Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

ai presentation introduction

thankgiving

5 templates

ai presentation introduction

94 templates

ai presentation introduction

11 templates

ai presentation introduction

american history

85 templates

ai presentation introduction

tropical island

31 templates

Artificial Intelligence

It seems that you like this template, artificial intelligence presentation, free google slides theme, powerpoint template, and canva presentation template.

Welcome to the future, where robots might just become your new best friend! In this exciting lesson on artificial intelligence, we're going to delve into the wacky world of machines that can think for themselves. Forget what you may have seen in sci-fi movies – today, we're going to take a real-life look at how these futuristic technologies are shaping our world. With this lesson curated by educators, we'll explore the endless possibilities of AI – from the way we communicate and learn, to the way we work and live. So, sit back, relax, and get ready to have your mind blown by the power of AI!

Features of this template

  • Designed for Middle School
  • 100% editable and easy to modify
  • 17 different slides to impress your audience
  • Contains easy-to-edit graphics such as graphs, maps, tables, timelines and mockups
  • Includes 500+ icons and Flaticon’s extension for customizing your slides
  • Designed to be used in Google Slides, Canva, and Microsoft PowerPoint
  • 16:9 widescreen format suitable for all types of screens
  • Includes information about fonts, colors, and credits of the resources used
  • Available in different languages

How can I use the template?

Am I free to use the templates?

How to attribute?

Attribution required If you are a free user, you must attribute Slidesgo by keeping the slide where the credits appear. How to attribute?

Create your presentation create personalized presentation content, writing tone, number of slides, available in.

ai presentation introduction

Register for free and start downloading now

Related posts on our blog.

How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides | Quick Tips & Tutorial for your presentations

How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides

How to Change Layouts in PowerPoint | Quick Tips & Tutorial for your presentations

How to Change Layouts in PowerPoint

How to Change the Slide Size in Google Slides | Quick Tips & Tutorial for your presentations

How to Change the Slide Size in Google Slides

Related presentations.

AI Tech Company presentation template

Premium template

Unlock this template and gain unlimited access

The Use of AI in Marketing presentation template

Register for free and start editing online

foundations of artificial intelligence

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

Jul 13, 2014

460 likes | 1.47k Views

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE. Introduction: Chapter 1. Outline. Course overview What is AI? A brief history The state of the art. Course overview. Introduction and Agents (chapters 1,2) Search (chapters 3,4,5,6) Logic (chapters 7,8,9) Planning (chapters 11,12)

Share Presentation

  • boolean circuit model
  • turing test
  • logical deliberation
  • autonomous planning program

toril

Presentation Transcript

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE Introduction: Chapter 1

Outline • Course overview • What is AI? • A brief history • The state of the art

Course overview • Introduction and Agents (chapters 1,2) • Search (chapters 3,4,5,6) • Logic (chapters 7,8,9) • Planning (chapters 11,12) • Uncertainty (chapters 13,14) • Learning (chapters 18,20) • Natural Language Processing (chapter 22,23)

What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally"

Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?"  "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning

Thinking humanly: cognitive modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) • are now distinct from AI

Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

Acting rationally: rational agent • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

Rational agents • An agent is an entity that perceives and acts • This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: [f: P*A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

AI prehistory • Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality • Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability • Economics utility, decision theory • Neuroscience physical substrate for mental activity • Psychology phenomena of perception and motor control, experimental techniques • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammar

Abridged history of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1952—69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents

State of the art • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • Proved a mathematical conjecture (Robbins conjecture) unsolved for decades • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people • NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft • Proverb solves crossword puzzles better than most humans

  • More by User

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Bart Selman [email protected] Machine Learning: Decision Trees R&N 18.3. Big Picture of Learning. Learning can be seen as fitting a function to the data. We can consider

940 views • 70 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Carla P. Gomes [email protected] Module: Neural Networks Multi-Layer-Perceptron (Reading: Chapter 20.5). Perceptron: Linear Separable Functions. x 2. Not linearly separable. +. -. XOR. x 1. -. +. Minsky & Papert (1969)

455 views • 22 slides

Foundations of Artificial Intelligence

Foundations of Artificial Intelligence

Outline. History of Reasoning FOL Inference Rules Generalized Modus Ponens Unification and MGU Soundness and Completeness Forward and Backward Chaining using GMP. A Brief History of Reasoning. 450BCE Stoics PL, inference (?) 32BCE Aristotle inference rules (syllogisms), quantifiers1

934 views • 56 slides

The Foundations of Artificial Intelligence

The Foundations of Artificial Intelligence

The Foundations of Artificial Intelligence. Our Working Definition of AI. Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if: they could extend what they do to a World Wide Web-sized amount of data and

807 views • 38 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Bart Selman [email protected] Module: Constraint Satisfaction Chapter 6, R&N (Completes part II – Problem Solving). Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs.

1.08k views • 81 slides

Logical Foundations of Artificial Intelligence

Logical Foundations of Artificial Intelligence

Logical Foundations of Artificial Intelligence. Markov Logic II. Markov Logic II. Summary of last class Applications Weight learning Formula learning. Markov Networks. Smoking. Cancer. Undirected graphical models. Asthma. Cough. Log-linear model:. Weight of Feature i. Feature i.

708 views • 55 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Carla P. Gomes [email protected] Module: CSP1 (Reading R&N: Chapter 5). Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs. Motivational Example: 8-Queens. Goal: place 8 non-attacking Queens. .

1.12k views • 90 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Carla P. Gomes [email protected] Module: Ensemble Learning (Reading: Chapter 18.4). Ensemble Learning. So far – learning methods that learn a single hypothesis , chosen form a hypothesis space that is used to make predictions.

528 views • 36 slides

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE. Introduction: Chapter 1. Introduction to AI. Course home page: http://www2.mta.ac.il/~gideon/ai.html Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Lecturer: Gideon Dror [email protected].

359 views • 13 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Carla P. Gomes [email protected] Module: Computational Learning Theory: PAC Learning (Reading: Chapter 18.5). Computational learning theory. Inductive learning:

389 views • 28 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Instructor: Prof. Selman [email protected] Introduction (Reading R&N: Chapter 1). Course Administration. Office hours and web page early next week Text book: Russell & Norvig --- Artificial Intelligence: A Modern Approach (AIMA).

621 views • 41 slides

CS3243	FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE. AY2003/2004 Semester 2 Introduction: Chapter 1. CS3243. Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition

394 views • 13 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Bart Selman Reinforcement Learning R&N – Chapter 21 Note: in the next two parts of RL, some of the figure/section numbers refer to an earlier edition of R&N with a more basic description of the techniques.

294 views • 15 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Bart Selman [email protected] Module: Informed Search Readings R&N - Chapter 3: 3.5 and 3.6. Search. Search strategies determined by choice of node (in queue) to expand Uninformed search: Distance to goal not taken into account

733 views • 64 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Carla P. Gomes [email protected] Module: Nearest Neighbor Models (Reading: Chapter 20.4). 1-Nearest Neighbor. One of the simplest of all machine learning classifiers Simple idea: label a new point the same as the closest known point.

206 views • 9 slides

CS3243	FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE. AY2003/2004 Semester 2 Introduction: Chapter 1. CS3243. Course home page: http://www.comp.nus.edu.sg/~cs3243 IVLE for schedule, lecture notes, tutorials, assignment, grading, office hours, etc.

283 views • 13 slides

The Foundations of Artificial Intelligence

432 views • 38 slides

CS3243	FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

303 views • 13 slides

IMAGES

  1. Introduction To Artificial Intelligence Powerpoint Presentation Slides

    ai presentation introduction

  2. Introduction To Artificial Intelligence Powerpoint Presentation Slides

    ai presentation introduction

  3. Artificial Intelligence PowerPoint Template Designs

    ai presentation introduction

  4. Artificial Intelligence PowerPoint Slide Template

    ai presentation introduction

  5. PPT

    ai presentation introduction

  6. Artificial Intelligence Introduction AI PPT Slides

    ai presentation introduction