Artificial Intelligence

Presented by Dr. Hayssam Serhan

Outline Overview of AI Neural Networks Fuzzy Logic Expert Systems R Language (Introduction)

AI Computing Caution: AI is NOT magic AI is a unique approach to programming computers Thinking or conscious computer, is still far off on the digital horizon

AI Objectives Making machines more useful by Making them SMARTER

Understanding intelligence shall be our First Goal

Intelligent Behavior Learn from experience Apply knowledge acquired from experience Handle complex situations Solve problems when important information is missing React quickly and correctly to a new situation Be creative and imaginative Use heuristics

Major Branches of AI Robotics & Perceptive Systems Mechanical and computer devices that perform tedious

tasks with high precision.

Games Playing programming computers to play games. The greatest

advances have occurred in the field of games playing.

Natural Language Processing (NLP) Computers understand and react to statements and

commands made in a “natural” language.

Major Branches of AI Expert System (ES) programming computers to make decisions in real-life

Neural Network Computer system that can act like or simulate the

functioning of the human brain. Unsupervised learning. Supervised learning.

Machine Learning Learning System Machine learning is the study of computer algorithms

that improve automatically through experience Computer changes how it functions or reacts to

situations based on feedback.

“A computer program is said to learn from experience E with respect to some task T and some performance

measure P, if its performance on T, as measured by P, improves with experience E”

Tom Mitchell (1998)

Human VS Artificial Intelligence - Pros

Human Intelligence

Intuition, Common sense, Judgment, Creativity, etc.

The ability to demonstrate their intelligence by communicating effectively

Reasoning and Critical thinking

Artificial Intelligence

Ability to simulate human behavior and cognitive processes

Capture and preserve human expertise

Fast Response.

Human VS Artificial Intelligence - Cons

Human Intelligence Humans are fallible

They have limited knowledge

Information processing of

serial nature proceed very slowly in the brain

Humans are unable to retain large amounts of data

Artificial Intelligence No "common sense" Cannot readily deal with "mixed" knowledge May have high development costs Raise legal and ethical concerns

Conventional Computing VS Artificial Intelligence

Artificial Intelligence

AI software uses the techniques of search and pattern matching

Programmers design AI software to give the computer only the problem, not the steps necessary to solve it

Conventional computing

Conventional computer software follow a logical series of steps to reach a conclusion

Computer programmers originally designed software that accomplished tasks by completing algorithms

Knowledge Representation & Limits

The number of atomic facts that the average person knows is astronomical. Building a complete knowledge base of commonsense requires enormous amounts of engineering. Much of what people know is not represented as "facts" that they could express verbally

Conclusion Intelligent Agents must be able to set goals and achieve them. They need a way to visualize the future and be able to make choices. Currently, no computers exhibit full artificial intelligence. Early AI researchers developed algorithms that require enormous computational resources. The search for more efficient problem-solving algorithms is a high priority for AI research.

Neural Networks Traditional computers cannot work around the failure of even a single transistor. With the biological designs, the algorithms are ever changing, allowing the system to continuously adapt and work around failures to complete tasks.

“We’re moving from engineering computing systems to something that

has many of the characteristics of biological computing”

Larry Smarr, an astrophysicist who directs the California Institute for

Telecommunications and Information Technology

“The new approach, used in both hardware and software, is being driven

by the explosion of scientific knowledge about the brain. But scientists are still far from fully

understanding how brains function” Kwabena Boahen,

a computer scientist who leads Stanford’s Brains in Silicon research program

“The largest class this fall at Stanford was a graduate level machine-learning course covering both statistical and biological

approaches, taught by the computer scientist Andrew Ng. More than 760 students enrolled”

“Everyone knows there is something big happening, and they’re trying find out what it is.”

Terry Sejnowski, a computational neuroscientist at the Salk Institute

Human Brain Movie

Nervous Systems Human brain contains ~ 1011 neurons. Each neuron is connected ~ 104 others. Neurons are slower than logic gates : 10-9 secs for semiconductors 10-3 secs for biologicals neurons

Energy efficiency of the brain is estimated at: 10-16 Joules / operation / sec, The best energy efficiency of computers : is

10-6 Joules / operation / sec

Nervous Systems it takes on average between 100 and 200 msec to recognize a familiar face, it takes days to process much simpler tasks with conventional computers Some scientists compared the brain with a “complex, nonlinear, parallel computer”.

IBM Supercomputer – Compass I.B.M. announced last year that it had built a supercomputer simulation of the brain (Compass) It encompassed roughly 10 billion neurons. It ran about 1,500 times more slowly than an actual brain. Further, it required several megawatts of power, compared with just 20 watts of power used by the biological brain. “attempting to simulate a brain, at the same speed would require a flow of electricity in a conventional

computer that is equivalent to what is needed to power both San Francisco and New York,”

Dr. Modha said

Google & DeepMind Google has acquired DeepMind for 400M$ DeepMind has not yet developed any commercial products. DeepMind main asset appears to be its personnel DeepMind claims that it combines “the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms.”

Google & AI Google researchers were able to get a machine-learning algorithm based on neural networks, to perform an identification task. The network scanned a database of 10 million images, and in doing so trained itself to recognize cats In June, Google said it had used those neural network techniques to develop a new search service to help customers find specific photos more accurately

Applications

Pattern classification Object recognition Function approximation Data compression Time series analysis and forecast . . .

Neurons

The main purpose of neurons is to receive, analyze and transmit further the information in a form of signals (electric pulses). When a neuron sends the information we say that a neuron “fires”.

Structure of a Biological Neuron

Artificial Neuron

Artificial Multilayer Neural Network

Artificial Neural Networks Movie

Multilayer Perceptron

. . .

. . .

. . .

. . .

x1 x2 xm

y1 y2 yn

Hidden Layer

Input Layer

Output Layer

Knowledge and Memory

. . .

. . .

. . .

. . .

x1 x2 xm

y1 y2 yn

The output behavior of a network is determined by the weights. Weights the memory of an NN. Knowledge distributed across the network. Large number of nodes increases the storage “capacity”; ensures that the knowledge is

robust; fault tolerance.

Store new information by changing weights.

Exp.: Pattern Classification

. . .

. . .

. . .

. . .

x1 x2 xm

y1 y2 yn

Function: x y

The NN’s output is used to distinguish between and recognize different input patterns. Different output patterns correspond to particular classes of input patterns. Networks with hidden layers can be used for solving more complex problems then just a linear pattern classification.

input pattern x

output pattern y

Neural Networks Learning Rules Learning Rules for Multiple-Layered Perceptron Networks

Supervised Learning Goals The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to its correct output. An example would be a simple classification task, where the input is an image of an animal (or the characteristics of this animal), and the correct output would be the name of the animal.

Training Neural Network: Back-Propagation

Supervised learning method, Requires a dataset of the desired output for many inputs, making up the training set, Backpropagation requires that the activation function used by the artificial neurons (or "nodes") be differentiable.

A multi-layered network can create internal representations and learn different features per layer. The first layer may be responsible for learning the orientations of lines using the inputs from the individual pixels in the image. The second layer may combine the features learned in the first layer and learn to identify simple shapes. Each higher layer learns more and more abstract features that can be used to classify the image. Each layer finds patterns in the layer below it and it is this ability to create internal representations that are independent of outside input that gives multi-layered networks its power.

Motivation

Backpropagation Learning Algo. The learning algorithm can be divided into two phases: Phase 1: Propagation

Forward propagation of a training pattern's input through the neural network in order to generate the propagation's output activations.

Backward propagation of the propagation's output activations through the neural network using the training pattern target in order to generate the deltas of all output and hidden neurons.

Phase 2: Weight update Subtract a ratio (percentage) of the gradient from the weight. This ratio (percentage) influences the speed and quality of

learning; it is called the learning rate. The greater the ratio, the faster the neuron trains; the lower the ratio, the more accurate the training is.

Algorithm initialize network weights (often small random values) do forEach training example ex prediction = neural-net-output(network, ex) // forward pass actual = teacher-output(ex) compute error (prediction - actual) at the output units compute for all weights from output layer to hidden layer // backward pass compute for all weights from hidden layer to input layer // backward pass continued update network weights until all examples classified correctly or another stopping criterion satisfied return the network

Neural Network: Simulation

Neuromorphic Processors Those new processors consist of electronic components that can be connected by wires that mimic biological synapses. They are based on large groups of neuron-like elements, and known as neuromorphic processors, They are not “programmed.” The connections between circuits are “weighted” according to correlations in data that the processor has already “learned.” Those weights are then altered as data flows in to the chip, causing them to change their values and to “spike.” That generates a signal that travels to other components and, in reaction, changes the neural network,

Conclusion Neural Network technology offers more natural interaction with the real world. Neural Networks can: learn and adapt to changes in a problem’s environment, establish patterns in situations where rules are not known, deal with fuzzy or incomplete information.

However, they lack explanation facilities and usually act as a black box. The process of training neural networks with current technologies is still slow.

Motion and manipulation: Robotics The field of robotics is closely related to AI.

Motion and Manipulation: Robotics

Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of: localization mapping and motion

Robot Quick Description

Each Leg consists of 7 DOFs 3 DOFs – Active for the HIP 1 DOFs – Active for the KNEE 2 DOFs – Active for the ANKLE 1 DOFs – Passive for the FOOT

Robot Control Algorithm

Université de Versailles St Quentin

Neural Network A More Complicated Design (Muscle Modelling)

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Learning with plant Identification

Université de Versailles St Quentin – Université Libanaise

Extension

Extension

`Plantarflexion

Robot: Walking – Movies & Stability

Fuzzy Logic

Very important technology dealing with vague, imprecise and uncertain knowledge and data

Fuzzy Logic Fuzzy logic or fuzzy set theory was introduced by Professor Lotfi Zadeh Human experts do not usually think in probability values, but in such terms as often, generally, sometimes, occasionally and rarely. At the heart of fuzzy logic lies the concept of a linguistic variable Linguistic variables are words rather than numbers Fuzzy logic provides the way to break through the computational bottlenecks of traditional expert systems. Eventually, fuzzy theory, ignored in the West, was taken seriously in the East – by the Japanese

Fuzzy Logic: Motivation Modeling of imprecise concepts: Age, Weight, Height, …

Modeling of imprecise dependencies: If Temperature is low and Oil is cheap then

crank up the heating system Origin of Information: Modeling of Expert Knowledge Representation of information extracted from

inherently imprecise data

Characteristic Functions: Crisp Sets

Classical Sets can be described by a characteristic function:

Example: A = {x | a ≤ x ≤ b}

Characteristic Functions: Fuzzy Sets

Fuzzy Sets are described by a membership function: Example:

Linguistic Variables and Values

Linguistic values & Context

Fuzzy Rule System

Basic Elements of a Fuzzy Logic System

2- Fuzzy-Inference

1- Fuzzification 3- Defuzzification

Linguistic Level

Numerical Level

Fuzzy Rule Systems: Example 1

Center Of gravity

Application of Fuzzy Logic

Term Definitions: Distance:= {far, medium, close, zero, neg_close} Angle := {pos_big, pos_small, zero, neg_small, neg_big} Power := {pos_high, pos_medium, zero, neg_medium, neg_high}

1. Fuzzification: - Linguistic Variables -

Membership Function Definition:

-90° -45° 0° 45° 90°0

1

µ

Angle

zero

pos_smallneg_smallneg_big pos_big

4°

0.8

0.2

-10 0 10 20 300

1

µ

Distance [yards]

zero close medium farneg_close

12m

0.9

0.1

Computation of the “IF-THEN”-Rules: #1: IF Distance = medium AND Angle = pos_small THEN Power = pos_medium #2: IF Distance = medium AND Angle = zero THEN Power = zero #3: IF Distance = far AND Angle = zero THEN Power = pos_medium #4: …….

2. Fuzzy-Inference: “IF-THEN”-Rules

Aggregation: Computing the “IF”-Part Composition: Computing the “THEN”-Part

The Rules of the Fuzzy Logic Systems Are the “Laws” It Executes !

2. Fuzzy-Inference: Composition Result for the Linguistic Variable "Power": pos_high with the degree 0.0 pos_medium with the degree 0.8 ( = max{ 0.8, 0.1 } ) zero with the degree 0.2 neg_medium with the degree 0.0 neg_high with the degree 0.0

Composition Computes How Each Rule Influences the Output Variables !

3. Defuzzification Finding a Compromise Using “Center-of-Maximum”:

-30 -15 0 15 300

1

µ

Power [Kilowatts]

zeroneg_mediumneg_high pos_medium pos_high

6.4 KW

“Balancing” Out the Result !

Fuzzy Logic: Simulation

Improved Computational Power Fuzzy rule-based systems perform faster than conventional expert systems Fuzzy Systems require fewer rules. A fuzzy expert system merges the rules, making them more powerful. Lotfi Zadeh believes that in a few years most expert systems will use fuzzy logic to solve highly nonlinear and computationally difficult problems.

Summary Fuzzy systems allow expression of expert knowledge in a more natural way, they still depend on the rules extracted from the experts, and thus might be smart or dumb. Some experts can provide very clever fuzzy rules – but some just guess and may even get them wrong. Therefore, all rules must be tested and tuned, which can be a prolonged and tedious process. It took Hitachi engineers several years to test and tune only 54 fuzzy rules to guide the Sendal Subway System.

Expert Systems

An expert system is a computer program that is designed to hold the accumulated knowledge of one or more domain experts

ES imitate the expert’s reasoning processes to solve specific problems

Overview of Expert Systems Can… Explain their reasoning or suggested decisions Display intelligent behavior Draw conclusions from complex relationships Provide portable knowledge

Expert system shell A collection of software packages and tools used

to develop expert systems

IBM & Expert Systems It has been two years since Watson, the artificial intelligence program created by I.B.M.. Watson, Watson has access to roughly 200 million pages of information, and is able to understand natural language queries and answer questions. The computer maker had initially planned to test the system as an expert adviser to doctors; the idea was that Watson’s encyclopedic knowledge of medical conditions could aid a human expert in diagnosing illnesses.

IBM & Watson In May, I.B.M. announced a general-purpose version of its software, the “I.B.M. Watson Engagement Advisor.” The idea is to make the company’s question-answering system available in a wide range of call center, technical support and telephone sales applications. The company says that as many as 61 percent of all telephone support calls currently fail because human support-center employees are unable to give people correct or complete information.

When to Use an Expert System

Capture and preserve irreplaceable human expertise Provide expertise needed at a number of locations at the same time Provide expertise needed in a hostile environment that is dangerous to human health Provide expertise that is expensive or rare Develop a solution faster than human experts Provide a high potential payoff or significantly reduced downside risk

Limitations of Expert Systems Limited to relatively narrow problems

May have high development costs

May raise legal and ethical concerns

Cannot readily deal with “mixed” knowledge

Possibility of error

Difficult to maintain

Legal and Ethical Issues Who is responsible if the advice is wrong? The user? The domain expert? The knowledge engineer? The programmer of the expert system shell? The company selling the software?

Transferring Expertise Objective of an expert system To transfer expertise from an expert to a

computer system and Then on to other humans (nonexperts)

Activities Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer to the user

Knowledge is stored in a knowledge base

An Expert System Example General Electric's (GE) : Top Locomotive Field Service Engineer was

Nearing Retirement Traditional Solution: Apprenticeship but would like A more effective and dependable way to disseminate expertise To prevent valuable knowledge from retiring To minimize extensive travel or moving the locomotives

To MODEL the way a human troubleshooter works Months of knowledge acquisition 3 years of prototyping

A novice engineer or technician can perform at an expert’s level On a personal computer Installed at every railroad repair shop served by GE

Participants in Expert Systems

Domain expert The individual or group whose expertise and

knowledge is captured for use in an expert system Knowledge user The individual or group who uses and benefits from

the expert system Knowledge engineer Someone trained or experienced in the design,

development, implementation, and maintenance of an expert system

Determining requirements

Identifying experts

Construct expert system components

Implementing results

Maintaining and reviewing system

Expert Systems Development

Domain • The area of knowledge

addressed by the expert system.

Inference engine

Explanation facility

Knowledge base

acquisition facility

User interface

Knowledge base

Experts User

Expert System Components

Evolution of Expert Systems Software

Expert system shell Collection of software packages & tools to design,

develop, implement, and maintain expert systems

Ease

of u

se

low

high

Before 1980 1980s 1990s

Traditional programming languages

Special and 4th generation languages

Expert system shells

Expert Systems Shells Software Development Packages

Exsys InstantTea K-Vision KnowledgePro

Applications of Expert Systems

PROSPECTOR: Used by geologists to identify sites for drilling or mining

PUFF: Medical system for diagnosis of

respiratory conditions

Applications of Expert Systems

DESIGN ADVISOR: Gives advice to

designers of processor chips

MYCIN: Medical system for

diagnosing blood disorders. First used in 1979

Applications of Expert Systems

DENDRAL: Used to identify the structure of chemical compounds.

First used in 1965

LITHIAN: Gives advice to archaeologists

examining stone tools

Expert Systems Development Alternatives

low

high

low high

Development costs

Time to develop expert system

Use existing package

Develop from shell

Develop from

scratch

Expert Systems Benefits Enhancement of Problem Solving and Decision Making

Improved Product and Decision Quality

Increased Output and Productivity

Decreased Decision Making Time

Capture Scarce Expertise

Can Work with Incomplete or Uncertain Information

Knowledge Transfer to Remote Locations

Problems and Limitations of Expert Systems

Domain experts not always able to explain their logic and reasoning ES work well only in a narrow domain of knowledge Knowledge engineers are rare and expensive Expert system users have natural cognitive limits Lack of trust by end-users ES may not be able to arrive at valid conclusions ES may sometimes produce incorrect recommendations Lacks common sense Cannot make creative responses as human expert Cannot adapt to changing environments

Conclusion Classic expert systems are especially good for closed-system applications with precise inputs and logical outputs. They use expert knowledge in the form of rules and, if required, can interact with the user to establish a particular fact. A major drawback is that human experts cannot always express their knowledge in terms of rules or explain the line of their reasoning. This can prevent the expert system from accumulating the necessary knowledge, and consequently lead to its failure.

Summary Expert, neural and fuzzy systems have now matured and been applied to a broad range of different problems, mainly in engineering, medicine, finance, business and management. Each technology handles the uncertainty and ambiguity of human knowledge differently, and each technology has found its place in knowledge engineering. They no longer compete; rather they complement each other. A synergy of expert systems with fuzzy logic and neural computing improves adaptability, robustness, fault-tolerance and speed of knowledge-based systems. Besides, computing with words makes them more “human”.

R Language Statistic analysis on the fly Mathematical function and graphic module embedded FREE! & Open Source!

R Tops Data Mining Software Poll

For the past 12 years, KDNuggets has conducted an annual poll asking "What analytics/data mining software you used in the past 12 months for a real project (not just evaluation)". In this year's poll, R was the top-ranked data mining solution, selected by 30.7% of poll respondents. Microsoft Excel was second, at 29.8%. Rapidminer, which took the #1 spot over R in 2011 and 2010, ranked third. And as Bob Muenchen notes, four of the top five ranked data mining solutions in this year's poll are open-source. R was also ranked in this poll as the most popular language for implementing data mining application, beating out SQL and Java.

Important Problems in Data Mining

Prediction Finding patterns (Apriori) Clustering Classification

Regression Ranking Density Estimation

Prediction For most of the following algorithms (as well as linear regression), we would in practice first generate the model using training data, and then predict values for test data. To make predictions, we use the predict function. Typically, the first argument is the variable in which you saved the model, and the second argument is a matrix or data frame of test data. For instance, if we were to predict for the linear regression model above, and x1 test and x2 test are vectors containing test data, we can use the command

>predicted_values<-predict(lm_model, newdata=as.data.frame(cbind(x1_test, x2_test)))

Finding patterns (Apriori) In large datasets -e.g. (Diapers → Beer). Use Apriori! To run the Apriori algorithm, first install the arules package and load it. Note that the dataset must be a binary incidence matrix; the column names should correspond to the “items” that make up the “transactions.” The following commands print out a summary of the results and a list of the generated rules.

> dataset <-read.csv("C:\\Datasets\\mushroom.csv", header = TRUE) > mushroom_rules <-apriori(as.matrix(dataset), parameter = list(supp = 0.8, conf = 0.9)) > summary(mushroom_rules) > inspect(mushroom_rules)

Clustering grouping data into clusters that “belong” together -objects within a cluster are more similar to each other than to those in other clusters. Kmeans, Kmedians Input: {xi}mi=1,xi ∈X ⊂ Rn Output: f : X →{1,...,K} (K clusters) clustering consumers for market research, clustering genes into families, image segmentation (medical imaging) If X is the data matrix and m is the number of clusters, then the command is:

> kmeans_model <-kmeans(x=X, centers=m)

Classification

Input: {(xi,yi)}m “examples,” “instances with labels,” “observations” xi ∈X,yi ∈ {−1, 1} “binary” Let X train and X test be matrices of the training and test data respectively, and labels be a binary vector of class attributes for the training examples. For k equal to K, the command is: > knn_model <-knn(train=X_train, test=X_test, cl=as.factor(labels), k=K)

automatic handwriting recognition, speech recognition, biometrics, document classification

Identifying to which of a set of categories a new observation belongs, on the basis of a training set of data.

Decision trees: rpart, party Random forest: randomForest, party SVM: e1071, kernlab Neural networks: nnet, neuralnet, RSNNS Performance evaluation: ROCR

Regression Input: {(xi,yi)}mi=1, xi ∈X,yi ∈ R Output: f : X→ R predicting an individual’s income, predict house prices, predict stock prices, predict test scores the command is: > glm_mod <-glm(y ∼ x1+x2, family=binomial(link="logit"), data=as.data.frame(cbind(y,x1,x2)))

Ranking in between classification and regression. Search engines use ranking methods

Density Estimation predict conditional probabilities {(xi,yi)}mi=1, xi ∈X,yi ∈ {−1, 1} Output: f : X→ [0, 1] as “close” to P(y =1|x) as possible. estimate probability of failure, probability to default on loan

Training and Testing for supervised learning

Training: training data are input, and model f is the output Testing: You want to predict y for a new x, where (x, y) comes from the same distribution as Compute f(x) and compare it to y. How well does f(x) match y? Measure goodness of f using a loss function Rtest(f) Rtest is also called the true risk or the test error We want Rtest to be small, to indicate that f(x) would be a good predictor (“estimator”) of y called the true risk or the test error

Time series decomposition: decomp(), decompose(), arima(), stl()

Time series forecasting: forecast Time Series Clustering: TSclust Dynamic Time Warping (DTW): dtw

Time Series Analysis with R

Packages: igraph, sna Centrality measures: degree(), betweenness(),

closeness(), transitivity() Clusters: clusters(), no.clusters() Cliques: cliques(), largest.cliques(), maximal.cliques(),

clique.number() Community detection: fastgreedy.community(),

spinglass.community()

Social Network Analysis with R

Scatter plot dataset <-read.csv ('fbgood.txt',head=TRUE, sep='t', row.names=1) x = dataset$friends y = dataset$getgoods plot(x,y)

Linear Fit fit <- lm(y ~ x); abline(fit, col = 'red', lwd=3)

2nd order polynomial fit plot(x,y) polyfit2 <- lm(y ~ poly(x, 2)); lines(sort(x), polyfit2$fit[order(x)], col = 2, lwd = 3)

3rd order polynomial fit plot(x,y) polyfit3 <- lm(y ~ poly(x, 3)); lines(sort(x), polyfit3$fit[order(x)], col = 2, lwd = 3)

Packages: RHadoop, RHive RHadoop10 is a collection of 3 R packages:

rmr2 - perform data analysis with R via MapReduce on a Hadoop cluster

rhdfs - connect to Hadoop Distributed File System (HDFS) rhbase - connect to the NoSQL HBase database

You can play with it on a single PC (in standalone or pseudo-distributed mode), and your code developed on that will be able to work on a cluster of PCs (in full-distributed mode)!

Step by step to set up my first R Hadoop system http://www.rdatamining.com/tutorials/rhadoop

¹⁰https://github.com/RevolutionAnalytics/RHadoop/wiki

R and Hadoop

An Example of MapReducing with R

library(rmr2) map <- function(k, lines) { words.list <- strsplit(lines, "s") words <- unlist(words.list) return(keyval(words, 1)) } reduce <- function(word, counts) { keyval(word, sum(counts)) } wordcount <- function(input, output = NULL) { mapreduce(input = input, output = output, input.format = "text", map = map, reduce = reduce) } ## Submit job out <- wordcount(in.file.path, out.file.path)

Thank you for your time !

Email: hserhan@hotmail.com

THE END