ERA: Architectures for Inference
|
|
- Isabella Booth
- 5 years ago
- Views:
Transcription
1 ERA: Architectures for Inference Dan Hammerstrom Electrical And Computer Engineering 7/28/09 1
2 Intelligent Computing In spite of the transistor bounty of Moore s law, there is a large class of problems that computers still do not solve well These problems involve the transformation of data across the boundary between the real world and the digital world They occur wherever a computer is sampling and acting on real world data, which includes almost all embedded computing applications Our lack of general solutions to these problems, outside of specialized niches, constitutes a significant barrier to computer usage and huge potential markets Hammerstrom 7/28/09 2
3 These are difficult problems that require computers to find complex structures and relationships through space and time in massive quantities of low precision, ambiguous, noisy data AI pursued solutions in this area, but ran into scaling problems among other things Artificial Neural Networks (ANN) extended computational intelligence in a number of important ways, primarily by adding the ability to incrementally learn and adapt However, ANNs also had trouble scaling, and they were often difficult to apply to many problems Traditional Rule Based Knowledge systems are now evolving into probabilistic structures where inference becomes the key computation, generally based on Bayes rule Hammerstrom 7/28/09 3
4 Bayesian Networks We now have Bayesian networks A major contribution to this effort was the work of Judea Pearl Pearl, J., Probabilistic Reasoning In Intelligent Systems Networks of Plausible Inference, Morgan Kaufman, 1988 & 1997 These systems are far less brittle and they also more faithfully model aspects of animal behavior, since animals learn from their surroundings and appear to do a kind of probabilistic inference from learned knowledge as they interact with their environment Bayesian nets express the structured, graphical representations of probabilistic relationships between several random variables The graph structure is an explicit representation of conditional dependence (encoded by network edges) And the fundamental computation has become probabilistic inference The proper implementation of inference could be applied to *any* problem potentially being a golden bullet algorithm! Hammerstrom 7/28/09 4
5 A Simple Bayesian Network The CPT for node D, there are similar tables for A, B, and C P(d b,c) d 1 d 2 b 1, c Each node has a CPT or Conditional Probability Table b 2, c b 1, c b 2, c Hammerstrom 7/28/09 5
6 Pattern Recognition As Noisy Channel Decoding 6
7 Inference, A Simple Example The Inference Problem: Choose the most likely y, based on P[y x ] We need to infer the most likely original message given the data we received and our knowledge of the statistics of channel errors and the messages being generated Hammerstrom 7/28/09 7
8 Assumption: Bayesian Inference As A Fundamental Computation In Future Systems In mapping inference computations to hardware there there are a number of issues to be considered, including: The type and degree of parallelism (multiple, independent threads versus data parallelism) Arithmetic precision Inter-thread communication Local storage requirements, etc. There are several variations on basic Bayesian techniques, over a number of different fields, from communication theory and pattern recognition, to computer vision, robotics, and speech recognition However, for this review of inference as a computational model, three general families of algorithms are considered: 1) Inference by Analysis, 2) Inference by Random Sampling, and 3) Inference Using Distributed Representations 8
9 Analytic Inference Analytic techniques constitute the most widely used approach for doing inference over Bayesian Networks Most Bayesian Networks are evaluated using Bayesian Belief Propagation (BBP) developed by Pearl Typically data are input to the network by setting certain variables to known or observed values ( evidence ) Bayesian Belief Propagation is then performed to find the probability distributions of the free variables Analytic techniques require significant precision and dynamic range, generally in the form of floating point representations This plus the limited parallelism makes them good candidates for multi-core architectures, but not necessarily for more advanced nano-scale computation 9
10 Random Sampling Another approach to handling inference is via random sampling There are a number of techniques, most of which fall under the general category of Monte Carlo Simulation Again, evidence is input by setting some nodes to known values Then random samples of the free variables are generated Two commonly used techniques are Adaptive Importance Sampling and Markov Chain Monte Carlo simulation These techniques basically use adaptive sampling techniques to do an algorithmic search of the model s state space For large complex Bayesian Structures, such random sampling is often the only way to evaluate the network However, random sampling suffers from the fact that as the size of the Network increases, increasingly larger sample sets are required to obtain sufficiently accurate statistics 10
11 An example of hardware / device support for such a system is the work of Prof. Krishna Palem (Rice University) on probabilistic CMOS (pcmos) Prof. Palem has shown that using pcmos can provide significant performance benefits in implementing Monte Carlo random sampling pcmos logic then is used to generate random numbers, thereby accelerating the sampling process Monte Carlo techniques are computationally intensive and so still tend to have scaling limitations However, on the plus side, massively parallel evaluations are possible and arithmetic precision requirements are relaxed So such techniques map cleanly to simpler, massively parallel, low precision computing structures These techniques may also benefit from morphic cores with hardware accelerated random number generation. 11
12 Distributed Data Representation Networks Networks based on distributed data representations (DDR) are actually a different way to structure Bayesian Networks, and although analytic and sampling techniques can be used on these structures, they also allow different kinds of massively parallel execution The use of DDR is very promising, but is also the most limited in successful demonstrations of real applications Computing with DDRs can be thought of as the computational equivalent of spread spectrum communication In a distributed representation, meaning is not represented by single symbolic units, but is the result of the interaction of a group of units typically configured in a network structure, and often each unit can participate in several representations Representing data in this manner more easily allows incremental, integrative, decentralized adaptation The computational and communication loads are spread more evenly across the system Distributed representation also appears to be an important computational principle in neural systems 12
13 Biological neural circuits perform inference over huge knowledge structures in fractions of a second using extremely slow, unreliable devices It is not clear exactly how they manage this incredible trick, considering that Bayesian inference is so computationally intense (NP-Hard) However, there is some speculation that DDR plays an important role however, more theory and algorithm development is needed This is an active area of research and no doubt much progress will be made by the time many Emerging Research Devices become commercially available. One hypothesis is that sparsely connection networks lead naturally to the distribution of representation, and subsequently to significant factorization of the inference process, this, coupled with massively parallel hardware, may enable entirely new levels of inference capabilities. 13
14 One such model was developed by Lee and Mumford, who proposed a hierarchical, Bayesian inference model of the primate visual cortex 14 Lee and Mumford Visual cortex model Hammerstrom
15 Another example is the work of Jeff Hawkins and Dileep George at Numenta, Inc. Their model starts with an approximation to a general Bayesian module, which can then be combined into a hierarchy to form what they call a Hierarchical Temporal Memory (HTM) Issues related to hardware architectures for Bayesian Inference and how they may be implemented with emerging devices are now being studied 15
16 Architecture Mapping Bayesian Networks to a multi-core implementation is straightforward, just implement each node as a task - and in a simple SMP based, multi-core machine that would most certainly provide good performance However, this approach breaks down as we scale to very large networks Bayesian Networks tend to be storage intensive, so implementation issues such as data structure organization, memory management and cache utilization also become important A potentially serious performance constraint may be access to primary memory, and it is not yet clear how effective caching will be in ameliorating this delay Another is limited inter-thread bandwidth in distributed memory systems (since BBP Inference also sometimes requires extensive message passing) However, as we scale to the very large networks required to solve complex problems, a variety of optimizations become possible and, in fact, necessary 16
17 One promising massively parallel approach is that of associative processing, which has been shown to approximate Bayesian inference, and which has the potential for huge levels of parallelism Using morphic cores for heterogeneous multi-core structures, such massively parallel implementations of Bayesian networks becomes relevant Another interesting variation is to eliminate synchrony, where inter-module update messages arrive at random times and computation within a module proceeds at its own pace, updating its internal estimates when it receives update messages, otherwise continuing without them More study is needed to explore radical new implementation technologies, such as analog based soft constraint satisfaction, and how they may be used to do inference 17
18 Algorithm Family Summary Technique Parallelism (Threads) Inter-Thread Communication Computational Precision Storage/Node State of the Art Analytic Moderate Moderate High Moderate Mature Random Sampling Distributed / Hierarchies High Low Moderate Moderate Mature High Low Low Low Preliminary 18
NEOCORTICAL CIRCUITS. specifications
NEOCORTICAL CIRCUITS specifications where are we coming from? human-based computing using typically human faculties associating words with images -> labels for image search locating objects in images ->
More informationRethinking Cognitive Architecture!
Rethinking Cognitive Architecture! Reconciling Uniformity and Diversity via Graphical Models! Paul Rosenbloom!!! 1/25/2010! Department of Computer Science &! Institute for Creative Technologies! The projects
More informationPIB Ch. 18 Sequence Memory for Prediction, Inference, and Behavior. Jeff Hawkins, Dileep George, and Jamie Niemasik Presented by Jiseob Kim
PIB Ch. 18 Sequence Memory for Prediction, Inference, and Behavior Jeff Hawkins, Dileep George, and Jamie Niemasik Presented by Jiseob Kim Quiz Briefly describe the neural activities of minicolumn in the
More informationInformation Processing & Storage in the Brain
Information Processing & Storage in the Brain Jaekyun Moon KAIST Oct 16, 2012 @ NVRAMOS Is the doll turning clock-wise or counter-clock-wise? 2 Left Brain ( 지성적 ) vs Right Brain ( 감성적 ) LEFT BRAIN FUNCTIONS
More informationM.Sc. in Cognitive Systems. Model Curriculum
M.Sc. in Cognitive Systems Model Curriculum April 2014 Version 1.0 School of Informatics University of Skövde Sweden Contents 1 CORE COURSES...1 2 ELECTIVE COURSES...1 3 OUTLINE COURSE SYLLABI...2 Page
More informationAND BIOMEDICAL SYSTEMS Rahul Sarpeshkar
ULTRA-LOW-POWER LOW BIO-INSPIRED INSPIRED AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar Research Lab of Electronics Massachusetts Institute of Technology Electrical Engineering and Computer Science FOE Talk
More informationSparse Coding in Sparse Winner Networks
Sparse Coding in Sparse Winner Networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University, Athens, OH 45701 {starzyk, yliu}@bobcat.ent.ohiou.edu
More informationCS 4365: Artificial Intelligence Recap. Vibhav Gogate
CS 4365: Artificial Intelligence Recap Vibhav Gogate Exam Topics Search BFS, DFS, UCS, A* (tree and graph) Completeness and Optimality Heuristics: admissibility and consistency CSPs Constraint graphs,
More informationOscillatory Neural Network for Image Segmentation with Biased Competition for Attention
Oscillatory Neural Network for Image Segmentation with Biased Competition for Attention Tapani Raiko and Harri Valpola School of Science and Technology Aalto University (formerly Helsinki University of
More informationBelief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits.
Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits. Dileep George Electrical Engineering, Stanford University & Numenta Inc., Menlo Park, CA 94025 dil@stanford.edu
More informationMemory Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex
Memory Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex Saulius J. Garalevicius Department of Computer and Information Sciences, Temple University
More informationCognitive Neuroscience History of Neural Networks in Artificial Intelligence The concept of neural network in artificial intelligence
Cognitive Neuroscience History of Neural Networks in Artificial Intelligence The concept of neural network in artificial intelligence To understand the network paradigm also requires examining the history
More informationAssignment Question Paper I
Subject : - Discrete Mathematics Maximum Marks : 30 1. Define Harmonic Mean (H.M.) of two given numbers relation between A.M.,G.M. &H.M.? 2. How we can represent the set & notation, define types of sets?
More informationThe Development and Application of Bayesian Networks Used in Data Mining Under Big Data
2017 International Conference on Arts and Design, Education and Social Sciences (ADESS 2017) ISBN: 978-1-60595-511-7 The Development and Application of Bayesian Networks Used in Data Mining Under Big Data
More informationCS343: Artificial Intelligence
CS343: Artificial Intelligence Introduction: Part 2 Prof. Scott Niekum University of Texas at Austin [Based on slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All materials
More informationLecture 9 Internal Validity
Lecture 9 Internal Validity Objectives Internal Validity Threats to Internal Validity Causality Bayesian Networks Internal validity The extent to which the hypothesized relationship between 2 or more variables
More informationQuestions and Answers
In R. Dechter, H. Geffner, and J.Y. Halpern (Eds.), Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publication, 2010. 31 Questions and Answers Nils J. Nilsson Few people have
More informationOutline. What s inside this paper? My expectation. Software Defect Prediction. Traditional Method. What s inside this paper?
Outline A Critique of Software Defect Prediction Models Norman E. Fenton Dongfeng Zhu What s inside this paper? What kind of new technique was developed in this paper? Research area of this technique?
More informationCurriculum structure of B.Tech ECE Programme (III Semester onwards)
1.Range for total credits in III, IV, V and VI semester is 22-28 2. A course on Management (PC), will be run in VII semester for four branches and the rest will have it in VIII semester 3. Course credit
More informationBayesian Networks Representation. Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University
Bayesian Networks Representation Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University March 16 th, 2005 Handwriting recognition Character recognition, e.g., kernel SVMs r r r a c r rr
More informationMotivation: Attention: Focusing on specific parts of the input. Inspired by neuroscience.
Outline: Motivation. What s the attention mechanism? Soft attention vs. Hard attention. Attention in Machine translation. Attention in Image captioning. State-of-the-art. 1 Motivation: Attention: Focusing
More informationUsing Bayesian Networks to Analyze Expression Data. Xu Siwei, s Muhammad Ali Faisal, s Tejal Joshi, s
Using Bayesian Networks to Analyze Expression Data Xu Siwei, s0789023 Muhammad Ali Faisal, s0677834 Tejal Joshi, s0677858 Outline Introduction Bayesian Networks Equivalence Classes Applying to Expression
More informationDecisions and Dependence in Influence Diagrams
JMLR: Workshop and Conference Proceedings vol 52, 462-473, 2016 PGM 2016 Decisions and Dependence in Influence Diagrams Ross D. hachter Department of Management cience and Engineering tanford University
More informationSignal Detection Theory and Bayesian Modeling
Signal Detection Theory and Bayesian Modeling COGS 202: Computational Modeling of Cognition Omar Shanta, Shuai Tang, Gautam Reddy, Reina Mizrahi, Mehul Shah Detection Theory and Psychophysics: A Review
More informationCISC453 Winter Probabilistic Reasoning Part B: AIMA3e Ch
CISC453 Winter 2010 Probabilistic Reasoning Part B: AIMA3e Ch 14.5-14.8 Overview 2 a roundup of approaches from AIMA3e 14.5-14.8 14.5 a survey of approximate methods alternatives to the direct computing
More informationUsing Probabilistic Reasoning to Develop Automatically Adapting Assistive
From: AAAI Technical Report FS-96-05. Compilation copyright 1996, AAAI (www.aaai.org). All rights reserved. Using Probabilistic Reasoning to Develop Automatically Adapting Assistive Technology Systems
More informationUsing Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming
Appears in Proceedings of the 17th International Conference on Inductive Logic Programming (ILP). Corvallis, Oregon, USA. June, 2007. Using Bayesian Networks to Direct Stochastic Search in Inductive Logic
More informationFodor on Functionalism EMILY HULL
Fodor on Functionalism EMILY HULL Deficiencies in Other Theories Dualism Failure to account for mental causation Immaterial substance cannot cause physical events If mental processes were a different substance
More informationSupplementary notes for lecture 8: Computational modeling of cognitive development
Supplementary notes for lecture 8: Computational modeling of cognitive development Slide 1 Why computational modeling is important for studying cognitive development. Let s think about how to study the
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 5: Data analysis II Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis 5 Data analysis II 6 Single
More informationAppendix I Teaching outcomes of the degree programme (art. 1.3)
Appendix I Teaching outcomes of the degree programme (art. 1.3) The Master graduate in Computing Science is fully acquainted with the basic terms and techniques used in Computing Science, and is familiar
More informationNeuro-Inspired Statistical. Rensselaer Polytechnic Institute National Science Foundation
Neuro-Inspired Statistical Pi Prior Model lfor Robust Visual Inference Qiang Ji Rensselaer Polytechnic Institute National Science Foundation 1 Status of Computer Vision CV has been an active area for over
More informationRobot Learning Letter of Intent
Research Proposal: Robot Learning Letter of Intent BY ERIK BILLING billing@cs.umu.se 2006-04-11 SUMMARY The proposed project s aim is to further develop the learning aspects in Behavior Based Control (BBC)
More informationAutomatic Extraction of Synoptic Data. George Cernile Artificial Intelligence in Medicine AIM
Automatic Extraction of Synoptic Data George Cernile Artificial Intelligence in Medicine AIM Agenda Background Technology used Demonstration Questions How often are checklist elements included in a report,
More informationDeSTIN: A Scalable Deep Learning Architecture with Application to High-Dimensional Robust Pattern Recognition
DeSTIN: A Scalable Deep Learning Architecture with Application to High-Dimensional Robust Pattern Recognition Itamar Arel and Derek Rose and Robert Coop Department of Electrical Engineering and Computer
More informationHigh-level Vision. Bernd Neumann Slides for the course in WS 2004/05. Faculty of Informatics Hamburg University Germany
High-level Vision Bernd Neumann Slides for the course in WS 2004/05 Faculty of Informatics Hamburg University Germany neumann@informatik.uni-hamburg.de http://kogs-www.informatik.uni-hamburg.de 1 Contents
More informationDIGITIZING HUMAN BRAIN: BLUE BRAIN PROJECT
DIGITIZING HUMAN BRAIN: BLUE BRAIN PROJECT Diwijesh 1, Ms. Pooja Khanna 2 1 M.tech CSE, Amity University 2 Associate Professor, ASET ABSTRACT Human brain is most complex and unique creation of nature which
More informationA probabilistic method for food web modeling
A probabilistic method for food web modeling Bayesian Networks methodology, challenges, and possibilities Anna Åkesson, Linköping University, Sweden 2 nd international symposium on Ecological Networks,
More informationBayesian Belief Network Based Fault Diagnosis in Automotive Electronic Systems
Bayesian Belief Network Based Fault Diagnosis in Automotive Electronic Systems Yingping Huang *, David Antory, R. Peter Jones, Craig Groom, Ross McMurran, Peter Earp and Francis Mckinney International
More informationBundelkhand Institute of Engineering & Technology, Jhansi. B. Tech.-First Semester (Common to all Branches)
Bundelkhand Institute of Engineering & Technology, Jhansi B. Tech.-First Semester (Common to all Branches) 9.30 AM to 11.30 AM AUC-001 Human Values & Professional Ethics 23-12-2017 Saturday 2.00PM to 4.00
More informationArtificial Intelligence Lecture 7
Artificial Intelligence Lecture 7 Lecture plan AI in general (ch. 1) Search based AI (ch. 4) search, games, planning, optimization Agents (ch. 8) applied AI techniques in robots, software agents,... Knowledge
More informationPythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL SAL. Lee W. Wagenhals Alexander H. Levis
Pythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL Lee W. Wagenhals Alexander H. Levis ,@gmu.edu Adversary Behavioral Modeling Maxwell AFB, Montgomery AL March 8-9, 2007 1 Outline Pythia
More informationIntelligent Control Systems
Lecture Notes in 4 th Class in the Control and Systems Engineering Department University of Technology CCE-CN432 Edited By: Dr. Mohammed Y. Hassan, Ph. D. Fourth Year. CCE-CN432 Syllabus Theoretical: 2
More informationComputational Neuroscience. Instructor: Odelia Schwartz
Computational Neuroscience 2017 1 Instructor: Odelia Schwartz From the NIH web site: Committee report: Brain 2025: A Scientific Vision (from 2014) #1. Discovering diversity: Identify and provide experimental
More informationCS148 - Building Intelligent Robots Lecture 5: Autonomus Control Architectures. Instructor: Chad Jenkins (cjenkins)
Lecture 5 Control Architectures Slide 1 CS148 - Building Intelligent Robots Lecture 5: Autonomus Control Architectures Instructor: Chad Jenkins (cjenkins) Lecture 5 Control Architectures Slide 2 Administrivia
More informationVision: Over Ov view Alan Yuille
Vision: Overview Alan Yuille Why is Vision Hard? Complexity and Ambiguity of Images. Range of Vision Tasks. More 10x10 images 256^100 = 6.7 x 10 ^240 than the total number of images seen by all humans
More informationA Vision-based Affective Computing System. Jieyu Zhao Ningbo University, China
A Vision-based Affective Computing System Jieyu Zhao Ningbo University, China Outline Affective Computing A Dynamic 3D Morphable Model Facial Expression Recognition Probabilistic Graphical Models Some
More informationComputational Explorations in Cognitive Neuroscience Chapter 7: Large-Scale Brain Area Functional Organization
Computational Explorations in Cognitive Neuroscience Chapter 7: Large-Scale Brain Area Functional Organization 1 7.1 Overview This chapter aims to provide a framework for modeling cognitive phenomena based
More informationAU B. Sc.(Hon's) (Fifth Semester) Esamination, Introduction to Artificial Neural Networks-IV. Paper : -PCSC-504
AU-6919 B. Sc.(Hon's) (Fifth Semester) Esamination, 2014 Introduction to Artificial Neural Networks-IV Paper : -PCSC-504 Section-A 1. (I) Synaptic Weights provides the... method for the design of neural
More informationSpatial Cognition for Mobile Robots: A Hierarchical Probabilistic Concept- Oriented Representation of Space
Spatial Cognition for Mobile Robots: A Hierarchical Probabilistic Concept- Oriented Representation of Space Shrihari Vasudevan Advisor: Prof. Dr. Roland Siegwart Autonomous Systems Lab, ETH Zurich, Switzerland.
More informationAffective Game Engines: Motivation & Requirements
Affective Game Engines: Motivation & Requirements Eva Hudlicka Psychometrix Associates Blacksburg, VA hudlicka@ieee.org psychometrixassociates.com DigiPen Institute of Technology February 20, 2009 1 Outline
More informationAn Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns
An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns 1. Introduction Vasily Morzhakov, Alexey Redozubov morzhakovva@gmail.com, galdrd@gmail.com Abstract Cortical
More informationGrounded Cognition. Lawrence W. Barsalou
Grounded Cognition Lawrence W. Barsalou Department of Psychology Emory University July 2008 Grounded Cognition 1 Definition of grounded cognition the core representations in cognition are not: amodal symbols
More informationBAYESIAN NETWORK FOR FAULT DIAGNOSIS
BAYESIAN NETWOK FO FAULT DIAGNOSIS C.H. Lo, Y.K. Wong and A.B. ad Department of Electrical Engineering, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong Fax: +852 2330 544 Email: eechlo@inet.polyu.edu.hk,
More informationMemory, Attention, and Decision-Making
Memory, Attention, and Decision-Making A Unifying Computational Neuroscience Approach Edmund T. Rolls University of Oxford Department of Experimental Psychology Oxford England OXFORD UNIVERSITY PRESS Contents
More informationUnit 2 Boundary Value Testing, Equivalence Class Testing, Decision Table-Based Testing. ST 8 th Sem, A Div Prof. Mouna M.
Unit 2 Boundary Value Testing, Equivalence Class Testing, Decision Table-Based Testing ST 8 th Sem, A Div 2017-18 Prof. Mouna M. Naravani 19-02-2018 Dept. of CSE, BLDEACET, Vijarapur 2 Boundary Value Testing
More informationObject recognition and hierarchical computation
Object recognition and hierarchical computation Challenges in object recognition. Fukushima s Neocognitron View-based representations of objects Poggio s HMAX Forward and Feedback in visual hierarchy Hierarchical
More informationCS221 / Autumn 2017 / Liang & Ermon. Lecture 19: Conclusion
CS221 / Autumn 2017 / Liang & Ermon Lecture 19: Conclusion Outlook AI is everywhere: IT, transportation, manifacturing, etc. AI being used to make decisions for: education, credit, employment, advertising,
More informationResearch and Innovation Roadmap Annual Projects
Research and Innovation Roadmap 2019 Annual Projects Contents Overview 01 Capability Development 02 Service Delivery Improvement 03 Forensic Fundamentals 04 Operational Requirement 05 Intelligence Application
More informationPavlovian, Skinner and other behaviourists contribution to AI
Pavlovian, Skinner and other behaviourists contribution to AI Witold KOSIŃSKI Dominika ZACZEK-CHRZANOWSKA Polish Japanese Institute of Information Technology, Research Center Polsko Japońska Wyższa Szko
More informationA Bayesian Network Analysis of Eyewitness Reliability: Part 1
A Bayesian Network Analysis of Eyewitness Reliability: Part 1 Jack K. Horner PO Box 266 Los Alamos NM 87544 jhorner@cybermesa.com ICAI 2014 Abstract In practice, many things can affect the verdict in a
More informationBiologically-Inspired Human Motion Detection
Biologically-Inspired Human Motion Detection Vijay Laxmi, J. N. Carter and R. I. Damper Image, Speech and Intelligent Systems (ISIS) Research Group Department of Electronics and Computer Science University
More informationCogs 202 (SP12): Cognitive Science Foundations. Computational Modeling of Cognition. Prof. Angela Yu. Department of Cognitive Science, UCSD
Cogs 202 (SP12): Cognitive Science Foundations Computational Modeling of Cognition Prof. Angela Yu Department of Cognitive Science, UCSD http://www.cogsci.ucsd.edu/~ajyu/teaching/cogs202_sp12/index.html
More informationEEL-5840 Elements of {Artificial} Machine Intelligence
Menu Introduction Syllabus Grading: Last 2 Yrs Class Average 3.55; {3.7 Fall 2012 w/24 students & 3.45 Fall 2013} General Comments Copyright Dr. A. Antonio Arroyo Page 2 vs. Artificial Intelligence? DEF:
More informationProbabilistic Models in Cognitive Science and Artificial Intelligence
Probabilistic Models in Cognitive Science and Artificial Intelligence Very Brief History of Cog Sci and AI 1950 s-1980 s Symbolic models of cognition von Neumann computer architecture as metaphor 1980
More informationDigital. hearing instruments have burst on the
Testing Digital and Analog Hearing Instruments: Processing Time Delays and Phase Measurements A look at potential side effects and ways of measuring them by George J. Frye Digital. hearing instruments
More informationPlan Recognition through Goal Graph Analysis
Plan Recognition through Goal Graph Analysis Jun Hong 1 Abstract. We present a novel approach to plan recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure
More informationRecognizing Scenes by Simulating Implied Social Interaction Networks
Recognizing Scenes by Simulating Implied Social Interaction Networks MaryAnne Fields and Craig Lennon Army Research Laboratory, Aberdeen, MD, USA Christian Lebiere and Michael Martin Carnegie Mellon University,
More informationComputational Models for Belief Revision, Group Decisions and Cultural Shifts
Computational Models for Belief Revision, Group Decisions and Cultural Shifts Whitman Richards (PI), M. I. T. Computer Science and Artificial Intelligence Laboratory MURI Objective: models that forecast
More informationPS3021, PS3022, PS4040
School of Psychology Important Degree Information: B.Sc./M.A. Honours The general requirements are 480 credits over a period of normally 4 years (and not more than 5 years) or part-time equivalent; the
More informationIntelligent Machines That Act Rationally. Hang Li Bytedance AI Lab
Intelligent Machines That Act Rationally Hang Li Bytedance AI Lab Four Definitions of Artificial Intelligence Building intelligent machines (i.e., intelligent computers) Thinking humanly Acting humanly
More informationIntroduction to Artificial Intelligence 2 nd semester 2016/2017. Chapter 2: Intelligent Agents
Introduction to Artificial Intelligence 2 nd semester 2016/2017 Chapter 2: Intelligent Agents Mohamed B. Abubaker Palestine Technical College Deir El-Balah 1 Agents and Environments An agent is anything
More information10CS664: PATTERN RECOGNITION QUESTION BANK
10CS664: PATTERN RECOGNITION QUESTION BANK Assignments would be handed out in class as well as posted on the class blog for the course. Please solve the problems in the exercises of the prescribed text
More informationJitter-aware time-frequency resource allocation and packing algorithm
Jitter-aware time-frequency resource allocation and packing algorithm The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As
More informationLocal Image Structures and Optic Flow Estimation
Local Image Structures and Optic Flow Estimation Sinan KALKAN 1, Dirk Calow 2, Florentin Wörgötter 1, Markus Lappe 2 and Norbert Krüger 3 1 Computational Neuroscience, Uni. of Stirling, Scotland; {sinan,worgott}@cn.stir.ac.uk
More informationCAS Seminar - Spiking Neurons Network (SNN) Jakob Kemi ( )
CAS Seminar - Spiking Neurons Network (SNN) Jakob Kemi (820622-0033) kemiolof@student.chalmers.se November 20, 2006 Introduction Biological background To be written, lots of good sources. Background First
More informationOverview of the visual cortex. Ventral pathway. Overview of the visual cortex
Overview of the visual cortex Two streams: Ventral What : V1,V2, V4, IT, form recognition and object representation Dorsal Where : V1,V2, MT, MST, LIP, VIP, 7a: motion, location, control of eyes and arms
More informationExpert System Profile
Expert System Profile GENERAL Domain: Medical Main General Function: Diagnosis System Name: INTERNIST-I/ CADUCEUS (or INTERNIST-II) Dates: 1970 s 1980 s Researchers: Ph.D. Harry Pople, M.D. Jack D. Myers
More informationTHE FUTURE HEALTH CARE SYSTEMS The case of Cardiovascular Medicine
THE FUTURE HEALTH CARE SYSTEMS The case of Cardiovascular Medicine Prof. Panos Ε. Vardas ESC Past President ESC/EHA Chief Strategy Advisor, BRUSSELS Disclosures Nothing to declare related to this presentation,
More informationLECTURE 5: REACTIVE AND HYBRID ARCHITECTURES
Reactive Architectures LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas There are many unsolved (some would say insoluble) problems
More informationNeurons and neural networks II. Hopfield network
Neurons and neural networks II. Hopfield network 1 Perceptron recap key ingredient: adaptivity of the system unsupervised vs supervised learning architecture for discrimination: single neuron perceptron
More informationA Smart Texting System For Android Mobile Users
A Smart Texting System For Android Mobile Users Pawan D. Mishra Harshwardhan N. Deshpande Navneet A. Agrawal Final year I.T Final year I.T J.D.I.E.T Yavatmal. J.D.I.E.T Yavatmal. Final year I.T J.D.I.E.T
More informationRecognition of English Characters Using Spiking Neural Networks
Recognition of English Characters Using Spiking Neural Networks Amjad J. Humaidi #1, Thaer M. Kadhim *2 Control and System Engineering, University of Technology, Iraq, Baghdad 1 601116@uotechnology.edu.iq
More informationAutomatic Definition of Planning Target Volume in Computer-Assisted Radiotherapy
Automatic Definition of Planning Target Volume in Computer-Assisted Radiotherapy Angelo Zizzari Department of Cybernetics, School of Systems Engineering The University of Reading, Whiteknights, PO Box
More informationImplementation of Perception Classification based on BDI Model using Bayesian Classifier
Implementation of Perception Classification based on BDI Model using Bayesian Classifier Vishwanath Y 1 Murali T S 2 Dr M.V Vijayakumar 3 1 Research Scholar, Dept. of Computer Science & Engineering, Jain
More informationBayesian Inference Bayes Laplace
Bayesian Inference Bayes Laplace Course objective The aim of this course is to introduce the modern approach to Bayesian statistics, emphasizing the computational aspects and the differences between the
More informationBehavior Architectures
Behavior Architectures 5 min reflection You ve read about two very different behavior architectures. What are the most significant functional/design differences between the two approaches? Are they compatible
More informationTEACHING YOUNG GROWNUPS HOW TO USE BAYESIAN NETWORKS.
TEACHING YOUNG GROWNUPS HOW TO USE BAYESIAN NETWORKS Stefan Krauss 1, Georg Bruckmaier 1 and Laura Martignon 2 1 Institute of Mathematics and Mathematics Education, University of Regensburg, Germany 2
More informationAgent-Based Systems. Agent-Based Systems. Michael Rovatsos. Lecture 5 Reactive and Hybrid Agent Architectures 1 / 19
Agent-Based Systems Michael Rovatsos mrovatso@inf.ed.ac.uk Lecture 5 Reactive and Hybrid Agent Architectures 1 / 19 Where are we? Last time... Practical reasoning agents The BDI architecture Intentions
More informationIntroduction. Patrick Breheny. January 10. The meaning of probability The Bayesian approach Preview of MCMC methods
Introduction Patrick Breheny January 10 Patrick Breheny BST 701: Bayesian Modeling in Biostatistics 1/25 Introductory example: Jane s twins Suppose you have a friend named Jane who is pregnant with twins
More informationOutline. Hierarchical Hidden Markov Models for HIV-Transmission Behavior Outcomes. Motivation. Why Hidden Markov Model? Why Hidden Markov Model?
Hierarchical Hidden Markov Models for HIV-Transmission Behavior Outcomes Li-Jung Liang Department of Medicine Statistics Core Email: liangl@ucla.edu Joint work with Rob Weiss & Scott Comulada Outline Motivation
More informationLearning to Use Episodic Memory
Learning to Use Episodic Memory Nicholas A. Gorski (ngorski@umich.edu) John E. Laird (laird@umich.edu) Computer Science & Engineering, University of Michigan 2260 Hayward St., Ann Arbor, MI 48109 USA Abstract
More informationArtificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks
Artificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks 1.Syed Khursheed ul Hasnain C Eng MIEE National University of Sciences & Technology, Pakistan
More informationData mining for Obstructive Sleep Apnea Detection. 18 October 2017 Konstantinos Nikolaidis
Data mining for Obstructive Sleep Apnea Detection 18 October 2017 Konstantinos Nikolaidis Introduction: What is Obstructive Sleep Apnea? Obstructive Sleep Apnea (OSA) is a relatively common sleep disorder
More informationGrounding Ontologies in the External World
Grounding Ontologies in the External World Antonio CHELLA University of Palermo and ICAR-CNR, Palermo antonio.chella@unipa.it Abstract. The paper discusses a case study of grounding an ontology in the
More informationComputational Cognitive Neuroscience
Computational Cognitive Neuroscience Computational Cognitive Neuroscience Computational Cognitive Neuroscience *Computer vision, *Pattern recognition, *Classification, *Picking the relevant information
More informationIntelligent Machines That Act Rationally. Hang Li Toutiao AI Lab
Intelligent Machines That Act Rationally Hang Li Toutiao AI Lab Four Definitions of Artificial Intelligence Building intelligent machines (i.e., intelligent computers) Thinking humanly Acting humanly Thinking
More informationarxiv: v1 [cs.ai] 5 Oct 2018 October 8, 2018
André Ofner Research Focus Cognitive Sciences University of Potsdam Potsdam, Germany ofner@uni-potsdam.de Sebastian Stober Artificial Intelligence Lab Otto von Guericke University Magdeburg, Germany stober@ovgu.de
More informationRolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press.
Digital Signal Processing and the Brain Is the brain a digital signal processor? Digital vs continuous signals Digital signals involve streams of binary encoded numbers The brain uses digital, all or none,
More information