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Approximate Pattern Matching Using Hierarchical Graph Construction and Sparse Distributed Representation
Portland State University PDXScholar Electrical and Computer Engineering Faculty Publications and Presentations Electrical and Computer Engineering 7-23-2019 Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation Aakanksha Mathuria Portland State University, [email protected] Dan Hammerstrom Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/ece_fac Part of the Engineering Commons Let us know how access to this document benefits ou.y Citation Details "Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation," A. Mathuria, D.W. Hammerstrom, International Conference on Neuromorphic Systems, Knoxville, TN, July 23-5, 2019 This Conference Proceeding is brought to you for free and open access. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation Aakanksha Mathuria Electrical and Computer Engineering Portland State University USA [email protected] Dan W. Hammerstrom Electrical and Computer Engineering Portland State University USA [email protected] ABSTRACT training on large numbers of images. None of these techniques actually captures the spatial relationships of the low level or high- With recent developments in deep networks, there have been level features, which biological networks appear to do. significant advances in visual object detection and Efficient graph representations capture the higher order recognition. However, some of these networks are still easily information content of the objects and provide algorithmic benefits fooled/hacked and have shown “bag of features” failures. -
Beneficial AI 2017
Beneficial AI 2017 Participants & Attendees 1 Anthony Aguirre is a Professor of Physics at the University of California, Santa Cruz. He has worked on a wide variety of topics in theoretical cosmology and fundamental physics, including inflation, black holes, quantum theory, and information theory. He also has strong interest in science outreach, and has appeared in numerous science documentaries. He is a co-founder of the Future of Life Institute, the Foundational Questions Institute, and Metaculus (http://www.metaculus.com/). Sam Altman is president of Y Combinator and was the cofounder of Loopt, a location-based social networking app. He also co-founded OpenAI with Elon Musk. Sam has invested in over 1,000 companies. Dario Amodei is the co-author of the recent paper Concrete Problems in AI Safety, which outlines a pragmatic and empirical approach to making AI systems safe. Dario is currently a research scientist at OpenAI, and prior to that worked at Google and Baidu. Dario also helped to lead the project that developed Deep Speech 2, which was named one of 10 “Breakthrough Technologies of 2016” by MIT Technology Review. Dario holds a PhD in physics from Princeton University, where he was awarded the Hertz Foundation doctoral thesis prize. Amara Angelica is Research Director for Ray Kurzweil, responsible for books, charts, and special projects. Amara’s background is in aerospace engineering, in electronic warfare, electronic intelligence, human factors, and computer systems analysis areas. A co-founder and initial Academic Model/Curriculum Lead for Singularity University, she was formerly on the board of directors of the National Space Society, is a member of the Space Development Steering Committee, and is a professional member of the Institute of Electrical and Electronics Engineers (IEEE). -
Artificial Intelligence and the “Barrier” of Meaning Workshop Participants
Artificial Intelligence and the “Barrier” of Meaning Workshop Participants Alan Mackworth is a Professor of Computer Science at the University of British Columbia. He was educated at Toronto (B.A.Sc.), Harvard (A.M.) and Sussex (D.Phil.). He works on constraint-based artificial intelligence with applications in vision, robotics, situated agents, assistive technology and sustainability. He is known as a pioneer in the areas of constraint satisfaction, robot soccer, hybrid systems and constraint-based agents. He has authored over 130 papers and co-authored two books: Computational Intelligence: A Logical Approach (1998) and Artificial Intelligence: Foundations of Computational Agents (2010; 2nd Ed. 2017). He served as the founding Director of the UBC Laboratory for Computational Intelligence and the President of AAAI and CAIAC. He is a Fellow of AAAI, CAIAC, CIFAR and the Royal Society of Canada. ***** Alison Gopnik is a professor of psychology and affiliate professor of philosophy at the University of California at Berkeley. She received her BA from McGill University and her PhD. from Oxford University. She is an internationally recognized leader in the study of children’s learning and development and introduced the idea that probabilistic models and Bayesian inference could be applied to children’s learning. She is an elected member of the American Academy of Arts and Sciences and a fellow of the Cognitive Science Society and the American Association for the Advancement of Science. She is the author or coauthor of over 100 journal articles and several books including “Words, thoughts and theories” MIT Press, 1997, and the bestselling and critically acclaimed popular books “The Scientist in the Crib” William Morrow, 1999, “The Philosophical Baby; What children’s minds tell us about love, truth and the meaning of life”, and “The Gardener and the Carpenter”, Farrar, Strauss and Giroux. -
2020 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy
http://gcrinstitute.org 2020 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy McKenna Fitzgerald, Aaron Boddy, & Seth D. Baum Global Catastrophic Risk Institute Cite as: McKenna Fitzgerald, Aaron Boddy, and Seth D. Baum, 2020. 2020 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy. Global Catastrophic Risk Institute Technical Report 20-1. Technical reports are published to share ideas and promote discussion. They have not necessarily gone through peer review. The views therein are the authors’ and are not necessarily the views of the Global Catastrophic Risk Institute. Executive Summary Artificial general intelligence (AGI) is artificial intelligence (AI) that can reason across a wide range of domains. While most AI research and development (R&D) deals with narrow AI, not AGI, there is some dedicated AGI R&D. If AGI is built, its impacts could be profound. Depending on how it is designed and used, it could either help solve the world’s problems or cause catastrophe, possibly even human extinction. This paper presents a survey of AGI R&D projects that are active in 2020 and updates a previous survey of projects active in 2017. Both surveys attempt to identify every active AGI R&D project and characterize them in terms of relevance to ethics, risk, and policy, focusing on seven attributes: The type of institution in which the project is based Whether the project publishes open-source code Whether the project has military connections The nation(s) in which the project is based The project’s goals for its AGI The extent of the project’s engagement with AGI safety issues The overall size of the project The surveys use openly published information as found in scholarly publications, project websites, popular media articles, and other websites. -
HTM Cortical Learning Algorithms
HIERARCHICAL TEMPORAL MEMORY including HTM Cortical Learning Algorithms VERSION 0.2.1, SEPTEMBER 12, 2011 ©Numenta, Inc. 2011 Use of Numenta’s software and intellectual property, including the ideas contained in this document, are free for non-commercial research purposes. For details, see http://www.numenta.com/about-numenta/licensing.php. Read This First! This is a draft version of this document. There are several things missing that you should be aware of. What IS in this document: This document describes in detail new algorithms for learning and prediction developed by Numenta. The new algorithms are described in sufficient detail that a programmer can understand and implement them if desired. It starts with an introductory chapter. If you have been following Numenta and have read some of our past white papers, the material in the introductory chapter will be familiar. The other material is new. What is NOT in this document: There are several topics related to the implementation of these new algorithms that did not make it into this early draft. - Although most aspects of the algorithms have been implemented and tested in software, none of the test results are currently included. - There is no description of how the algorithms can be applied to practical problems. Missing is a description of how you would convert data from a sensor or database into a distributed representation suitable for the algorithms. - The algorithms are capable of on-line learning. A few details needed to fully implement on-line learning in some rarer cases are not described. - Other planned additions include a discussion of the properties of sparse distributed representations, a description of applications and examples, and citations for the appendixes. -
HIERARCHICAL TEMPORAL MEMORY Including HTM Cortical Learning Algorithms
HIERARCHICAL TEMPORAL MEMORY including HTM Cortical Learning Algorithms VERSION 0.2.1, SEPTEMBER 12, 2011 ©Numenta, Inc. 2011 Use of Numenta’s software and intellectual property, including the ideas contained in this document, are free for non-commercial research purposes. For details, see http://www.numenta.com/about-numenta/licensing.php. ©Numenta 2011 Page 1 Numenta 번역 라이센스 Copyright (c) 2010, 2011 Numenta, Inc. All rights reserved. 여기에 포함된 글, 알고리즘, 예시 코드, 가코드 그리고 모든 다른 내용은 Numenta Inc.에서 발행된 hiearchial temporal memory("HTM") 기술에 기반하거나 번역된 것입니다. Numenta 는 원작에 대한 저작권을 가지고 있으며, 여기에 번역된 HTM 이나 알고리즘 관련한 특허에 대한 권리를 가지고 있습니다. Numenta 는 독립된 HTM 시스템의 사용이나 개발이 오직 연구 목적일 경우에 한하여, 특허 권리를 주장하지 않음에 동의하였으며, 상업이나 제품 개발을 위한 목적은 여기서 제외됩니다. HTM 기술을 상업적으로 이용하거나 제품을 사용하는 것이 Numenta 의 특허를 침해할 여지가 있는 경우, Numenta 로부터 상용 라이센스와 관련된 허락을 필요로 합니다. 앞에서 기술한 내용을 바탕으로, Numenta 는 오직 연구 목적으로만 알고리즘과 원작의 사용을 허가하며, 어떠한 상업적, 제품 사용도 이에 해당하지 않습니다. 이 라이센스에서 "상업적, 제품 사용" 이라 함은 추후에 훈련된 HTM 네트워크를 운용할 목적으로 그것을 학습시키거나, 상업적 혹은 제품 생산을 위한 응용프로그램을 개발하거나, HTM 기술의 결과물을 다른 사람들이 상업적 혹은 제품 생산을 위해 이용하도록 하는 것을 말합니다. 이 원본을 배포, 출판 혹은 복사하기 위해서는 반드시 이 번역된 라이센스의 전문을 영어와 해당 언어로 포함해야만 합니다. 어떠한 특허권에 대한 명시적 혹은 묵시적 라이센스도 본 라이센스에 부여되지 않았습니다. Numenta 는 아래의 허가된 번역본에 대하여, 번역의 품질이나 정확도에 대하여 어떠한 책임이나 의무도 가지지 않습니다. ©Numenta 2011 Page 2 Numenta Translation License Copyright (c) 2010, 2011 Numenta, Inc. -
Cognitive Architectures: Where Do We Go from Here?
Cognitive Architectures: Where do we go from here? Włodzisław DUCHa,1, Richard J. OENTARYOb, and Michel PASQUIERb aNicolaus Copernicus University, Torun, Poland bNanyang Technological University, Singapore Abstract. Cognitive architectures play a vital role in providing blueprints for building future intelligent systems supporting a broad range of capabilities similar to those of humans. How useful are existing architectures for creating artificial general intelligence? A critical survey of the state of the art in cognitive architectures is presented providing a useful insight into the possible frameworks for general intelligence. Grand challenges and an outline of the most promising future directions are described. Keywords: cognitive architectures, artificial general intelligence, neurocognitive models, intelligent agents. 1. Introduction A long-term goal for artificial general intelligence (AGI) is to create systems that will exceed human level of competence in a large number of areas. There is a steady progress toward this goal in several domains, including recognition of specific patterns, memorization and retrieval of vast amount of information, interpreting signals and other types of numerical information, autonomous control, board games and reasoning in restricted domains. Yet even in lower level cognitive functions, such as object recognition or scene analysis artificial systems are still far behind the natural ones. Higher-level cognitive functions, such as language, reasoning, problem solving or planning, involve complex knowledge structures and are much more difficult to realize. Various types of memory stored by the brain facilitate recognition, association, semantic interpretation and rapid retrieval of large amounts of complex information patterns. At quite basic level organization of storage and retrieval of information in computers is completely different than in brains. -
Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding
Rochester Institute of Technology RIT Scholar Works Theses 5-2018 Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding Luke G. Boudreau [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Boudreau, Luke G., "Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding" (2018). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding Luke G. Boudreau Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding Luke G. Boudreau May 2018 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Department of Computer Engineering Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding Luke G. Boudreau Committee Approval: Dr. Dhireesha Kudithipudi Advisor Date Professor of Computer Engineering Dr. Ernest Fokou´e Date Associate Professor of School of Mathematical Sciences Dr. Raymond Ptucha Date Assistant Professor of Computer Engineering i Acknowledgments There are any individuals that I would like to acknowledge. My parents, Karen and Jerry Boudreau, who have funded a significant portion of my education at RIT. My advisor, Dr. Kudithipudi, who has a thorough understanding of graduate student's priorities. Felipe Petroski Such, who has shown that when you find your passion you will pour all your energy in. -
A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy
http://gcrinstitute.org A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy Seth D. Baum Global Catastrophic Risk Institute Global Catastrophic Risk Institute Working Paper 17-1 Cite as: Seth D. Baum, 2017. A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy. Global Catastrophic Risk Institute Working Paper 17-1. Note from the author: Comments welcome. Revisions expected. This version 12 November 2017. Working papers are published to share ideas and promote discussion. They have not necessarily gone through peer review. The views therein are the authors’ and are not necessarily the views of the Global Catastrophic Risk Institute. Executive Summary Artificial general intelligence (AGI) is AI that can reason across a wide range of domains. While most AI research and development (R&D) is on narrow AI, not AGI, there is some dedicated AGI R&D. If AGI is built, its impacts could be profound. Depending on how it is designed and used, it could either help solve the world’s problems or cause catastrophe, possibly even human extinction. This paper presents the first-ever survey of active AGI R&D projects for ethics, risk, and policy. The survey attempts to identify every active AGI R&D project and characterize them in terms of seven attributes: The type of institution the project is based in Whether the project publishes open-source code Whether the project has military connections The nation(s) that the project is based in The project’s goals for its AGI The extent of the project’s -
Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture
Rochester Institute of Technology RIT Scholar Works Theses 6-2015 Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture Abdullah M. Zyarah Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Zyarah, Abdullah M., "Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture" (2015). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture by Abdullah M. Zyarah A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Engineering Supervised by Dr. Dhireesha Kudithipudi Department of Computer Engineering Kate Gleason College of Engineering Rochester Institute of Technology Rochester, New York June 2015 Approved by: Dr. Dhireesha Kudithipudi Associate Professor - R.I.T. Dept. of Computer Engineering Dr. Marcin Lukowiak Associate Professor - R.I.T. Dept. of Computer Engineering Dr. Mehran Mozaffari Kermani Assistant Professor - R.I.T. Dept. of Electrical and Microelectronic Engineering Dr. Sohail Dianat Department Head - Professor - R.I.T. Dept. of Electrical and Microelectronic Engineering Thesis Release Permission Form Rochester Institute of Technology Kate Gleason College of Engineering Title: Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture I, Abdullah M. Zyarah, hereby grant permission to the Wallace Memorial Library to reproduce my thesis in whole or part. Abdullah M. Zyarah Date Dedication To my family.. -
Classifying Via Hierarchical Temporal Memory
Classifying via Hierarchical Temporal Memory Fabian´ Fallas-Moya Francisco Torres-Rojas Sede del Atlantico´ Escuela de Computacion´ Universidad de Costa Rica Instituto Tecnologico´ de Costa Rica Cartago, Costa Rica San Jose,´ Costa Rica Email: [email protected] Email: [email protected] Abstract—With recently advances in technology (hardware and software) there is an interest of humanity in having machines that behave like humans do. One aspect that researchers have to overcome is how to imitate the cognitive processes of the brain; cognitive processes like visual pattern recognition, speech recognition, space comprehension and so on. This task needs an algorithm that receives raw information from the environment, thus a signal processing method is needed to convert the raw Fig. 1: Object tracking: Graphical description. Taken from [1] input into useful information. Computer Vision is an interesting field of research, because the process of capturing images is B. Challenges simple and the hardware to process these images is available with current technology. This research focuses on the field of A video is composed by a sequence of frames. A particu- classifying images using hierarchical temporal memory (HTM), larity of video is that its content can be affected by environ- a machine learning technique that imitates the neocortex and mental aspects, for example noise or shape transformations. emulates cognitive processes. There are some challenges for a video classifying system to overcome [8]. The first one is clutter, which happens when I. INTRODUCTION the appearance of any object or the background are similar to the appearance of the target, possibly interfering with its The computing power of a computer is greater than anyone observation.