledge. Q10. Explain Global Knowledge Leadership. What are the driving forces behind global expansion of knowledge management? Need Answer Sheet of this Question paper, contact [email protected] www.mbacasestudyanswers.com ARAVIND – 09901366442 – 09902787224
Views: 156 Mba Casestudyhelp
Machine Rebellion
 
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The possibility of Artificial Intelligence turning on humanity has been a concern for as long as we've had computers. Today we will look at some of those fears and see which ones might be valid and which might not be cause for alarm. Use my link http://www.audible.com/isaac and get a free audio book with a 30 day trial! Visit our Website: http://www.isaacarthur.net Join the Facebook Group: https://www.facebook.com/groups/1583992725237264/ Support the Channel on Patreon: https://www.patreon.com/IsaacArthur Visit the sub-reddit: https://www.reddit.com/r/IsaacArthur/ Listen or Download the audio of this episode from Soundcloud: https://soundcloud.com/isaac-arthur-148927746/machine-rebellion Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier Graphics Team: Edward Nardella Jarred Eagley Justin Dixon Jeremy Jozwik Katie Byrne Kris Holland Misho Yordanov Murat Mamkegh Pierre Demet Sergio Botero Stefan Blandin Script Editing: Andy Popescu Connor Hogan Edward Nardella Eustratius Graham Gregory Leal Jefferson Eagley Keith Blockus Luca de Rosa Mark Warburton Michael Gusevsky Mitch Armstrong MolbOrg Naomi Kern Philip Baldock Sigmund Kopperud Tiffany Penner Music AJ Prasad, "Cold Shadows" Lee Rosevere, "It's such a beautiful day" Kai Engel, "Morbid Imagination" Sergey Cheremisinov, "Jump in Infinity" Markus Junnikkala, "A Memory of Earth" Kai Engel, "Crying Earth" Sergey Cheremisinov, "Labyrinth" Brandon Liew, "Into the Storm" Lombus, "Doppler Shores"
Views: 199720 Isaac Arthur
1.1| NLP Course Introduction 14m11s
 
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Natural Language Processing NLP ========================= About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research. Show less Who is this class for: This course is for those who are interested in NLP field and want to know the current state-of-the-art in research and production. We expect that you have already taken some courses on machine learning and deep learning, but probably have never applied those models to texts and want to get a quick start.
Views: 48 TO Courses
Top 30 C++ -1 cse technical interview questions and answers tutorial for fresher experienced
 
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Top 30 C++ -1 cse technical interview questions and answers tutorial for fresher experienced c++ interview questions and answers c++ interview questions and answers in hindi c++ programming interview questions c++ programming interview questions and answers c++ interview questions and answers for experienced c++ interview questions c and c++ interview questions interview questions for c++ c c++ interview questions interview questions on c++ vc++ interview questions difference between c and c++ difference between c and c++ and java difference between c and c++ in tamil difference between c and c++ in telugu difference between c and c++ in hindi the difference between c and c++ what is difference between c and c++ basic oops concepts basic oops concepts with examples basic oops concepts in java basic oops concepts in c# basic oops concepts in hindi basic oops concept c++ oops basic concept basic concept of oops what is class and object in java what is class in c++ what is classification what is class and object in java by durga what is classification in data mining what is classical music what is class in java by durga what is classpath in java what is class in java in hindi what is class in oops what is class what is class in java what is class and object what is class diagram what is class 10 sd card what is class and object in c++ what is class a amplifier what is class and object in java in hindi what is class a surfacing what is class armor in destiny what is class consciousness what is class conflict what is abstract class in c# what is class in c what is class diagram in hindi what is class d amplifier what is class dojo what is class in html what is class in net what is class in hindi what is class in java telugu what is class in php what is class in java in tamil what is abstract class in java what is wrapper class in java what is singleton class in java what is scanner class in java what is abstract class in java in hindi what is static class in java what is anonymous class in java what is class loader what is class link what is class in python what is class sociology what is encapsulation in java what is encapsulation in c# what is encapsulation in oops what is encapsulation in hindi what is encapsulation in c++ in hindi what is encapsulation in networking what is encapsulation in java in hindi what is data encapsulation what is encapsulation what is encapsulation in c++ Placement Papers,Test Pattern,Placement Paper, Interview experience,Campus Placement Process, Placement Papers with Solution,Technical, hr Interview tips, Questions and Answers,aptitude papers,company details,pdf,for cse,ece,eee,Interview Experience and Selection Process,online Aptitude Test,bpo interview questions,Interview Puzzles,Company Profile.Learn and practice Aptitude questions and answers with explanation for interview, competitive examination and entrance test,aptitude,questions, answers, interview, placement, papers, engineering, interview videos,tips,videos for freshers,questions,prank,,attitude,at google,actress,about yourself,answers,electronics, civil, mechanical, networking, 2015, 2016, 2017,2018, reasoning, program, verbal, gk, knowledge, language, explanation, solution, problem, online, test, exam,quiz hr, interview, human, resource, questions, answers, freshers, discussion, topics, companies, Technical interview questions pdf, Technical online test - all answers are posted by experts. interview body language | conversation | communication skills | cracking tips | crack | videos | tips interview videos for freshers | of great personalities | attitude is everything | skills interview questions and answers for freshers in india | about technical support interview questions and answers for freshers | cse |ece technical manager interview questions and answers, technical job interview questions and answers, best technical interview questions and answers java | for freshers in india, For more details visit: http://www.wikitechy.com/
Views: 16241 Wikitechy
Deep Learning on Graphs (Neo4j Online Meetup #41)
 
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Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful. Our neural networks can take questions and knowledge graphs and return answers. Imagine: a google assistant that reads your own knowledge graph (and actually works) a BI tool reads your business' knowledge graph a legal assistant that reads the graph of your case Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages. Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases. Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data. ----------------------------- ABOUT THE SPEAKER ----------------------------- Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks. ----------------------------- ONLINE DISCUSSIONS ----------------------------- We'll be taking questions live during the session, but if you have any before or after be sure to post them in the project's thread in the Neo4j Community Site (https://community.neo4j.com/t/online-meetup-deep-learning-with-knowledge-graphs/2963). ---------------------------------------------------------------------------------------- WANT TO BE FEATURED IN OUR NEXT NEO4J ONLINE MEETUP? ---------------------------------------------------------------------------------------- We select talks from our Neo4j Community site! https://community.neo4j.com/ To submit your talk, post in in the #projects (if including a link to github or website) or #content (if linking to a blog post, slideshow, video, or article) categories. ------------------------------------------------------------------------- VOTE FOR THE PRESENTATIONS YOU'D LIKE TO SEE! ------------------------------------------------------------------------- 'VOTE' for the projects and content you'd like to see! Browse the the projects and content categories in our community site and 'heart' the ones you're interested in seeing! community.neo4j.com
Views: 2388 Neo4j
15. Learning: Near Misses, Felicity Conditions
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is an example of one-shot learning. We also discuss other aspects of how students learn, and how to package your ideas better. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 39785 MIT OpenCourseWare
Lecture - 17 Rule Based Systems II
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science & Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 12681 nptelhrd
Live from Disrupt Berlin 2018 Day 2
 
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TechCrunch Disrupt Berlin 2018 - Day 2
Views: 2466 TechCrunch
Gifted Case Study
 
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Gifted Case Study - created at http://animoto.com
Views: 138 aholman524
Lecture - 25 Rule Based Expart System
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 24201 nptelhrd
Utilizing Neo4j with AI Applications
 
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Patrick Smith and Brian Rodrigue, Excella, dive into how to use neo4j for AI applications.
Views: 485 Neo4j
CppCon 2017: Tobias Fuchs “Multidimensional Index Sets for Data Locality in HPC Applications”
 
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The Point of Views: Multidimensional Index Sets for Data Locality in HPC Applications http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2017 — In High Performance Computing, data access has complex implications and requires concepts that are fundamentally different from those provided in the STL. Iterators as we know them just are not enough. The proposed range concepts for the standard library are a significant improvement but are designed for the mental model of iterating and mapping values, not hierarchical domain decomposition. Even for a seemingly trivial array there are countless ways to partition and store its elements in distributed memory, and algorithms are required to behave and scale identically for all of them. It also does not help that most applications operate on multidimensional data structures where efficient access to neighborhood regions is crucial. Among HPC developers, it is therefore widely accepted that canonical iteration space and physical memory layout must be specified as separate concepts. For this, we use views based on multidimensional index sets, inspired by the proposed range concepts. In this session, we will explain the challenges when distributing container elements for thousands of cores and how modern C++ allows to achieve portable efficiency. As an HPC afficionado, you know you want this: copy( matrix_a | local() | block({ 2,3 }), matrix_b | block({ 4,5 }) ) If this does not look familiar to you: we give a gentle introduction to High Performance Computing along the way. — Tobias Fuchs: LMU Munich, Leibniz Supercomputing Centre, Research Associate Tobi is a freelancer in embedded systems and real-time applications for over 10 years, mostly for medical devices, and went back to academia for PhD studies in High Performance Computing at LMU Munich. He is the lead developer of the DASH C++ template library, a project of the German Research Foundation (DFG), and currently focuses on models and programming abstractions for data locality. As a hobby, he lures unsuspecting students into his C++ programming course to entrap them in category theory. — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 3641 CppCon
A Princess of Mars by Edgar Rice Burroughs (Barsoom #1)
 
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John Carter, an American Civil War veteran, goes prospecting in Arizona and, when set upon by Indians, is mysteriously transported to Mars, called "Barsoom" by its inhabitants. Carter finds that he has great strength on this planet, due to its lesser gravity. Carter soon falls in among the Tharks, a nomadic tribe of the planet's warlike, four-armed, green inhabitants. A timeless classic of sci-fi fantasy literature, this is the first book in Burrough's Barsoom Series. Foreword - 00:00 Chapter 01. On the Arizona Hills - 7:52 Chapter 02. The Escape of the Dead - 24:28 Chapter 03. My Advent on Mars - 36:04 Chapter 04. A Prisoner - 53:14 Chapter 05. I Elude My Watch Dog - 1:07:32 Chapter 06. A Fight That Won Friends - 1:17:34 Chapter 07. Child-Raising on Mars - 1:27:55 Chapter 08. A Fair Captive from the Sky - 1:40:53 Chapter 09. I Learn the Language - 1:54:32 Chapter 10. Champion and Chief - 2:04:21 Chapter 11. With Dejah Thoris - 2:28:03 Chapter 12. A Prisoner with Power - 2:44:07 Chapter 13. Love-Making on Mars- 2:57:06 Chapter 14. A Duel to the Death - 3:12:21 Chapter 15. Sola Tells Me Her Story - 3:32:32 Chapter 16. We Plan Escape - 3:53:50 Chapter 17. A Costly Recapture - 4:17:00 Chapter 18. Chained in Warhoon - 4:36:21 Chapter 19. Battling in the Arena - 4:46:25 Chapter 20. In the Atmosphere Factory - 4:57:50 Chapter 21. An Air Scout for Zodanga - 5:19:22 Chapter 22. I Find Dejah - 5:42:07 Chapter 23. Lost in the Sky - 6:05:14 Chapter 24. Tars Tarkas Finds a Friend - 6:19:08 Chapter 25. The Looting of Zodanga - 6:36:23 Chapter 26. Through Carnage to Joy - 6:48:17 Chapter 27. From Joy to Death - 7:02:59 Chapter 28. At the Arizona Cave - 7:14:57 Read by Mark Nelson (https://librivox.org/reader/251) Book #1 in the John Carter (Barsoom) Audiobook Series: https://www.youtube.com/playlist?list=PLTLQR-c2Hn-u9vR4RnZfM-hd4zaoFq8sS This is followed by "The Gods of Mars": https://www.youtube.com/watch?v=nwvS0VZjIhc
Views: 10905 Audiobooks Unleashed
JFK:  AND THE DEEP; STATE :  WITH  PALADIN, DR. JIM FETZER & OLE DAMMEGARD
 
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ROUNDTABLE DISCUSSION bringing forward all the evidence and where it leads. In tribute to the life of John F. Kennedy KERRY CASSIDY PROJECT CAMELOT http://projectcamelot.tv
Views: 26186 Project Camelot
IBPS SO 2018 | IBPS IT officer professional knowledge detailed syllabus [in Hindi] IBPS SO 2018
 
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Please watch: "mp patwari Topicwise syllabus 2017, म प्र पटवारी परीक्षा पाठ्यक्रम By Rohit khera Sir" https://www.youtube.com/watch?v=tZf_t0Qykak --~-- it officer, ibps, ibps it officer, it officer exam preparation, sbi, bank exam preparation, sbi it officer, ssc, adda247, so, officer, specialist officer, system officer job profile, trending, bankclerk, examnews, trendingvideos, youtube, facebook, ibps it officer 2017 review, ibps it officer 2017 interview experience - lucknow, professional knowledge, the grand jbr, ibps it officer experience, आईबीपीएस यह अधिकारी 2017 साक्षात्कार अनुभव, ibps it officer 2017 interview experience, bank interview, bank it officer role, it officer role, talentsprint, it officer job profile, bank it officer job profile, how to crack ibps it officer, ibps it officer strategy, ibps it officer 2017, it officer exam notes, preparation for it officer exam, material for ibps it officer exam, crack it officer exam, crack ibps it officer exam IBPS IT officer professional knowledge detailed syllabus [in Hindi] IBPS SO 2017. for more study material visit my Website: http://www.allexamplace.com/ If you enjoyed this video and would like to receive more similar content, For technology tricks and unboxing videos and gadget review visit http://www.alldigitalplace.com/ youtube:- https://www.youtube.com/channel/UCe5UBn2_4MB59PbvnCo3oEQ Join me at: Follow him on Twitter - https://twitter.com/errohitkhera Add him on Facebook - https://www.facebook.com/rohitkhera22 Follow him Instagram - http://instagram.com/errohitkhera MY GEAR MY BIG CAMERA: http://amzn.to/2ellwAw MY DSLR MIC: http://amzn.to/2eydYau MY MIC: http://amzn.to/2elpLfi MY CAR TRIPOD: http://amzn.to/2eypceW MY OTHER PHONE TRIPODS: MY SMALL TRIPOD: http://amzn.to/2eyjcTu MY SMALL CAMERA: http://amzn.to/2eWYYWo SECOND MIC: http://amzn.to/2ex42MZ MY TABLE TRIPOD: http://amzn.to/2elq7ma CHEAPER ACTION CAMERA: - http://amzn.to/2dzsMHm SMARTPHONE TRIPOD: http://amzn.to/2eX0Jmd MY DESKTOP MIC: http://amzn.to/2eloogV MY VLOG CAMERA: http://amzn.to/2dzuo40 MY SECOND DESKTOP MIC: http://amzn.to/2eX19cu Best Gaming Laptops (Best Buy Links) 1st: Lenovo Laptops Under 40,000 INR at http://fkrt.it/dRoMyTuuuN 2nd: Asus Laptops Under 40,000 INR at http://fkrt.it/Ch5Pb!NNNN 3rd: Other Best Gaming Laptops at http://amzn.to/2ellF6T Best Earphones Under 2000 INR 1st: Altec Lansing MZW100 http://fkrt.it/ChA6R!NNNN 2nd: HEAD X DYNAMIC http://amzn.to/2e1igVz 3rd: Senheiser CX 180 Street II http://amzn.to/2eX0E28 Best Headphones Over The Head 1st: Sennheiser HD 202 II Professional http://amzn.to/2dzvzQT 2nd: Sennheiser HD-201 Lightweight http://amzn.to/2e1eV97 Best Mobile Phones Under 5000 INR ( Best Buy Links ) New 1st: Xolo Era 1X http://fkrt.it/d0jLrTuuuN 2nd: Yu Yunique http://amzn.to/2exbkAd 3rd: Swipe Elite Plus http://fkrt.it/CViMb!NNNN 4th: InFocus bingo 21 http://amzn.to/2dzw39R Old 1st: Phicomm Energy 653 -http://amzn.to/2exa6oy 2nd: XOLO Era 4G - http://amzn.to/2eyl5zO Best Mobile Phones Under 5000 INR to 8000 INR ( Best Buy Links ) New 1st: Redmi 3S http://fkrt.it/duk79TuuuN 2nd: Coolpad Mega http://amzn.to/2elq0qP 3rd: Moto E3 Power http://fkrt.it/dwlIXTuuuN 4th: Lenovo A6000 Shot http://fkrt.it/CBLg5!NNNN 5th Coolpad Note 3 Lite http://amzn.to/2eX3h3Z Old 1st: YU Yuphoria - http://fkrt.it/CBZXT!NNNN 2nd: Coolpad Note 3 Lite - http://amzn.to/2eX2JLu 3rd: Lenovo K5 Plus - http://fkrt.it/CVGL6!NNNN 4th: Meizu M2 - http://fkrt.it/TcdffTuuuN Best Mobile Phones Under 8000 INR to 10000 INR ( Best Buy Links ) 1st: Redmi Note 3 http://amzn.to/2ezYaW9 2nd: Coolpad Note 3 http://amzn.to/2eiDVKP 3rd: Redmi 3S Prime http://fkrt.it/!fsfr!NNNN 4th: Le 1S http://fkrt.it/Tc!QbTuuuN 5th Asus Zenfone Max http://amzn.to/2eBI23r 6th Moto G4 Play http://amzn.to/2eAhBiY Best Mobile Phones Under 10000 INR to 13000 INR ( Best Buy Links ) 1st: LeEco Le 2 http://fkrt.it/!dNlq!NNNN 2nd: Redmi Note 3 32 Version http://fkrt.it/TCI7fTuuuN 3rd: Redmi 3S Prime http://fkrt.it/Tm4hzTuuuN 4th: Le 1S http://fkrt.it/TmQ2zTuuuN 5th Asus Zenfone Max http://amzn.to/2epVdcp 6th Moto G4 Plus http://amzn.to/2eAljt2 2. Best Smartphone Offers: Best Phone Deals on Flipkart - http://fkrt.it/TIxynTuuuN Best Phone Deals on Amazon - http://amzn.to/2epVyfb
Views: 3211 ALL EXAM PLACE
Stare Into The Lights My Pretties
 
02:08:22
We live in a world of screens. The average adult spends the majority of their waking hours in front of some sort of screen or device. We're enthralled, we're addicted to these machines. How did we get here? Who benefits? What are the cumulative impacts on people, society and the environment? What may come next if this culture is left unchecked, to its end trajectory, and is that what we want? *Stare Into The Lights My Pretties* investigates these questions with an urge to return to the real physical world, to form a critical view of technological escalation driven by rapacious and pervasive corporate interest. Covering themes of addiction, privacy, surveillance, information manipulation, behaviour modification and social control, the film lays the foundations as to why we may feel like we're sleeprunning into some dystopian nightmare with the machines at the helm. Because we are, if we don't seriously avert our eyes to stop this culture from destroying what is left of the real world. WATCH HERE https://stareintothelightsmypretties.jore.cc/ SEGMENTS 0:00:00 - Introduction 0:04:03 - “Progress” 0:16:58 - No Accident 0:23:10 - Mindset (Screen Culture) 0:51:12 - It’s All About Me! 1:10:48 - The Megamachine 1:16:52 - Creeping Normalcy 1:33:02 - Vegged Out 1:39:43 - It’s Full of Sugar and It Tastes So Nice 1:56:50 - The Real World 2:06:13 - Credits VOICES Susan Greenfield, Katina Michael, Derrick Jensen, Lelia Green, Roger Clarke, Nicholas Carr, Sherry Turkle, Douglas Rushkoff, Lewis Mumford, Eli Pariser, Andrew Keen, Clifford Nass, Rebecca Mackinnon, Bruce Schneier, Jerry Mander, Jeff Chester. CREDITS Written and Directed by Jordan Brown. Original camera by Jordan Brown, Masao Tamaoki and James Tomalin. Music by Jore, Sigur Rós, The Cinematic Orchestra, Ólafur Arnalds, Bonobo, Soundsource, Bzaurie, Clark, Rollmottle, Ma Spaventi, Nils Frahm, Max Richter, Eunoia and Seame Campbell. Additional footage credit where credit is due is made out to respective creators, some of whom are: Em Styles, Katerina Vittozzi, Eric De Lavarène, Isabelle Delannoy, Brian Frank, Yann Arthus-Bertrand, Ivan Cash, Yordan Zhechev, Ron Fricke, Monika Fleishmann, Raymond Delacruz, Rob Featherstone, Michael Mcsweeney, Juan Falgueras, Trevor Hedge, Jean Counet, David Kleijwegt, Godfrey Reggio, Naomi Ture, Chris Zobl, Siddharth Hirwani, Melly Lee, Refik Anadol, Marc Homs, Schnellebuntebilder, Kyle Littlejohn, Tobias Gremmler, Marina Wanderlust, Kristopher Lee, Brandon Johnson, Nicolas Fevrier, Judd Frazier, Ben Stevens, David Fedele, Frank Wiering, Rob Mcbride, Vido Yuandao, Justine Ezarik, David Machado Santos, Vasco Sotomaior, Wolfgang Strauss, Kornhaber Brown, Matthew Epler, James Kwan, China Techy, BigThink, Gigaom, Inc. Magazine, The Guardian, TED, TEDx, BBC, ABC, CNN, Indymedia; and all further credit where credit is due for unknown or unattributed creators whose work appears. Content creators and/or participants may or may not agree with the views expressed in this film, which was made with no budget, not-for-profit, and is released to the world for free for the purposes of critical discourse, education, and for cultivating radical social and political change.
Views: 28354 rwrite dotorg
10. Symbolic Execution
 
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MIT 6.858 Computer Systems Security, Fall 2014 View the complete course: http://ocw.mit.edu/6-858F14 Instructor: Armando Solar-Lezama In this lecture, Professor Solar-Lezama from MIT CSAIL presents the concept of symbolic execution. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 13642 MIT OpenCourseWare
Eager vs Lazy RC - Georgia Tech - Advanced Operating Systems
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud189/l-413668841/m-420668916 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud189 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 1165 Udacity
Moral Math of Robots: Can Life and Death Decisions Be Coded?
 
01:32:08
A self-driving car has a split second to decide whether to turn into oncoming traffic or hit a child who has lost control of her bicycle. An autonomous drone needs to decide whether to risk the lives of busload of civilians or lose a long-sought terrorist. How does a machine make an ethical decision? Can it “learn” to choose in situations that would strain human decision making? Can morality be programmed? We will tackle these questions and more as the leading AI experts, roboticists, neuroscientists, and legal experts debate the ethics and morality of thinking machines. Subscribe to our YouTube Channel for all the latest from WSF. Visit our Website: http://www.worldsciencefestival.com/ Like us on Facebook: https://www.facebook.com/worldsciencefestival Follow us on twitter: https://twitter.com/WorldSciFest Original Program Date: June 4, 2016 MODERATOR: Bill Blakemore PARTICIPANTS: Fernando Diaz, Colonel Linell Letendre, Gary Marcus, Matthias Scheutz, Wendell Wallach Can Life and Death Decisions Be Coded? 00:06 Siri... What is the meaning of life? 1:49 Participant introductions 4:01 Asimov's Three Laws of Robotics 6:22 In 1966 ELIZA was one of the first artificial intelligence systems. 10:20 What is ALPHAGO? 15:43 TAY Tweets the first AI twitter bot. 19:25 Can you test learning Systems? 26:31 Robots and automatic reasoning demonstration 30:31 How do driverless cars work? 39:32 What is the trolley problem? 49:00 What is autonomy in military terms? 56:40 Are landmines the first automated weapon? 1:10:30 Defining how artificial intelligence learns 1:16:03 Using Minecraft to teach AI about humans and their interactions 1:22:27 Should we be afraid that AI will take over the world? 1:25:08
Views: 46322 World Science Festival
Secret Satellite -- Засекреченный спутник [СУБТИТРЫ]
 
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Spy in the sky. One of the best documentaries ever made! Blast-off into an Era of vacuum tubes, computer punch-cards, International Uncertainty, Political Instability and mutual distrust. We must know the past to comprehend our present completely. But in order to understand something, it has to be compared. What was going on back then shows clearly what is happening in the World today. See the rare archival footage. Hear those who've been there, done that. Do your analysis, logical reasoning and thinking. Enjoy the story of The Cold War and Espionage. The story of people, machines, views and believes. And it goes much deeper!.. See for yourself!! http://en.wikipedia.org/wiki/Cold_War http://en.wikipedia.org/wiki/Espionage http://en.wikipedia.org/wiki/Space_Race http://en.wikipedia.org/wiki/Reconnaissance_satellites en.wikipedia.org/wiki/Corona_(satellite) http://www.daviddarling.info/encyclopedia/C/Corona_satellite.html http://en.wikipedia.org/wiki/Murphy%27s_law https://en.wikipedia.org/wiki/Sod's_law https://en.wikipedia.org/wiki/Central_Intelligence_Agency https://en.wikipedia.org/wiki/National_Reconnaissance_Office https://en.wikipedia.org/wiki/Itek https://en.wikipedia.org/wiki/RAND_Corporation https://en.wikipedia.org/wiki/Lockheed_Corporation https://en.wikipedia.org/wiki/Skunk_Works https://en.wikipedia.org/wiki/Lockheed_U-2 https://en.wikipedia.org/wiki/Thor-Agena Who Owns Space? A Primer on Space Law! https://youtu.be/0pC7Aa3s00Y ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ I, at any rate, am convinced that God doesn't play dice with the world. — A. Einstein Every time I get to know something new I learn that I know none. Fly the plane. Train your brain. Play the Game. Feel no pain. Narde — Lost fruit of the Tree of Knowledge. To Commemorate, Inspire, Educate... © © © © © © © © © © © © © © © © © © © Produced in association with Discovery Chanel All rights belong to their respective owners. I do not own, nor do I (or) intend to infringe or profit from this content whatsoever. FAIR USE~ Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. → ☠ Video might contain graphic scenes of super-natural phenomena, intensive aerial / psychological combat, engineering construction, deadly weapons in action, mind boggling destructions, brain disturbing reactions, distinctive historical facts, creative human / scientific achievements, real and(or) fictional artifacts. Viewer discretion (ill) advised.
Views: 515232 Babaj Aga
Google I/O 2013 - Fireside chat with Research at Google
 
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Alfred Spector, Jeff Dean, Peter Norvig, Thad Starner Research at Google is unique, as it is conducted across the entire Engineering organization - by Research Scientists as well as Software Engineers. Teams are integrated, boundaries are fluid, and we face challenges together while retaining a close feedback loop from our users. This enables us to quickly build, iterate, and launch new and innovative products that change the state of the art and thereby produce new research results. Google's definition of research is broad and happens in multiple ways across the organization. Our research results in technology advances in the areas of Audio/Video Fingerprinting, Image Understanding, MapReduce/Parallel Computing, Deep Learning, and Parsing at Scale, which we apply to many of our products. And, it also results in new whole new products, such as Voice Search and Google Translate. Learn about Google's hybrid approach to research from the engineers and scientists behind ideas such as Google Glass, MapReduce, Search, and online education, who conduct big idea experiments that translate into real-time innovation at Google. For all I/O 2013 sessions, go to https://developers.google.com/live
Views: 4284 Google Developers

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