Understanding ai
•Download as PPT, PDF•
2 likes•694 views
The document provides an overview of artificial intelligence, including definitions of AI, different types of AI that focus on modeling how humans think and act or how ideal agents should think and act, areas of AI research, knowledge representation, and expert systems. It discusses challenges in acquiring and representing knowledge for expert systems and controlling reasoning.
Report
Share
Report
Share
![Today’s topics ,[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Presentation on artificial intelligence
This presentation provides an overview of artificial intelligence (AI) and how it works. It defines intelligence as the ability to learn from and interact with one's environment. Artificial intelligence is defined as making computers do intelligent tasks like humans. AI works using artificial neurons in artificial neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. Examples of AI applications include expert systems like PROSPECTOR for geology and PUFF for medicine diagnosis. Machine learning allows AI to mimic human intelligence by learning from failure, being told, or exploration. While human intelligence has intuition and creativity, AI can simulate human behavior, comprehend large data quickly, and preserve human expertise to achieve more than is known. AI is needed to
Artificial intelligence
This document discusses artificial intelligence (AI) and related concepts. It defines AI as making computers do things that require human intelligence. It explains that AI works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses machine learning methods, expert systems, applications of expert systems, the Turing test, and comparisons between human and artificial intelligence.
Ai
This document discusses different types and approaches to artificial intelligence:
1. The Turing Test approach aims to create AI that can converse with humans in natural language without being distinguished from a human. This requires capabilities in natural language processing, knowledge representation, automated reasoning, and machine learning.
2. The cognitive modeling approach seeks to understand human thought through introspection, psychological experiments observing human behavior, and brain imaging to then model human thinking in AI.
3. The "laws of thought" approach uses logic and probability to model rational thought, moving from perception to understanding how the world works to predicting the future.
4. The rational agent approach creates agents that can autonomously perceive their environment, act
Introduction To Artificial Intelligence
Introduction to Artificial Intelligence is the part of IOE Computer Engineering Syllabus covering the first chapter of AI. It covers definition, types and characteristics of AI. Similarly, it also deals with the Turing test for determining machine intelligence.
ARTIFICIAL INTELLIGENCE Presentation
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Introduction
This document provides an introduction to artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, and discusses how AI systems can learn, understand, and think like humans. The document outlines the history of AI from the 1950s to the present, traces some of the key developments in fields like machine learning, neural networks, and autonomous vehicles. It aims to examine different aspects of human and artificial intelligence.
Artificial Inteligence
The document provides an introduction to artificial intelligence (AI), including definitions, a brief history, and applications. It defines AI as a branch of computer science dealing with tasks like perception, reasoning, and learning. The history outlines early AI programs from the 1940s-1950s and growth areas from the 1980s-present. Applications discussed include natural language processing, speech recognition, computer vision, robotics, and automatic programming. Both positive potential futures with helpful personal robots and negative potential futures with uncontrolled robots destroying property are envisioned. The conclusion emphasizes developing AI in a constructive rather than destructive manner and with secure control.
Artificial intelligence introduction
The document provides an overview of an Artificial Intelligence course. The main objectives are to provide knowledge of AI, familiarize students with search techniques, and acquaint them with related fields and applications. The syllabus covers topics like search techniques, knowledge representation, machine learning, neural networks, expert systems, and natural language processing. It includes details on the unit hours, marks distribution, evaluation criteria, and references. The course aims to teach students the fundamental concepts and applications of AI through lectures and practical exercises in programming languages like LISP and PROLOG.
Recommended
Presentation on artificial intelligence
This presentation provides an overview of artificial intelligence (AI) and how it works. It defines intelligence as the ability to learn from and interact with one's environment. Artificial intelligence is defined as making computers do intelligent tasks like humans. AI works using artificial neurons in artificial neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. Examples of AI applications include expert systems like PROSPECTOR for geology and PUFF for medicine diagnosis. Machine learning allows AI to mimic human intelligence by learning from failure, being told, or exploration. While human intelligence has intuition and creativity, AI can simulate human behavior, comprehend large data quickly, and preserve human expertise to achieve more than is known. AI is needed to
Artificial intelligence
This document discusses artificial intelligence (AI) and related concepts. It defines AI as making computers do things that require human intelligence. It explains that AI works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses machine learning methods, expert systems, applications of expert systems, the Turing test, and comparisons between human and artificial intelligence.
Ai
This document discusses different types and approaches to artificial intelligence:
1. The Turing Test approach aims to create AI that can converse with humans in natural language without being distinguished from a human. This requires capabilities in natural language processing, knowledge representation, automated reasoning, and machine learning.
2. The cognitive modeling approach seeks to understand human thought through introspection, psychological experiments observing human behavior, and brain imaging to then model human thinking in AI.
3. The "laws of thought" approach uses logic and probability to model rational thought, moving from perception to understanding how the world works to predicting the future.
4. The rational agent approach creates agents that can autonomously perceive their environment, act
Introduction To Artificial Intelligence
Introduction to Artificial Intelligence is the part of IOE Computer Engineering Syllabus covering the first chapter of AI. It covers definition, types and characteristics of AI. Similarly, it also deals with the Turing test for determining machine intelligence.
ARTIFICIAL INTELLIGENCE Presentation
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Introduction
This document provides an introduction to artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, and discusses how AI systems can learn, understand, and think like humans. The document outlines the history of AI from the 1950s to the present, traces some of the key developments in fields like machine learning, neural networks, and autonomous vehicles. It aims to examine different aspects of human and artificial intelligence.
Artificial Inteligence
The document provides an introduction to artificial intelligence (AI), including definitions, a brief history, and applications. It defines AI as a branch of computer science dealing with tasks like perception, reasoning, and learning. The history outlines early AI programs from the 1940s-1950s and growth areas from the 1980s-present. Applications discussed include natural language processing, speech recognition, computer vision, robotics, and automatic programming. Both positive potential futures with helpful personal robots and negative potential futures with uncontrolled robots destroying property are envisioned. The conclusion emphasizes developing AI in a constructive rather than destructive manner and with secure control.
Artificial intelligence introduction
The document provides an overview of an Artificial Intelligence course. The main objectives are to provide knowledge of AI, familiarize students with search techniques, and acquaint them with related fields and applications. The syllabus covers topics like search techniques, knowledge representation, machine learning, neural networks, expert systems, and natural language processing. It includes details on the unit hours, marks distribution, evaluation criteria, and references. The course aims to teach students the fundamental concepts and applications of AI through lectures and practical exercises in programming languages like LISP and PROLOG.
Artificial intelligence
This document provides an overview of artificial intelligence, including definitions, history, and types. It defines AI as systems that can interpret data, learn from it, and achieve goals through adaptation like humans. The concepts of AI emerged in the 1950s from scientists like Turing who explored the possibility of machine intelligence. Modern AI uses machine learning, deep learning, and neural networks to analyze data and make predictions. AI is being applied in many areas like transportation, banking, social media and more. The document also discusses philosophical questions around AI's impact on jobs and possibility of surpassing humans. It concludes we should focus on creating devices to perform specific tasks rather than aiming for a single system superior to humans in all ways.
Artificial intelligence
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Robot & Frank & Basic AI
This presentation was after a showing of Robot & Frank and Livermore, CA public library. The point of the session was to explore AI basics and discuss the potential of the movie coming true.
Artificial intelligence
The document provides an introduction to artificial intelligence (AI), including definitions of AI, descriptions of the eras of AI development, types of AI approaches, and applications of AI. It discusses factors that have influenced recent advancement in AI and identifies areas of AI research focus. The summary is:
The document introduces artificial intelligence (AI), defining it as human-made thinking power. It describes the history and eras of AI development, different types and approaches of AI including weak AI, strong AI, and super AI. Furthermore, it discusses applications of AI and factors influencing recent advancement, and identifies areas of ongoing AI research focus.
Robotics & Artificial Intelligence
A robot is a machine that gathers information about its environment, uses that information to follow instructions and do work. Robots can perform tasks that are dangerous or difficult for humans, like radioactive waste cleanup or precise automobile production. Robots are also being designed to perform surgery, explore dangerous areas like space and the ocean, and use artificial intelligence to behave more like living creatures.
Ai & ai with python
Artificial intelligence (AI) simulates human intelligence through machine learning, reasoning, and self-correction. AI is used in expert systems, speech recognition, and machine vision. A virtual assistant is an AI application that understands voice commands and completes tasks for users. Python is a high-level, interpreted, interactive programming language that is designed to be highly readable using English keywords. It has simple syntax requiring less coding and is easy-to-learn and read.
Artificial intelligence and ethics
The document discusses several key issues regarding the ethics of developing artificial intelligence and creating a personal AI:
1. It is theoretically possible to create artificial intelligence that exhibits intelligent behavior, but personal intelligence on the level of human intelligence poses significant challenges.
2. Developing a personal AI that experiences consciousness, emotions, and qualia like humans raises philosophical questions about personhood, rights, and whether such an experiment could cause harm.
3. While an AI may be able to reason logically, replicating human moral decision-making and judgment requires the ability to empathize and consider unique contexts and scenarios, which is extremely difficult to program.
DWX 2018 Session about Artificial Intelligence, Machine and Deep Learning
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
Introduction to artificial intelligence
Introduction to artificial intelligenceSchool of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.AI And Philosophy
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
Basics of machine_learning
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
Artificial Intelligence Notes Unit 1
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
AI vs Human
The Turing Test Approach
Reverse Turing test
Human and Machine Intelligence
Speech and Voice Recognition
901470 chap1
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
Python AI tutorial
In this Artificial Intelligence Programming, we will see AI Problems, Tools in AI, and Artificial Intelligence approaches.
Artificial Intelligence for Business
These deck contains an overview of what is commercially available in terms of Artificial Intelligence for business applications. Be aware: it was created on April 2017 and this is a very fast moving industry....
Human and Artificial Intelligence
Human intelligence involves learning, adaptation, emotional skills, social skills, and metacognition. Artificial intelligence aims to computationally achieve goals. Theories of intelligence include identifying factors like fluid/crystallized intelligence or multiple intelligences. Measuring intelligence has focused on tests and processing speed/capacity. Culture also influences definitions of and performance on intelligence assessments. While AI can match or exceed humans in narrow domains, it still lacks general human-level intelligence and common sense.
Artificial intelligence
This document provides an overview of the history and use of artificial intelligence on computer systems. It discusses:
1) The origins of AI research beginning in the 1940s and 1950s with pioneers like Alan Turing and the first conference on AI at Dartmouth College in 1956.
2) The development of AI through boom periods in the 1960s with significant government funding, and bust periods in the 1970s and 1980s due to limitations and funding cuts.
3) Recent advances in AI from the 1990s to present using techniques like deep learning, access to large datasets, and increasing computational power which have led to applications in areas like logistics, data mining, and medical diagnosis.
AI Lecture 1 (introduction)
This document provides an overview of an artificial intelligence course titled CSE 412 taught in fall 2018. It introduces topics that will be covered like what AI is, the foundations and history of AI, the state of the art in AI, philosophical foundations, and logic programming using Prolog. The instructor is Tajim Md. Niamat Ullah Akhund and the document outlines the major sections and contents to be covered in the course.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
AI Lesson 01
The document provides an introduction to artificial intelligence (AI), including:
1) Defining AI as designing intelligent systems and examining different views on what constitutes intelligence.
2) Describing typical AI problems like object recognition, language processing, and games, noting that expert tasks are now solvable by computers but common tasks remain challenging.
3) Discussing the practical impact of AI and different approaches like strong AI, weak AI, applied AI, and cognitive AI.
4) Noting the current limits of AI in areas requiring common sense knowledge or understanding unconstrained natural language.
More Related Content
What's hot
Artificial intelligence
This document provides an overview of artificial intelligence, including definitions, history, and types. It defines AI as systems that can interpret data, learn from it, and achieve goals through adaptation like humans. The concepts of AI emerged in the 1950s from scientists like Turing who explored the possibility of machine intelligence. Modern AI uses machine learning, deep learning, and neural networks to analyze data and make predictions. AI is being applied in many areas like transportation, banking, social media and more. The document also discusses philosophical questions around AI's impact on jobs and possibility of surpassing humans. It concludes we should focus on creating devices to perform specific tasks rather than aiming for a single system superior to humans in all ways.
Artificial intelligence
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Robot & Frank & Basic AI
This presentation was after a showing of Robot & Frank and Livermore, CA public library. The point of the session was to explore AI basics and discuss the potential of the movie coming true.
Artificial intelligence
The document provides an introduction to artificial intelligence (AI), including definitions of AI, descriptions of the eras of AI development, types of AI approaches, and applications of AI. It discusses factors that have influenced recent advancement in AI and identifies areas of AI research focus. The summary is:
The document introduces artificial intelligence (AI), defining it as human-made thinking power. It describes the history and eras of AI development, different types and approaches of AI including weak AI, strong AI, and super AI. Furthermore, it discusses applications of AI and factors influencing recent advancement, and identifies areas of ongoing AI research focus.
Robotics & Artificial Intelligence
A robot is a machine that gathers information about its environment, uses that information to follow instructions and do work. Robots can perform tasks that are dangerous or difficult for humans, like radioactive waste cleanup or precise automobile production. Robots are also being designed to perform surgery, explore dangerous areas like space and the ocean, and use artificial intelligence to behave more like living creatures.
Ai & ai with python
Artificial intelligence (AI) simulates human intelligence through machine learning, reasoning, and self-correction. AI is used in expert systems, speech recognition, and machine vision. A virtual assistant is an AI application that understands voice commands and completes tasks for users. Python is a high-level, interpreted, interactive programming language that is designed to be highly readable using English keywords. It has simple syntax requiring less coding and is easy-to-learn and read.
Artificial intelligence and ethics
The document discusses several key issues regarding the ethics of developing artificial intelligence and creating a personal AI:
1. It is theoretically possible to create artificial intelligence that exhibits intelligent behavior, but personal intelligence on the level of human intelligence poses significant challenges.
2. Developing a personal AI that experiences consciousness, emotions, and qualia like humans raises philosophical questions about personhood, rights, and whether such an experiment could cause harm.
3. While an AI may be able to reason logically, replicating human moral decision-making and judgment requires the ability to empathize and consider unique contexts and scenarios, which is extremely difficult to program.
DWX 2018 Session about Artificial Intelligence, Machine and Deep Learning
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
Introduction to artificial intelligence
Introduction to artificial intelligenceSchool of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.AI And Philosophy
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
Basics of machine_learning
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
Artificial Intelligence Notes Unit 1
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
AI vs Human
The Turing Test Approach
Reverse Turing test
Human and Machine Intelligence
Speech and Voice Recognition
901470 chap1
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
Python AI tutorial
In this Artificial Intelligence Programming, we will see AI Problems, Tools in AI, and Artificial Intelligence approaches.
Artificial Intelligence for Business
These deck contains an overview of what is commercially available in terms of Artificial Intelligence for business applications. Be aware: it was created on April 2017 and this is a very fast moving industry....
Human and Artificial Intelligence
Human intelligence involves learning, adaptation, emotional skills, social skills, and metacognition. Artificial intelligence aims to computationally achieve goals. Theories of intelligence include identifying factors like fluid/crystallized intelligence or multiple intelligences. Measuring intelligence has focused on tests and processing speed/capacity. Culture also influences definitions of and performance on intelligence assessments. While AI can match or exceed humans in narrow domains, it still lacks general human-level intelligence and common sense.
Artificial intelligence
This document provides an overview of the history and use of artificial intelligence on computer systems. It discusses:
1) The origins of AI research beginning in the 1940s and 1950s with pioneers like Alan Turing and the first conference on AI at Dartmouth College in 1956.
2) The development of AI through boom periods in the 1960s with significant government funding, and bust periods in the 1970s and 1980s due to limitations and funding cuts.
3) Recent advances in AI from the 1990s to present using techniques like deep learning, access to large datasets, and increasing computational power which have led to applications in areas like logistics, data mining, and medical diagnosis.
AI Lecture 1 (introduction)
This document provides an overview of an artificial intelligence course titled CSE 412 taught in fall 2018. It introduces topics that will be covered like what AI is, the foundations and history of AI, the state of the art in AI, philosophical foundations, and logic programming using Prolog. The instructor is Tajim Md. Niamat Ullah Akhund and the document outlines the major sections and contents to be covered in the course.
What's hot (20)
DWX 2018 Session about Artificial Intelligence, Machine and Deep Learning
DWX 2018 Session about Artificial Intelligence, Machine and Deep Learning
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...
Similar to Understanding ai
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
AI Lesson 01
The document provides an introduction to artificial intelligence (AI), including:
1) Defining AI as designing intelligent systems and examining different views on what constitutes intelligence.
2) Describing typical AI problems like object recognition, language processing, and games, noting that expert tasks are now solvable by computers but common tasks remain challenging.
3) Discussing the practical impact of AI and different approaches like strong AI, weak AI, applied AI, and cognitive AI.
4) Noting the current limits of AI in areas requiring common sense knowledge or understanding unconstrained natural language.
Artificial intelligence
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
AI.ppt
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
Introduction to Artificial Intelligence.pptx
This document provides an introduction to artificial intelligence including definitions of intelligence and AI, applications of AI, and a brief history of the field. It discusses key concepts like agents in AI, different types of AI based on capabilities and functionality, goals and approaches of AI, and characteristics of AI programs such as symbolic processing, reasoning, and the ability to learn. The document also summarizes Alan Turing's famous 1950 test for machine intelligence.
Intro artificial intelligence
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
AI Introduction Artificial intelligence introduction fundamentals alogirthms ...
AI Introduction Artificial intelligence introduction fundamentals alogirthms ...Dr.ganesh Narasimhan
AI introductionARTIFICIAL INTELLIGENCETterm Paper
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Introduction to Knowledge Graphs
The document provides an introduction to knowledge graphs. It discusses how knowledge graphs are being used by large enterprises and intelligent agents to capture concepts, entities, and relationships within domains to drive business, generate insights, and enhance relationships. The presentation will cover an overview of what knowledge graphs are, who uses them, why they are used, and how to use them. It then provides some examples of how knowledge graphs are applied, including in intelligent agents, semantic web, search engines, social networks, biology, enterprise knowledge management, and more.
Lect 01, 02
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
Artificial_Intelligence
The document discusses artificial intelligence and how it works. It defines artificial intelligence as making computers do intelligent tasks like humans. It discusses neural networks which are composed of artificial neurons that mimic biological neurons. The document also discusses machine learning approaches like failure driven learning, learning by being told, and learning by exploration. Examples of applications of AI are given, like expert systems used in geology and medicine. The key differences between human and artificial intelligence are noted.
chapter 3 - Artificial Intelligence.pptx
The document provides an overview of artificial intelligence (AI), including definitions, components, types, applications, and levels. It defines AI as using computer science to create intelligent machines that can behave and think like humans. Intelligence involves reasoning, learning, problem-solving, perception, and language understanding. AI systems are composed of agents that perceive their environment and act on it. Examples of AI applications include autonomous vehicles, medical diagnosis, games, and online assistants. Machine learning is an advanced form of AI that allows machines to learn from experience rather than being explicitly programmed. The document also discusses the history of AI and describes six levels and two main types.
Artificial intelligence
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons that mimic biological neurons. Neural networks are composed of interconnected artificial neurons. The Turing test tests a machine's ability to demonstrate intelligence comparable to a human. There are different types of AI like expert systems, machine learning, and intelligent agents. While AI can process large amounts of data fast without human limitations, it lacks common sense, intuition, and creativity that humans possess. Overall, AI aims to supplement natural human intelligence by performing tasks through machines to reduce human labor and mistakes.
Chapter 3 - EMTE.pptx
The document provides an introduction to artificial intelligence, including what AI is, its history and development over different eras. It discusses the types and approaches of AI, including reactive machines, limited memory systems, theory of mind and self-awareness. It also outlines how AI systems map to human thinking processes and how factors like advances in computing, big data, cloud computing and data science have influenced AI's development. Finally, it gives examples of real-world AI applications in various fields such as transportation, healthcare, home services and public safety.
Emerging Technology chapter 3.pptx
Based on Capabilities
B. Based on Functionality
1. Reactive Machines:
- Reactive machines are the simplest form of AI. They perceive their environment and
respond in a predetermined manner to achieve a specific goal.
- Examples include thermostats, industrial robots, and vacuum cleaners.
2. Limited Memory:
- These systems can remember past experiences and use that information to guide future
actions.
- Examples include chess playing programs and self-driving cars.
3. Theory of Mind:
- These systems can model other agents and take their beliefs, intentions, and desires into
account.
- Examples include personal assistants like Siri that understand context.
4. Self
Artificial Intteligence-unit 1.pptx
The document discusses various topics related to artificial intelligence including definitions of AI, goals of AI, whether machines can think, the Turing test, types of AI tasks including mundane, formal and expert tasks, technologies based on AI such as machine learning, natural language processing, computer vision, and applications of AI such as in healthcare, gaming, finance, data security, social media, travel and more.
Ai introduction
This document provides an introduction to an artificial intelligence course. The course aims to give students knowledge and understanding of core AI concepts like search, game playing, planning and machine learning. Students will learn how to apply these concepts to construct simple AI systems using a declarative language. The document outlines several core areas of AI including knowledge representation, reasoning, planning, learning, and interacting with the environment. It also discusses the history of AI and provides examples of modern AI applications.
What is Artificial Intelligence?
AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or final conclusions) and self-correction. Particular applications of the AI include expert system speech recognition and artificial vision.
AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to...
Artificial intelligence and sensor networks may now be poised to disrupt various industries and jobs. Recent advances in algorithms, sensors, data collection, mobile technology, and robotics have increased concerns about the potential threats of artificial superintelligence ending humanity. The rapid changes in science and technology could significantly impact jobs in the coming decades as AI and automation replace many human roles.
Artificialintelligence 131226011156-phpapp02
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human intelligence.
Similar to Understanding ai (20)
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
AI Introduction Artificial intelligence introduction fundamentals alogirthms ...
AI Introduction Artificial intelligence introduction fundamentals alogirthms ...
AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to...
AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to...
Recently uploaded
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsVideo Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Building RAG with self-deployed Milvus vector database and Snowpark Container...
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Recently uploaded (20)
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Understanding ai
- 1. Understanding AI By Salam Ali Ahasan ECSE-09010043 NUB
- 8. Operational Definition of AI Systems that act like humans Turing test. Systems that think like humans Cognitive Science Systems that think rationally Logic-based AI Systems that act rationally Rational Agents
- 13. Representing Knowledge - Logic 1958 McCarthy’s paper, “Programs with Common Sense” at (I, car) can (go (home, airport, driving)) at (I, desk) can (go (desk, car, walking)) 1965 Resolution theorem proving invented
- 14. Representing Knowledge- Semantic Nets 1961
- 15. Representing Knowledge – Capturing Experience Representing Experience with Scripts, Frames, and Cases 1977 Scripts Rony went to a restaurant. Rony ordered a hamburger. When the hamburger came, it was burnt to a crisp. Rony stormed out without paying. The restaurant script: Did Rony eat anything?
- 16. Understanding in Expert Systems? Understanding requires: *Representation and manipulation of Domain Knowledge *Perceive analogies *Learn Pragmatic view: *Intentional intelligence is not required *Programs will work the better if more human domain knowledge is encoded in them Normative Descriptive Limitive Definition of Expert System: An Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice Expert Systems are a subfield of Artificial Intelligence. Term: Knowledge Based System