The document discusses different definitions of artificial intelligence, including studying how to make computers solve problems requiring knowledge and intelligence, creating machines that perform intelligent functions, and studying mental faculties through computational models. It also examines what intelligence is, different approaches to AI such as symbol systems and reasoning, and challenges like the Turing Test which aim to determine if a machine can exhibit intelligent behavior. Key debates in AI are discussed around what constitutes intelligence and how to define and measure intelligent behavior in machines.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The birth of Artificial Intelligence (1952-1956)
Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The birth of Artificial Intelligence (1952-1956)
Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key topics covered include the Turing test, early AI systems like Eliza, knowledge representation approaches like scripts and frames, challenges like the Chinese room problem, and assumptions underlying symbolic and connectionist AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent, and challenges with symbolic and connectionist AI approaches.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
Introduction to AI and explain the magic of the magic of Nosql and explain in...SurajGurushetti
How about "Introduction to AI: Understanding the Basics"? It's a simple yet relevant topic that can cover fundamental concepts of artificial intelligence in a concise manner.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key aspects of AI covered include it being an interdisciplinary field that studies mental faculties through computational models, seeks to explain and emulate intelligent behavior computationally, and automates activities associated with human thinking like decision making and learning. The document also examines perspectives on what constitutes intelligence and the goals and challenges of creating intelligent machines.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key aspects of AI covered include it being an interdisciplinary field that studies mental faculties through computational models, seeks to explain and emulate intelligent behavior computationally, and automates activities associated with human thinking like decision making and learning. The document also examines perspectives on what constitutes intelligence and definitions of AI in terms of replicating human intelligence or demonstrating intelligent behavior.
new tends in artifical intelligence 2024.pptTAHIRZAMAN81
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key topics covered include the Turing test, early AI systems like Eliza, knowledge representation approaches like scripts and frames, challenges like the Chinese room problem, and assumptions underlying symbolic and connectionist AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key topics covered include the Turing test, early AI systems like Eliza, knowledge representation approaches like scripts and frames, challenges like the Chinese room problem, and assumptions underlying symbolic and connectionist AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent, and challenges with symbolic and connectionist AI approaches.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
Introduction to AI and explain the magic of the magic of Nosql and explain in...SurajGurushetti
How about "Introduction to AI: Understanding the Basics"? It's a simple yet relevant topic that can cover fundamental concepts of artificial intelligence in a concise manner.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key aspects of AI covered include it being an interdisciplinary field that studies mental faculties through computational models, seeks to explain and emulate intelligent behavior computationally, and automates activities associated with human thinking like decision making and learning. The document also examines perspectives on what constitutes intelligence and the goals and challenges of creating intelligent machines.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or understanding.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions through computational models, and make computers solve problems in intelligent ways. The document also examines perspectives on what constitutes intelligence, debates around the Turing Test and whether passing it ensures a system is intelligent. It explores early symbolic AI systems like Eliza and the challenges they faced in terms of scalability, brittleness and learning. Overall, the summary provides a high-level look at key topics in the history and study of AI.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key aspects of AI covered include it being an interdisciplinary field that studies mental faculties through computational models, seeks to explain and emulate intelligent behavior computationally, and automates activities associated with human thinking like decision making and learning. The document also examines perspectives on what constitutes intelligence and definitions of AI in terms of replicating human intelligence or demonstrating intelligent behavior.
new tends in artifical intelligence 2024.pptTAHIRZAMAN81
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI as a field that studies how to automate intelligent behavior, emulate cognitive functions, and solve problems requiring knowledge. The document also examines perspectives on what constitutes intelligence and definitions of AI. It explores early symbolic approaches to AI like the Turing Test, Eliza, and slot filling as well as criticisms of symbolic AI like the Chinese Room problem. Overall, the document aims to define AI and discuss different theories and approaches within the field.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
The document provides an overview of different definitions and perspectives on artificial intelligence (AI). It discusses AI in terms of studying how to automate intelligent behavior, problem solving, and cognitive functions. Key topics covered include the Turing test, early AI systems like Eliza, knowledge representation approaches like scripts and frames, challenges like the Chinese room problem, and assumptions underlying symbolic and connectionist AI.
AI can be defined in multiple ways, including studying how to make computers intelligent like humans, automating intelligent behavior, and studying cognitive abilities through computational models. The Turing test proposes that a computer can be considered intelligent if a human cannot distinguish it from a real person through conversation. Early programs like ELIZA passed the Turing test through simple pattern matching and question swapping rather than true understanding. While the Turing test can be passed through extensive rules, it does not prove a system has human-level intelligence or comprehension.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Walmart Business+ and Spark Good for Nonprofits.pdf
intro.ppt
1. AI Definitions
• The study of how to make programs/computers do thi
ngs that people do better
• The study of how to make computers solve problems
which require knowledge and intelligence
• The exciting new effort to make computers think …
machines with minds
• The automation of activities that we associate with hu
man thinking (e.g., decision-making, learning…)
• The art of creating machines that perform functions t
hat require intelligence when performed by people
• The study of mental faculties through the use of com
putational models
• A field of study that seeks to explain and emulate inte
lligent behavior in terms of computational processes
• The branch of computer science that is concerned wit
h the automation of intelligent behavior
Thinking
machines or
machine
intelligence
Studying
cognitive
faculties
Problem
Solving and
CS
2. So What Is AI?
• AI as a field of study
– Computer Science
– Cognitive Science
– Psychology
– Philosophy
– Linguistics
– Neuroscience
• AI is part science, part engineering
• AI often must study other domains in order to implement systems
– e.g., medicine and medical practices for a medical diagnostic system, engin
eering and chemistry to monitor a chemical processing plant
• AI is a belief that the brain is a form of biological computer and t
hat the mind is computational
• AI has had a concrete impact on society but unlike other areas of
CS, the impact is often
– felt only tangentially (that is, people are not aware that system X has AI)
– felt years after the initial investment in the technology
3. What is Intelligence?
• Is there a “holistic” definition for intelligence?
• Here are some definitions:
– the ability to comprehend; to understand and profit from experience
– a general mental capability that involves the ability to reason, plan, solve
problems, think abstractly, comprehend ideas and language, and learn
– is effectively perceiving, interpreting and responding to the environment
• None of these tells us what intelligence is, so instead, maybe we
can enumerate a list of elements that an intelligence must be able
to perform:
– perceive, reason and infer, solve problems, learn and adapt, apply commo
n sense, apply analogy, recall, apply intuition, reach emotional states, achi
eve self-awareness
• Which of these are necessary for intelligence? Which are suffici
ent?
• Artificial Intelligence – should we define this in terms of human
intelligence?
– does AI have to really be intelligent?
– what is the difference between being intelligent and demonstrating intelli
gent behavior?
4. Physical Symbol System Hypothesis
• A physical symbol system (PSS)
– consists of symbols (patterns) which are combinable into expr
essions
– there are processes which operate on these symbols to create
new symbols and expressions
• consider for instance English as a physical symbol system
• symbols are the alphabet
• expressions are words and sentences
• the processes are the English grammar and parsers and dictionaries
• The PSS Hypothesis states that a PSS has the necessary
and sufficient means for intelligent action
– a computer is a PSS
• if the PSS Hypothesis is true, then it should be possible to program a c
omputer to produce intelligent actions
• this is the (or a) goal of AI
• if the PSS Hypothesis is not true, what are the consequences for AI?
5. The Turing Test
• 1950 – Alan Turing devised
a test for intelligence called
the Imitation Game
– Ask questions of two entities
, receive answers from both
– If you can’t tell which of the
entities is human and which
is a computer program, then
you are fooled and we shoul
d therefore consider the co
mputer to be intelligent
Questions
Answers Answers
Which is the person?
Which is the computer?
6. Solving the Turing Test: Eliza
• Created by Joseph Weizenbaum
• This system would act like a Rogerian psychoanalyst by
holding a dialog with a person
– the dialog would be consist of the doctor (Eliza) asking questi
ons, the human responding, and the doctor using the respon
se to ask another question
• Weizenbaum wrote the program so that it would gener
ate an English response/question based on a group of p
atterns
– If the user sentence matched a pattern, this pattern would be
used to generate the next sentence/question
– A more recent, and much superior version is called A.L.I.C.E, s
ee http://alicebot.blogspot.com/
• click on “chat with A.L.I.C.E.”
7. Example
Eliza
Dialog
– H: Men are all alike
– E: In what way?
– H: They’re always bugging us about something or
another.
– E: Can you be more specific?
– H: Well, my boyfriend made me come here.
– E: Your boyfriend made you come here?
– H: He says I’m depressed much of the time.
– E: I am sorry to hear you are depressed.
– H: It’s true. I am unhappy.
– E: Do you think coming here will help you not to be
unhappy?
– H: I need some help, that much seems certain.
– E: What would it mean to you if you got some help?
– H: Perhaps I could learn to get along with my mother.
– E: Tell me more about your family
8. Eliza
Rules
(?* ?x) hello (?* ?y)
(How do you do. Please state your problem.)
(?* ?x) I want (?* ?y)
(What would it mean if you got ?y)
(Why do you want ?y)
(Suppose you got ?y soon)
(?* ?x) if (?* ?y)
(Do you really think its likely that ?y)
(Do you wish that ?y)
(What do you think about ?y) (Really-- if ?y)
(?* ?x) no (?* ?y) (Why not?)
(You are being a bit negative)
(Are you saying "NO" just to be negative?)
(?* ?x) I was (?* ?y) (Were you really?)
(Perhaps I already knew you were ?y)
(Why do you tell me you were ?y now?)
(?* ?x) I feel (?* ?y) (Do you often feel ?y ?)
(?* ?x) I felt (?* ?y)
(What other feelings do you have?)
Eliza also has a
rule to swap
‘I’ and ‘my’
to ‘you’ and ‘your’
9. Eliza Pattern Syntax
pat var match any one expression to a variable
constant or to a constant (see below)
segment-pat match against a sequence
single-pat match against one expression
(pat . pat) match the first and the rest of a list
single-pat
(?is var predicate) test predicate on one expression
(?or pat1 pat2 …) match on any of the patterns
(?and pat1 pat2 …) match on every of the expressions
(?not pat) match if expression does not match
segment-pat
((?* var) …) match on zero or more expressions
((?+ var) …) match on one or more expressions
((?? var) …) match zero or one expression
((?if expr) …) test if expression is true
var ?chars variables of the form ?name
constant atom symbols, numbers, chars
10. ALICE Demo
Jerry: my name is jerry.
ALICE: Hey jerry.
Jerry: do you know Turing?
ALICE: A brilliant and tragic figure in the history of computer science.
Jerry: who else do you know?
ALICE: Hugh Loebner, Robby Garner, and Steven Spielberg
Jerry: which film do you like?
ALICE: My favorite movie is Teknolust. Have you seen it?
Jerry: no.
ALICE: I would recommend you check it out.
Jerry: what is it about?
ALICE: Science fiction.
Jerry: do you remember my name?
ALICE: Your name is jerry, seeker.
Notice unlike Eliza, ALICE has a memory, can handle references
(have you seen it? no. I would recommend it.)
11. ALICE vs. Eliza
ALICE talking to ELIZA
ELIZA: I am ELIZA.
ALICE: I am very pleased to meet you ELIZA.
ELIZA: How can I help you?
ALICE: Just keep talking to me as long as you can.
ELIZA: Do you wish to as long as me?
ALICE: OK, I will as long as you.
ELIZA: Do you wish to will as long as me?
ALICE: OK, I will will as long as you.
ELIZA: Do you wish to will will as long as me?
ALICE: OK, I will will will as long as you.
ELIZA: Do you wish to will will will as long as me?
ALICE: OK, I will will will will as long as you.
ELIZA: Do you wish to will will will will as long as me?
ALICE: OK, I will will will will will as long as you.
ELIZA: Do you wish to will will will will will as long as me?
…
Eliza gets stuck on the phrase “I will” and then ALICE gets stuck
on the same phrase
12. How Useful is the Turing Test?
• With Eliza or Alice like rules, we can eventually solve the T
uring Test – it just takes writing enough rules
• Does the system understand what it is responding to?
– No, neither Eliza nor Alice understand the text, its just that Alice has
better, more in depth and wider ranging rules
• However, we could build a representation that model
s some real-world domain and knowledge base
– The system can fill in information from the conversation
• this is sort of like a database, or an object with data attributes to be filled in
• we can use a variety of AI representations like scripts, frames, semantic net
works
– Questions can be responded to by looking up the stored data
– In this way, the system is responding, not based merely on “canned”
knowledge, but on knowledge that it has “learned”
• Does this imply that the system knows what it is discussing?
– What does it mean to know something?
13. Table-Lookup vs. Reasoning
• Consider two approaches to programming a Tic-Tac-Toe player
– Solution 1: a pre-enumerated list of best moves given the board configurati
on
– Solution 2: rules (or a heuristic function) that evaluate a board configuratio
n, and using these to select the next best move
• Solution 1 is similar to how Eliza works
– This is not practical for most types of problems
– Consider solving the game of chess in this way, or trying to come up with a
ll of the responses that a Turing Test program might face
• Solution 2 will reason out the best move
– Given the board configuration, it will analyze each available move and dete
rmine which is the best
– Such a player might even be able to “explain” why it chose the move it did
• We can (potentially) build a program that can pass the Turing Test
using table-lookup even though it would be a large undertaking
• Could we build a program that can pass the Turing Test using reas
oning?
– Even if we can, does this necessarily mean that the system is intelligent?
14. Slot Filling
• Roger Schank created the S
cript representation
– the script describes typical se
quences of actions and actors
for some real-world situation
– a story (newspaper article) is
parsed and slots are filled in
– SAM (Script Applier Mechan
ism) uses the filled in script t
o answer questions
• The Script provides the kno
wledge needed to respond li
ke a human and thus solve t
he Turing Test
Schank’s Restaurant script
15. The Chinese Room Problem
• From John Searle, Philosopher, in an attempt to demon
strate that computers cannot be intelligent
– The room consists of you, a book, a storage area (optional), a
nd a mechanism for moving information to and from the roo
m to the outside
• a Chinese speaking individual provides a question for you in writing
• you are able to find a matching set of symbols in the book (and storag
e) and write a response, also in Chinese
Question (Chinese)
Book of Chinese Symbols
Answer
(Chinese)
Storage You
16. Chinese Room:
An Analogy for a Computer
User Input I/O pathway (bus) Output
Memory Program/Data
(Script) CPU (SAM)
Note: Searle’s original Chinese Room actually was based on a
Script that was implemented in Chinese, our version is just a
variation on the same theme
17. Searle’s Question
• You were able to solve the problem of communicating with the p
erson/user and thus you/the room passes the Turing Test
• But did you understand the Chinese messages being communica
ted?
– since you do not speak Chinese, you did not understand the symbols in th
e question, the answer, or the storage
– can we say that you actually used any intelligence?
• By analogy, since you did not understand the symbols that you in
teracted with, neither does the computer understand the symbo
ls that it interacts with (input, output, program code, data)
• Searle concludes that the computer is not intelligent, it has no “s
emantics,” but instead is merely a symbol manipulating device
– the computer operates solely on syntax, not semantics
• He defines to categories of AI:
– strong AI – the pursuit of machine intelligence
– weak AI – the pursuit of machines solving problems in an intelligent way
18. But Computers Solve Problems
• We can clearly see that computers solve problems in a s
eemingly intelligent way
– Where is the intelligence coming from?
• There are numerous responses to Searle’s argument
– The System’s Response:
• the hardware by itself is not intelligent, but a combination of the hard
ware, software and storage is intelligent
• in a similar vein, we might say that a human brain that has had no opp
ortunity to learn anything cannot be intelligent, it is just the hardware
– The Robot Response:
• a computer is void of senses and therefore symbols are meaningless to
it, but a robot with sensors can tie its symbols to its senses and thus un
derstand symbols
– The Brain Simulator Response:
• if we program a computer to mimic the brain (e.g., with a neural netw
ork) then the computer will have the same ability to understand as a hu
man brain
19. Brain vs. Computer
• In AI, we compare the brain (or the mind) and the compu
ter
– Our hope: the brain is a form of computer
– Our goal: we can create computer intelligence through progra
mming just as people become intelligent by learning
But we see that the computer
is not like the brain
The computer performs tasks
without understanding what
its doing
Does the brain understand
what its doing when it solves
problems?
20. Symbol Grounding
• One problem with the computer is that it works strictly s
yntactically
– Op code: 10011101 translates into a set of microcode instr
uctions such as: move IR16..31 to MAR, signal memory read,
move MBR to AC
– There is no understanding
• x = y + z; is meaningless to the computer
– the computer doesn’t understand addition, it just knows that a certain op c
ode means to move values to the adder and move the result elsewhere
• do you know what addition means?
– if so, how do you proscribe meaning to +
– how is this symbol grounded in your brain?
– can computers similarly achieve this?
– Recall Schank’s Restaurant script
• does the computer know what the symbols “waiter” or “PTRANS” repr
esent? or does it merely have code that tells the computer what to do w
hen it comes across certain words in the story, or how to respond to a gi
ven question?
21. Two AI Assumptions
• We can understand and model cognition without understa
nding the underlying mechanism
– That is, it is the model of cognition that is important not the phy
sical mechanism that implements it
– If this is true, then we should be able to create cognition (mind)
out of a computer or a brain or even other entities that can comp
ute such as a mechanical device
• This is the assumption made by symbolic AI researchers
• Cognition will emerge from the proper mechanism
– That is, the right device, fed with the right inputs, can learn and
perform the problem solving that we, as observers, call intellige
nce
– Cognition will arise as the result (or side effect) of the hardware
• This is the assumption made by connectionist AI researchers
• Notice that while the two assumptions differ, neither is ne
cessarily mutually exclusive and both support the idea tha
t cognition is computational
22. Problems with Symbolic AI Approaches
• Scalability
– It can take dozens or more man-years to create a useful system
s
– It is often the case that systems perform well up to a certain thr
eshold of knowledge (approx. 10,000 rules), after which perfor
mance (accuracy and efficiency) degrade
• Brittleness
– Most symbolic AI systems are programmed to solve a specific
problem, move away from that domain area and the system’s a
ccuracy drops rapidly rather than achieving a graceful degrada
tion
• this is often attributed to lack of common sense, but in truth, it is a lack
of any knowledge outside of the domain area
– No or little capacity to learn, so performance (accuracy) is stati
c
• Lack of real-time performance
23. Problems with Connectionist AI Approaches
• No “memory” or sense of temporality
– The first problem can be solved to some extent
– The second problem arises because of a fixed sized input but le
ads to poor performance in areas like speech recognition
• Learning is problematic
– Learning times can greatly vary
– Overtraining leads to a system that only performs well on the tr
aining set and undertraining leads to a system that has not gene
ralized
• No explicit knowledge-base
– So there is no way to tell what a system truly knows or how it k
nows something
• No capacity to explain its output
– Explanation is often useful in an AI system so that the user can
trust the system’s answer
24. So What Does AI Do?
• Most AI research has fallen into one of two categories
– Select a specific problem to solve
• study the problem (perhaps how humans solve it)
• come up with the proper representation for any knowledge needed to so
lve the problem
• acquire and codify that knowledge
• build a problem solving system
– Select a category of problem or cognitive activity (e.g., learnin
g, natural language understanding)
• theorize a way to solve the given problem
• build systems based on the model behind your theory as experiments
• modify as needed
• Both approaches require
– one or more representational forms for the knowledge
– some way to select proper knowledge, that is, search
25. What is Search?
• We define the state of the problem being solved as the va
lues of the active variables
– this will include any partial solutions, previous conclusions, user
answers to questions, etc
• while humans are often a
ble to make intuitive leaps
, or recall solutions with lit
tle thought, the computer
must search through vario
us combinations to find a
solution
• To the right is a search sp
ace for a tic-tac-toe gam
e
26. Search Spaces and Types of Search
• The search space consists of all possible states of the prob
lem as it is being solved
– A search space is often viewed as a tree and can very well con
sist of an exponential number of nodes making the search pro
cess intractable
– Search spaces might be pre-enumerated or generated during t
he search process
– Some search algorithms may search the entire space until a so
lution is found, others will only search parts of the space, poss
ibly selecting where to search through a heuristic
• Search spaces include
– Game trees like the tic-tac-toe game
– Decision trees (see next slides)
– Combinations of rules to select in a production system
– Networks of various forms (see next slides)
– Other types of spaces
27.
28.
29. Search Algorithms and Representations
• Breadth-first
• Depth-first
• Best-first (Heuristic Search)
• A*
• Hill Climbing
• Limiting the number of Plie
s
• Minimax
• Alpha-Beta Pruning
• Adding Constraints
• Genetic Algorithms
• Forward vs Backward Chain
ing
• We will study various forms of
representation and uncertainty
handling in the next class perio
d
• Knowledge needs to be repres
ented
– Production systems of some for
m are very common
• If-then rules
• Predicate calculus rules
• Operators
– Other general forms include sem
antic networks, frames, scripts
– Knowledge groups
– Models, cases
– Agents
– Ontologies
30. A Brief History of AI: 1950s
• Computers were thought of as an electronic brains
• Term “Artificial Intelligence” coined by John McCarthy
– John McCarthy also created Lisp in the late 1950s
• Alan Turing defines intelligence as passing the Imitation
Game (Turing Test)
• AI research largely revolves around toy domains
– Computers of the era didn’t have enough power or memory to s
olve useful problems
– Problems being researched include
• games (e.g., checkers)
• primitive machine translation
• blocks world (planning and natural language understanding within the t
oy domain)
• early neural networks researched: the perceptron
• automated theorem proving and mathematics problem solving
31. The 1960s
• AI attempts to move beyond toy domains
• Syntactic knowledge alone does not work, domain know
ledge required
– Early machine translation could translate English to Russian (“
the spirit is willing but the flesh is weak” becomes “the vodka
is good but the meat is spoiled”)
• Earliest expert system created: Dendral
• Perceptron research comes to a grinding halt when it is p
roved that a perceptron cannot learn the XOR operator
• US sponsored research into AI targets specific areas – n
ot including machine translation
• Weizenbaum creates Eliza to demonstrate the futility of
AI
32. 1970s
• AI researchers address real-world problems and solutions through e
xpert (knowledge-based) systems
– Medical diagnosis
– Speech recognition
– Planning
– Design
• Uncertainty handling implemented
– Fuzzy logic
– Certainty factors
– Bayesian probabilities
• AI begins to get noticed due to these successes
– AI research increased
– AI labs sprouting up everywhere
– AI shells (tools) created
– AI machines available for Lisp programming
• Criticism: AI systems are too brittle, AI systems take too much tim
e and effort to create, AI systems do not learn
33. 1980s: AI Winter
• Funding dries up leading to the AI Winter
– Too many expectations were not met
– Expert systems took too long to develop, too much money to i
nvest, the results did not pay off
• Neural Networks to the rescue!
– Expert systems took programming, and took dozens of man-y
ears of efforts to develop, but if we could get the computer to
learn how to solve the problem…
– Multi-layered back-propagation networks got around the prob
lems of perceptrons
– Neural network research heavily funded because it promised t
o solve the problems that symbolic AI could not
• By 1990, funding for neural network research was slowl
y disappearing as well
– Neural networks had their own problems and largely could no
t solve a majority of the AI problems being investigated
– Panic! How can AI continue without funding?
34. 1990s: ALife
• The dumbest smart thing you can do is staying alive
– We start over – lets not create intelligence, lets just create “life
” and slowly build towards intelligence
• Alife is the lower bound of AI
– Alife includes
• evolutionary learning techniques (genetic algorithms)
• artificial neural networks for additional forms of learning
• perception and motor control
• adaptive systems
• modeling the environment
• Let’s disguise AI as something new, maybe we’ll get so
me funding that way!
– Problems: genetic algorithms are useful in solving some opti
mization problems and some search-based problems, but not v
ery useful for expert problems
– perceptual problems are among the most difficult being solved
, very slow progress
35. Today: The New (Old) AI
• Look around, who is doing AI research?
• By their own admission, AI researchers are not doing “AI”, they a
re doing
– Intelligent agents, multi-agent systems/collaboration
– Ontologies
– Machine learning and data mining
– Adaptive and perceptual systems
– Robotics, path planning
– Search engines, filtering, recommendation systems
• Areas of current research interest:
– NLU/Information Retrieval, Speech Recognition
– Planning/Design, Diagnosis/Interpretation
– Sensor Interpretation, Perception, Visual Understanding
– Robotics
• Approaches
– Knowledge-based
– Ontologies
– Probabilistic (HMM, Bayesian Nets)
– Neural Networks, Fuzzy Logic, Genetic Algorithms