Artificial intelligence

Presented by:-

Nitesh Kumar Singh

Artificial Intelligence

Intelligence

Intelligence is the ability to learn about, to learn from, to understand about and interact with one’s environment.

Intelligence is the faculty of understanding.

Make sense out of ambiguous or contradictory messages.

Respond quickly and successfully to new situations.

Use reasoning to solve problems.

Intelligence is not to make no mistakes but quickly to understand how to make them good

(German Poet)

Artificial Intelligence

A branch of computer science whose goal is the design of

machines that have attributes associated with human intelligence, such as learning, reasoning, vision, understanding speech, and, ultimately, consciousness.

Computer software that can mimic the learning capability of a human. The use of programs to enable machines to perform tasks which humans perform using their intelligence.

The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology.

Artificial Intelligence

Examples: Defeating the best

human chess players. Driving hundreds of

miles through the desert unaided.

Chatting in internet chat-rooms.

Examining x-rays for tumors.

Artificial Intelligence: How it started?

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behaviour). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match. In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily. Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought.

Artificial Intelligence: How it started?

There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing;

they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly. In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog.

Foundations of AI

▪Foundation of AI is based on▪Mathematics▪Neuroscience▪Control Theory▪Linguistics

Foundations - Mathematics

More formal logical methods▪Boolean logic ▪Fuzzy logic

Uncertainty▪The basis for most modern approaches to

handle uncertainty in AI applications can be handled by Probability theory Modal and Temporal logics

Foundations - Neuroscience

How do the brain works? Early studies (1824) relied on injured and abnormal

people to understand what parts of brain work More recent studies use accurate sensors to correlate

brain activity to human thought▪ By monitoring individual neurons, monkeys can now control a

computer mouse using thought alone

Moore’s law states that computers will have as many gates as humans have neurons in 2020

How close are we to have a mechanical brain?▪ Parallel computation, remapping, interconnections,….

Foundations – Control Theory

Machines can modify their behavior in response to the environment (sense/action loop)▪ Water-flow regulator, steam engine governor,

thermostat The theory of stable feedback systems (1894)▪ Build systems that transition from initial

state to goal state with minimum energy▪ In 1950, control theory could only describe

linear systems and AI largely rose as aresponse to this shortcoming

Foundations - Linguistics

Speech demonstrates so much of human intelligence Analysis of human language reveals thought taking

place in ways not understood in other settings▪ Children can create sentences they have never heard

before▪ Language and thought are believed to be tightly

intertwined

AI Advantages Over Natural Intelligence

More permanent Ease of duplication and dissemination Less expensive Consistent and thorough Can be documented Can execute certain tasks much faster than a

human can Can perform certain tasks better than many or

even most people

Natural Intelligence Advantages over AI

Natural intelligence is creative People use sensory experience directly Can use a wide context of experience in

different situations

AI - Very Narrow Focus

Artificialintelligence

Robotics

Visionsystems

Learningsystems

Natural languageprocessing

Neural networks

Expert systems

Artificial Intelligence

Artificial intelligence includes :

Games playing: programming computers to play games such as chess and checkers.

Expert systems : programming computers to make decisions in real-life situations. (for example, some expert systems help doctors diagnose diseases based on symptoms)

Natural language : programming computers to understand natural human languages.

Neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains.

Robotics : programming computers to see and hear and react to other sensory stimuli.

Intelligence: Artificial or Not?

Alan Turing (1912 - 1954) Proposed a test - Turing’s

Imitation Game▪ Tests the intelligence of the

computer. Phase 1: ▪ Man and woman separated from

an interrogator.▪ The interrogator types in a

question to either party.▪ By observing responses, the

interrogator’s goal was to identify which was the man and which was the woman.

Interrogator

Honest Woman Lying Man

Intelligence: Artificial or Not?

Phase 2 of the Turing’s test: The man was replaced

by the computer. If the computer could

fool the interrogator as often as the person did, it could be said that the computer had displayed intelligence.

Interrogator

Honest Woman Computer

Expert Systems

According to Darlington:

“An expert system is a program that attempts to mimic human expertise by applying inference methods to a specific body of knowledge.”

The term expert system is used in a seminal paper by Alan Turing in 1937 related to a study in AI.

An Expert System (ES) is a computer program that reasons using knowledge to solve complex problems.

Traditionally, computers solve complex problems by arithmetic calculations; and the knowledge to solve the problem is only known by the human programmer.

Expert Systems

ES's are: 1. Open to inspection, both in presenting intermediate steps

and in answering questions about the solution process. 2. Easily modified, both in adding and deleting skills from

the knowledge base. 3. Heuristic, in using knowledge to obtain solutions

Development of Expert Systems will allow us not only to provide very powerful technical capabilities but also to further nurture our own understanding of human thought process.

Structure of an Expert System

An ES will normally have two aspects: A development environment A consultation environment

The former is used by the system builder to modify the system. The later is used by the non-expert to obtain knowledge or advice.

It is the latter which is thought of as an ES.

Components of an Expert System

An ES is a program with various components:

1. Knowledge acquisition subsystem2. Knowledge base3. Inference engine4. User interface5. Explanation subsystem6. Blackboard7. Knowledge refinement subsystem

Inferenceengine

Explanationfacility

Knowledgebase

acquisitionfacility

Userinterface

Knowledgebase

Experts User

Block Diagram Of an Expert System

The User Interface

An ES may obtain input from an online data source (database, text file, web page, etc).

An ES may be used to monitor a physical system, in which case input may come directly from sensing devices.

An ES may be used to control a physical system, in which case output will be signals to the system.

When interacting with humans, standard HCI (Human-Computer Interaction) concerns apply.

Knowledge-base

The power of problem solving is primarily the consequence of the knowledge base and secondarily on the inference method employed.

A storehouse of knowledge primitives. The design of knowledge representation scheme impacts the design of the inference engine, the knowledge updating process, the explanation process and the overall efficiency of the system.

Therefore the selection of the knowledge representation scheme is one of the most critical decision in ES design.

Knowledge update is done either : 1. Manual

by the knowledge engineer domain expert

2. Machine learning

Inference Engine

The inference engine controls the reasoning involved when the system is run.

It has its own mechanism for interpreting the stored knowledge (in the appropriate form), and for sequencing the steps involved in reaching conclusions.

Inference here means any of the methods by which the system reaches conclusions.

Facts: All animals breathe oxygen.

All dogs are animals. Infer: All dogs breathe oxygen.

Explanation System

If the user is to have confidence in the output from an ES, it will be important for the ES to have ways of explaining how its conclusions were arrived at.

It will be useful to allow the user to ask. In response to a question from the ES:

WHY (did you ask that question)? After a conclusion has been presented:

HOW (did you reach that conclusion)?

Blackboard

This just means a place where temporary working may be stored, where it is accessible to various component parts of a large ES.

This may include, for example, a (dynamic) ‘agenda’ --- a list of tasks to be done (by the ES).

It may also include a list of intermediate conclusions, or results of searches, in order to avoid duplication of effort.

Not all ES will use (or need) a blackboard.

Knowledge Refinement Subsystem

Knowledge refinement means analyzing experience and adjusting the body of stored knowledge as a result. People do this all the time, and a good ES can do it too.

This may consist merely of saving previous results for future reference, to avoid repeating searches or computations.

OR it may involve feedback from the user, e.g.You (the ES) gave me this advice and it was BAD/GOOD

Capabilities of Expert Systems

Strategic goal setting

Decision making

Planning

Design

Quality control and monitoring

Diagnosis

Explore impact of strategic goals

Impact of plans on resources

Integrate general design principles and manufacturing limitations

Provide advise on decisions

Monitor quality and assist in finding solutions

Look for causes and suggest solutions

Benefits of expert systems

Scarce expertise made available. Integration of expertise from different sources. Improved quality (e.g. where an ES assists in design). Ability to work with incomplete information. Reduced system downtime (ES monitors or finds

faults). Training (users gain expertise from the ES). Makes expertise available in remote locations. ES can work faster than people. Reliability (ES will not get tired or bored).

Problems with expert systems

Expert systems are difficult and expensive to develop and maintain.

Like all software, ES may contain errors. But unlike other software systems, ES may be designed to cope with incomplete or inconsistent information.

If an ES gives a wrong conclusion, it may be difficult to know whether this was caused by an error in the system or by an error in the information given to it.

ES are designed to be used by non-experts. As above, they are designed not to fail, so errors may show only in wrong conclusions, and a user without expertise may not be in a position to recognize a wrong conclusion.

Applications of Expert systems

PUFF:Medical system

for diagnosis of respiratory conditions

PROSPECTOR:Used by geologists to identify sites for drilling

or mining

Artificial Neural Networks

If an intelligent agent is supposed to behave like a human being, it may need to learn. Learning is a complex biological phenomenon that is not even totally understood in humans. Enabling an artificial intelligence agent to learn is definitely not an easy task. However, several methods have been used in the past that create hope for the future. Most of the methods use inductive learning or learning by example. This means that a large set of problems and their solutions is given to the machine from which to learn.

Biological inspiration

• Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.

• An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.

• The nervous system is build by relatively simple units, the neurons, so copying their behaviour and functionality should be the solution.

• The spikes travelling along the axon of the pre-synaptic neuron trigger the release of neurotransmitter substances at the synapse.

• The neurotransmitters cause excitation or inhibition in the dendrite of the post-synaptic neuron.

• The integration of the excitatory and inhibitory signals may produce spikes in the post-synaptic neuron.

• The contribution of the signals depends on the strength of the synaptic connection.

Structure of a Biological Neuron

Dendrites: Accepts Inputs

Soma: Processes the Inputs

Axon: Turns the processed inputs into outputs

Synapses: The electrochemical contact between neurons

An Artificial Neuron

W1

W2

W3

Wn

W0

f

X1

X2

X3

Xn

Axons Synapses Dendrites Body (Soma)

Axon

Bias

Output (y)

Artificial neural networks

Inputs

Output

An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

Artificial neural networks

A collection of neurons which are interconnected. The output of one connects to several others with different strength connections. This collection of neurons is termed as multi-layer networks. Initially, neural networks have no knowledge. (All information is learned from experience using the network.)

Input 1

Input 2

Input 3

Neuron 1

Neuron 2

Output from Neuron 1

Output fromNeuron 2

Artificial Neural Networks

Each processing element in an artificial neural net is analogous to a biological neuron

An element accepts a certain number of input values (dendrites) and produces a single output value (axon) of either 0 or 1.

Associated with each input value is a numeric weight (synapse) The effective weight of the element is the sum of the weights multiplied by their respective input values.v1 * w1 + v2 * w2 + v3 * w3

Each element has a numeric threshold value.

If the effective weight exceeds the threshold, the unit produces an output value of 1.

If it does not exceed the threshold, it produces an output value of 0.

Artificial neural networks

Artificial models of the brain are of two distinct types: Electronic: Has electronic circuits that act

like neurons. Software: This version runs a program on

the computer that simulates the action of the neurons.

Artificial neural networks

Tasks to be solved by artificial neural networks:

• controlling the movements of a robot based on self-perception and other information (e.g., visual information);

• deciding the category of potential food items (e.g., edible or non-edible) in an artificial world;

• recognizing a visual object (e.g., a familiar face);

• predicting where a moving object goes, when a robot wants to catch it.

Natural Language Processing

Three basic types of processing occur during human/ computer voice interaction:Voice synthesisUsing a computer to recreate the sound of human speech Voice recognition Using a computer to recognize the words spoken by a humanNatural language comprehensionUsing a computer to apply a meaningful interpretation to human communication

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Voice Synthesis

Dynamic voice generation A computer examines the letters that make up a word and produces the sequence of sounds that correspond to those letters in an attempt to vocalize the word.Phonemes The sound units into which human speech has been categorized.Recorded speech A large collection of words is recorded digitally and individual words are selected to make up a messageMany words must be recorded more than once to reflect different pronunciations and inflections.

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Voice Recognition

Problems with understanding speech Each person's sounds are unique. Each person's shape of mouth, tongue, throat, and nasal

cavities that affect the pitch and resonance of our spoken voice are unique.

Speech impediments, mumbling, volume, regional accents, and the health of the speaker are further complications.

Humans speak in a continuous, flowing manner, stringing words together.

Sound-alike phrases like “ice cream” and “I scream”. Homonyms such as “I” & “eye” or “see” & “sea”.

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Voice Recognition

Humans clarify these situations by context, but that requires another level of comprehension. Voice-recognition systems still have trouble with continuous speech.

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VoiceprintThe plot of frequency changes over time representing the sound of human speechA human trains a voice-recognition system by speaking a word several times so the computer gets an average voiceprint for a word

Voice Recognition

Used to authenticate the declaredsender of a voice message

Natural Language Comprehension

Natural language is ambiguous!

Lexical ambiguityThe ambiguity created when words have multiple meanings.

Syntactic ambiguityThe ambiguity created when sentences can be constructed in various ways.

Referential ambiguityThe ambiguity created when pronouns could be applied to multiple objects.

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Natural Language Comprehension

Lexical ambiguityStand up for your country.Take the street on the left.

Syntactic ambiguityI saw the bird watching from the corner.I ate the sandwich sitting on the table.

Referential ambiguityThe bicycle hit the curb, but it was not damaged.John was mad at Bill, but he didn't care.

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Can you thinkof

some others?

Applications

Neural networks can be used when enough pre-established inputs and outputs exist to train the network. Two areas in which neural networks have proved to be useful are optical character recognition (OCR), in which the intelligent agent is supposed to read any handwriting, and credit assignment, where different factors can be weighted to establish a credit rating, for example for a loan applicant.

Human Intelligence VS Artificial Intelligence

Human Intelligence VS Artificial Intelligence

HUMAN INTELLIGENCE

Intuition, Common sense, Judgments, Creativity, Beliefs etc

The ability to demonstrate their intelligence by communicating effectively

Plausible Reasoning and Critical thinking

ARTIFICIAL INTELLIGENCE

Ability to simulate human behavior and cognitive processes

Capture and preserve human expertise

Fast Response. The ability to comprehend large amounts of data quickly.

Pros

Human Intelligence VS Artificial Intelligence

HUMAN INTELLIGENCE

• Humans are fallible• They have limited

knowledge bases• Information processing of

serial nature proceed very slowly in the brain as compared to computers

Humans are unable to retain large amounts of data in memory.

ARTIFICIAL INTELLIGENCE

No “common sense” Cannot readily deal with

“mixed” knowledge May have high

development costs Raise legal and ethical

concerns

Cons