Pedro Domingos University of Washington. Evolution Experience Culture.

Slides:

Advertisements

Similar presentations

Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California

Advertisements

Markov Logic Networks Instructor: Pedro Domingos.

4 Intelligent Systems.

Research Areas S. E. Shimony Artificial intelligence and applications. Probabilistic reasoning. Knowledge discovery and data-mining. Meta-reasoning ( “

Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)

Markov Logic: A Unifying Framework for Statistical Relational Learning Pedro Domingos Matthew Richardson

Speaker:Benedict Fehringer Seminar:Probabilistic Models for Information Extraction by Dr. Martin Theobald and Maximilian Dylla Based on Richards, M., and.

1 Lecture 35 Brief Introduction to Main AI Areas (cont’d) Overview  Lecture Objective: Present the General Ideas on the AI Branches Below  Introduction.

APRIL, Application of Probabilistic Inductive Logic Programming, IST Albert-Ludwigs-University, Freiburg, Germany & Imperial College of Science,

CSCE101 –Chapter 8 (continued) Tuesday, December 5, 2006.

Improved Crowd Control Utilizing a Distributed Genetic Algorithm John Chaloupek December 3 rd, 2003.

Recursive Random Fields Daniel Lowd University of Washington June 29th, 2006 (Joint work with Pedro Domingos)

EECS 349 Machine Learning Instructor: Doug Downey Note: slides adapted from Pedro Domingos, University of Washington, CSE

Big Ideas in Cmput366. Search Blind Search Iterative deepening Heuristic Search A* Local and Stochastic Search Randomized algorithm Constraint satisfaction.

AI - Week 24 Uncertain Reasoning (quick mention) then REVISION Lee McCluskey, room 2/07

CSE 546 Data Mining Machine Learning Instructor: Pedro Domingos.

CSE 574: Artificial Intelligence II Statistical Relational Learning Instructor: Pedro Domingos.

Soft Computing 1 Neuro-Fuzzy and Soft Computing chapter 1 J.-S.R. Jang Bill Cheetham Kai Goebel.

Random Administrivia In CMC 306 on Monday for LISP lab.

Introduction to Computational Intelligence (Evolutionary Computation) Evolutionary Computation is the field of study devoted to the design, development,

CSE 590ST Statistical Methods in Computer Science Instructor: Pedro Domingos.

Statistical Relational Learning Pedro Domingos Dept. Computer Science & Eng. University of Washington.

Genetic Programming. Agenda What is Genetic Programming? Background/History. Why Genetic Programming? How Genetic Principles are Applied. Examples of.

CSE 515 Statistical Methods in Computer Science Instructor: Pedro Domingos.

A Computational Semiotics Approach for Soft Computing Ricardo R. Gudwin Fernando A.C. Gomide DCA-FEEC-UNICAMP.

Decision-Making on Robots Using POMDPs and Answer Set Programming Introduction Robots are an integral part of many sectors such as medicine, disaster rescue.

Department of Information Technology Indian Institute of Information Technology and Management Gwalior AASF hIQ 1 st Nov ‘09 Department of Information.

How We’re Going to Solve the AI Problem Pedro Domingos Dept. Computer Science & Eng. University of Washington.

Hybrid AI & Machine Learning Systems Using Ne ural Network and Subsumption Architecture Libraries By Logan Kearsley.

An Introduction to Artificial Intelligence and Knowledge Engineering N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering,

10/3/2015 ARTIFICIAL INTELLIGENCE Russell and Norvig ARTIFICIAL INTELLIGENCE: A Modern Approach.

Bayesian Learning By Porchelvi Vijayakumar. Cognitive Science Current Problem: How do children learn and how do they get it right?

Probability and Statistics Required!. 2 Review Outline  Connection to simulation.  Concepts to review.  Assess your understanding.  Addressing knowledge.

Ahsanul Haque *, Swarup Chandra *, Latifur Khan * and Michael Baron + * Department of Computer Science, University of Texas at Dallas + Department of Mathematical.

Lecture 8: 24/5/1435 Genetic Algorithms Lecturer/ Kawther Abas 363CS – Artificial Intelligence.

Artificial Intelligence And Machine learning. Drag picture to placeholder or click icon to add What is AI?

Fundamentals of Information Systems, Third Edition2 Principles and Learning Objectives Artificial intelligence systems form a broad and diverse set of.

Machine Learning.

Laboratory for Computational Intelligence, University of British Columbia Belief & Decision Networks Stochastic Local Search Neural NetworksGraph Searching.

Artificial Intelligence By Michelle Witcofsky And Evan Flanagan.

Markov Logic and Deep Networks Pedro Domingos Dept. of Computer Science & Eng. University of Washington.

1 2010/2011 Semester 2 Introduction: Chapter 1 ARTIFICIAL INTELLIGENCE.

Game Theory, Social Interactions and Artificial Intelligence Supervisor: Philip Sterne Supervisee: John Richter.

Definitions of AI There are as many definitions as there are practitioners. How would you define it? What is important for a system to be intelligent?

Pedro Domingos University of Washington. Traditional Programming Machine Learning Computer Data Algorithm Output Computer Data Output Algorithm.

Instructor: Pedro Domingos

CSE & CSE6002E - Soft Computing Winter Semester, 2011 Course Review.

Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.

Organic Evolution and Problem Solving Je-Gun Joung.

Hierarchical Mixture of Experts Presented by Qi An Machine learning reading group Duke University 07/15/2005.

Dinner for Two. Fuzzify Inputs Apply Fuzzy Operator.

Chapter 12 Case Studies Part B. Control System Design.

Who am I? Work in Probabilistic Machine Learning Like to teach 

Instructor: Pedro Domingos

PART IV: The Potential of Algorithmic Machines.

School of Computer Science & Engineering

TECHNOLOGY GUIDE FOUR Intelligent Systems.

Machine Learning for dotNET Developer Bahrudin Hrnjica, MVP

CSEP 546 Data Mining Machine Learning

First work in AI 1943 The name “Artificial Intelligence” coined 1956

CSEP 546 Data Mining Machine Learning

Basic Intro Tutorial on Machine Learning and Data Mining

Artificial Intelligence introduction(2)

CSE 515 Statistical Methods in Computer Science

CSEP 546 Data Mining Machine Learning

Leading in an AI World Stephanie Beben Chief Technologist, Data Scientist, Booz Allen Hamilton March 2018.

Future of Artificial Intelligence

Introduction to Artificial Intelligence Instructor: Dr. Eduardo Urbina

Artificial Intelligence Machine Learning

Optimization under Uncertainty

Presentation transcript:

Pedro Domingos University of Washington

Evolution Experience Culture

Evolution Experience CultureComputers

Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines. – Yann LeCun, Director of AI Research, Facebook

Traditional Programming Machine Learning Computer Data Algorithm Output Computer Data Output Algorithm

1. Fill in gaps in existing knowledge 2. Emulate the brain 3. Simulate evolution 4. Systematically reduce uncertainty 5. Notice similarities between old and new

TribeOriginsMaster Algorithm SymbolistsLogic, philosophyInverse deduction ConnectionistsNeuroscienceBackpropagation EvolutionariesEvolutionary biologyGenetic programming BayesiansStatisticsProbabilistic inference AnalogizersPsychologyKernel machines

Tom MitchellSteve MuggletonRoss Quinlan

AdditionSubtraction ――― = ? ―― 2 + ? ――― = 4 ――

Deduction Socrates is human + Humans are mortal. ――――――――――― = ? Induction Socrates is human + ? ――――――――――― = Socrates is mortal ――――――――――

Yann LeCunGeoff HintonYoshua Bengio

John Koza John HollandHod Lipson

David HeckermanJudea PearlMichael Jordan

Peter HartVladimir VapnikDouglas Hofstadter

TribeProblemSolution SymbolistsKnowledge compositionInverse deduction ConnectionistsCredit assignmentBackpropagation EvolutionariesStructure discoveryGenetic programming BayesiansUncertaintyProbabilistic inference AnalogizersSimilarityKernel machines

TribeProblemSolution SymbolistsKnowledge compositionInverse deduction ConnectionistsCredit assignmentBackpropagation EvolutionariesStructure discoveryGenetic programming BayesiansUncertaintyProbabilistic inference AnalogizersSimilarityKernel machines But what we really need is a single algorithm that solves all five!

Representation Probabilistic logic (e.g., Markov logic networks) Weighted formulas → Distribution over states Evaluation Posterior probability User-defined objective function Optimization Formula discovery: Genetic programming Weight learning: Backpropagation

Much remains to be done... We need your ideas

Home Robots Cancer Cures360 o Recommenders World Wide Brains

If we used all our technology resources, we could actually give people personalized recommendations for every step of your life. – Aneesh Chopra, former CTO of the U.S.