SlideShare a Scribd company logo
What is Artificial Intelligence?
• AI is the effort to develop systems that can
behave/act like humans.
• Turing Test
• The problem = unrestricted domains
– human intelligence vastly complex and broad
– associations, metaphors, and analogies
– common sense
– conceptual frameworks
Elements of AI
• Natural Language Processing
• Robotics
• Perceptive Systems (Vision)
• Expert Systems
How are Machines Intelligent?
• Constrained Heuristic Search
– How do you play chess?
• first move = 20 possible
• second move = 400 possible
• 7th move = 1,280,000,000 possible
– Depth First vs. Breath First Searching
– Ability to Learn
Decision Tree
Depth First Search
Breath First Search
Expert Systems
• Capture knowledge of an expert.
• Represent Knowledge as a
– rule base
• if then rules
– semantic net
• hierarchy
– frames
• shared characteristics, IS-A relationships
Expert System Successes
• XCON - configures systems for DEC
• Prospector - an mining expert
• MYCIN - infectious blood diseases
• EMYCIN - Empty MYCIN
Elements of Expert System Shell
• Knowledge Base
– rules
• Working Memory
– facts of current case
• Inference Engine
– applies rules using current set of facts
• Explanation Facility
• CLIPS
Neural Networks & The Brain
• Base on architecture of human brain
– Neurons connected by axons & dendrites
– 100 billion neurons
– 1,000 dendrites per neuron
– 100,000 billion synapses
– 10 million billion interconnectons per second
How a Neuron Works
Impulses
come from
other neurons.
When sum of
inputs reaches
a threshold,
neuron fires.
Sending
impulses
to next
level of
neurons.
An Artificial Neural Network
Inputs Hidden Output
w
w
w
w
w
w
Neural Networks, NN
• NNs learn by using a training set and
adjusting the weights on each connection.
• NNs do not have to be “told” explicit
relationship rules.
• NNs can work with partial inputs.
• NNs cannot explain their results.
• NNs can take a long time to train.
• A NN demonstration

More Related Content

PPT
Artificial Intelligence and Expert Systems
PPTX
csc384-Lecture01-Introduction_abcdpdf_pdf_to_ppt.pptx
PPT
Artificial Intelligence and expert system
PPTX
Ai introduction
PPTX
AIArtificial intelligence (AI) is a field of computer science a
PPT
Artificial intelligence
PDF
Lec-1.pdf
PPTX
Artificial Intelligent introduction or history
Artificial Intelligence and Expert Systems
csc384-Lecture01-Introduction_abcdpdf_pdf_to_ppt.pptx
Artificial Intelligence and expert system
Ai introduction
AIArtificial intelligence (AI) is a field of computer science a
Artificial intelligence
Lec-1.pdf
Artificial Intelligent introduction or history

Similar to AI.ppt (20)

PPT
AI chapter one/AI chapter one/AI chapter one
PPTX
1 artificial intelligence
PPTX
Lec1 introduction
PPT
n01.ppt
PPTX
Basic concepts of soft computing soft computing.pptx
PPTX
1- Introduction to A I and systems pptx
PPT
Artificial intelligent Lec 1-ai-introduction-
PDF
UNIT 1 SRMIST KTR_problem and agents.pdf
PPTX
AI for Everyone: Master the Basics
PPT
Useful Techniques in Artificial Intelligence
PPT
1 Introduction to Articial intelligence.ppt
PPTX
AI Introduction
PPT
11 expert systems___applied
PPTX
How to create a mind
PDF
Introduction to Artificial Intelligence.
PPT
Expert systems-hakim
PPT
ArtificialIntelligence.ppt
PPTX
Exo cortex
PPTX
Intro artificial intelligence
PPTX
Artificial intelligence in cyber defense
AI chapter one/AI chapter one/AI chapter one
1 artificial intelligence
Lec1 introduction
n01.ppt
Basic concepts of soft computing soft computing.pptx
1- Introduction to A I and systems pptx
Artificial intelligent Lec 1-ai-introduction-
UNIT 1 SRMIST KTR_problem and agents.pdf
AI for Everyone: Master the Basics
Useful Techniques in Artificial Intelligence
1 Introduction to Articial intelligence.ppt
AI Introduction
11 expert systems___applied
How to create a mind
Introduction to Artificial Intelligence.
Expert systems-hakim
ArtificialIntelligence.ppt
Exo cortex
Intro artificial intelligence
Artificial intelligence in cyber defense
Ad

More from TALHARIAZ46 (8)

PPT
Microprocessor and Microcontroller Based Systems.ppt
PPT
Introduction to Microprocessor .ppt
PPT
Microprocessor Based Systems.ppt
PPTX
Microbial-Food-Safety.pptx
PPTX
Industrial Control System.pptx
PPTX
Industrial Control System.pptx
PPTX
Food Safety Presentation.pptx
PPTX
Industrial Control Devices.pptx
Microprocessor and Microcontroller Based Systems.ppt
Introduction to Microprocessor .ppt
Microprocessor Based Systems.ppt
Microbial-Food-Safety.pptx
Industrial Control System.pptx
Industrial Control System.pptx
Food Safety Presentation.pptx
Industrial Control Devices.pptx
Ad

Recently uploaded (20)

PPT
A5_DistSysCh1.ppt_INTRODUCTION TO DISTRIBUTED SYSTEMS
PPT
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
PPTX
CURRICULAM DESIGN engineering FOR CSE 2025.pptx
PDF
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
PDF
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
PDF
COURSE DESCRIPTOR OF SURVEYING R24 SYLLABUS
PDF
Automation-in-Manufacturing-Chapter-Introduction.pdf
PDF
A SYSTEMATIC REVIEW OF APPLICATIONS IN FRAUD DETECTION
PDF
86236642-Electric-Loco-Shed.pdf jfkduklg
PDF
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
PDF
737-MAX_SRG.pdf student reference guides
PPTX
Nature of X-rays, X- Ray Equipment, Fluoroscopy
PPTX
Artificial Intelligence
PDF
PPT on Performance Review to get promotions
PPTX
communication and presentation skills 01
PPT
introduction to datamining and warehousing
PDF
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
PDF
Unit I ESSENTIAL OF DIGITAL MARKETING.pdf
PDF
Soil Improvement Techniques Note - Rabbi
PDF
Categorization of Factors Affecting Classification Algorithms Selection
A5_DistSysCh1.ppt_INTRODUCTION TO DISTRIBUTED SYSTEMS
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
CURRICULAM DESIGN engineering FOR CSE 2025.pptx
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
COURSE DESCRIPTOR OF SURVEYING R24 SYLLABUS
Automation-in-Manufacturing-Chapter-Introduction.pdf
A SYSTEMATIC REVIEW OF APPLICATIONS IN FRAUD DETECTION
86236642-Electric-Loco-Shed.pdf jfkduklg
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
737-MAX_SRG.pdf student reference guides
Nature of X-rays, X- Ray Equipment, Fluoroscopy
Artificial Intelligence
PPT on Performance Review to get promotions
communication and presentation skills 01
introduction to datamining and warehousing
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
Unit I ESSENTIAL OF DIGITAL MARKETING.pdf
Soil Improvement Techniques Note - Rabbi
Categorization of Factors Affecting Classification Algorithms Selection

AI.ppt

  • 1. What is Artificial Intelligence? • AI is the effort to develop systems that can behave/act like humans. • Turing Test • The problem = unrestricted domains – human intelligence vastly complex and broad – associations, metaphors, and analogies – common sense – conceptual frameworks
  • 2. Elements of AI • Natural Language Processing • Robotics • Perceptive Systems (Vision) • Expert Systems
  • 3. How are Machines Intelligent? • Constrained Heuristic Search – How do you play chess? • first move = 20 possible • second move = 400 possible • 7th move = 1,280,000,000 possible – Depth First vs. Breath First Searching – Ability to Learn
  • 7. Expert Systems • Capture knowledge of an expert. • Represent Knowledge as a – rule base • if then rules – semantic net • hierarchy – frames • shared characteristics, IS-A relationships
  • 8. Expert System Successes • XCON - configures systems for DEC • Prospector - an mining expert • MYCIN - infectious blood diseases • EMYCIN - Empty MYCIN
  • 9. Elements of Expert System Shell • Knowledge Base – rules • Working Memory – facts of current case • Inference Engine – applies rules using current set of facts • Explanation Facility • CLIPS
  • 10. Neural Networks & The Brain • Base on architecture of human brain – Neurons connected by axons & dendrites – 100 billion neurons – 1,000 dendrites per neuron – 100,000 billion synapses – 10 million billion interconnectons per second
  • 11. How a Neuron Works Impulses come from other neurons. When sum of inputs reaches a threshold, neuron fires. Sending impulses to next level of neurons.
  • 12. An Artificial Neural Network Inputs Hidden Output w w w w w w
  • 13. Neural Networks, NN • NNs learn by using a training set and adjusting the weights on each connection. • NNs do not have to be “told” explicit relationship rules. • NNs can work with partial inputs. • NNs cannot explain their results. • NNs can take a long time to train. • A NN demonstration