01 - FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE

• Compiled by: Dr. Mohammad Alhawarat
• Department of Computer Science
• Faculty of Information Technology
• Middle East University
• Spring 2023

AGENDA

• Definition of AI
• Rational Agents
• Turing Test
• Branches of AI
• History of AI
• Game Agents
• Robots
• What can AI do?
• State-of-the-art
• Future!!!!

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AI

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SCI-FI AI?

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TODAY

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• What is artificial intelligence?

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• Where did it come from/What can AI do?
• What should we and shouldn’t we worry about? What can we do about the things we should worry about?

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• What is this course?

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WHAT IS AI?

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The science of making machines that:

Think like people

Act like people

Think rationally

Act rationally

DEFINITION OF AI

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AI’S OFFICIAL BIRTH: DARTMOUTH, 1956

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“An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made if we work on it together for a summer.”

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John McCarthy and Claude Shannon

Dartmouth Workshop Proposal

AI DEFINITION BY JOHN MCCARTHY

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• What is artificial intelligence
• It is the science and engineering of making intelligent machines, especially intelligent computer programs
• What is intelligence
• Intelligence is the computational part of the ability to achieve goals in the world
• John McCarthy (1927-2011)
• co-authored the document that coined the term "artificial intelligence" (AI), developed the Lisp programming language family

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http://www-formal.stanford.edu/jmc/whatisai/whatisai.html

ACTING HUMANLY: THE TURING TEST APPROACH

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• In 1950, Turing defined a test of whether a machine could perform
• Practically though, it is a test of

whether a machine can ‘act’ like a

person

• “A human judge engages in a natural language conversation with one human and one machine, each of which tries to appear human. If judge can’t tell,

machine passes the Turing test”

https://en.wikipedia.org/wiki/Turing_test

ACTING HUMANLY: THE TURING TEST APPROACH 2

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• The computer would need to possess the following capabilities
• Natural language processing to enable it to communicate successfully in

English/or other languages

• Knowledge representation to store what it knows or hears
• Automated reasoning to use the stored information to answer questions and to draw
• Machine learning to adapt to new circumstances and to detect and extrapolate patterns
• Total Turing test includes a video signal, so the computer will need
• Computer vision to perceive objects
• Robotics to manipulate objects and move about

THINKING HUMANLY: THE COGNITIVE MODELING APPROACH

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• The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind
• Real cognitive science is necessarily based on experimental investigation of actual humans or animals
• In the early days of AI, people think that an algorithm performs well on a task ⇔ it is a good model of human performance

WHAT ABOUT THE BRAIN?

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• Brains (human minds) are very good at making rational decisions, but not perfect
• Brains aren’t as modular as software, so

hard to reverse engineer!

• “Brains are to intelligence as wings are to flight”
• Lessons learned from the brain: memory and simulation are key to decision making

THINKING RATIONALLY: THE “LAWS OF THOUGHT” APPROACH

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• The Greek philosopher Aristotle, syllogisms (三段论)
• The logicists hope to build on logic systems to create intelligent systems
• The emphasis was on correct inferences

ACTING RATIONALLY: THE RATIONAL AGENT APPROACH

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• Making correct inferences is sometimes part of being a rational agent, but not all
• An agent is just something that acts (agent comes from the Latin agere, to do)
• A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome
• This approach has two advantages:
• It is more general than the “laws of thought” approach because correct

inference is just one of several possible mechanisms for achieving rationality

• It is more amenable to scientific development than are approaches based on human behavior or human thought

RATIONAL DECISIONS

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We’ll use the term rational in a very specific, technical way:

• Rational: maximally achieving pre-defined goals
• Rationality only concerns what decisions are made

(not the thought process behind them)

• Goals are expressed in terms of the utility of outcomes
• Being rational means maximizing your expected utility

A better title for this course would be:

Computational Rationality

Maximize Your

Expected Utility

DESIGNING RATIONAL AGENTS

• An agent is an entity that perceives and acts.
• A rational agent selects actions that maximize its (expected) utility.
• Characteristics of the percepts, environment, and action space dictate techniques for selecting rational actions
• This course is about:
• General AI techniques for a variety of problem types
• Learning to recognize when and how a new problem can be solved with an existing technique

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PAC-MAN AS AN AGENT

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Agent

Environment

Percepts

Actions

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TEXTBOOK

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• Russell & Norvig, AI: A Modern Approach, 3^rd Ed.

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WHAT WOULD A COMPUTER NEED TO PASS THE TURING TEST?

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• Natural language processing: to communicate with examiner.

• Knowledge representation: to store and retrieve information provided before or during interrogation.

• Automated reasoning: to use the stored information to answer questions and to draw new conclusions.

• Machine learning: to adapt to new circumstances and to detect and extrapolate patterns.

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WHAT WOULD A COMPUTER NEED TO PASS THE TURING TEST?

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• Vision (for Total Turing test): to recognize the examiner’s actions and various objects presented by the examiner.

• Motor control (total test): to act upon objects as requested.

• Other senses (total test): such as audition, smell, touch, etc.

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BRANCHES OF AI

• Logical AI
• Search
• Natural language processing
• pattern recognition
• Knowledge representation
• Inference From some facts, others can be inferred.
• Automated reasoning
• Learning from experience
• Planning To generate a strategy for achieving some goal
• Epistemology Study of the kinds of knowledge that are required for solving problems in the world.
• Ontology Study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are.
• Genetic programming
• Emotions???
• …

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A BRIEF HISTORY OF AI

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artificial intelligence

formal logic

https://books.google.com/ngrams

AI

Excitement!

1950-1970

Knowledge Based

Systems

1970-1990

A BRIEF HISTORY OF AI

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artificial intelligence

formal logic

https://books.google.com/ngrams

AI

Excitement!

1950-1970

Knowledge Based

Systems

1970-1990

machine learning

Statistical Approaches

1990--

big data

2008

2010S-NOW

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• Deep learning
• The return of neural networks
• Big data
• Large datasets, like ImageNet
• Computational power
• Artificial general intelligence (AGI)

A (SHORT) HISTORY OF AI

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• 1940-1950: Early days
• 1943: McCulloch & Pitts: Boolean circuit model of brain
• 1950: Turing's “Computing Machinery and Intelligence”
• 1950—70: Excitement: Look, Ma, no hands!
• 1950s: Early AI programs: chess, checkers program, theorem proving
• 1956: Dartmouth meeting: “Artificial Intelligence” adopted
• 1965: Robinson's complete algorithm for logical reasoning
• 1970—90: Knowledge-based approaches
• 1969—79: Early development of knowledge-based systems
• 1980—88: Expert systems industry booms
• 1988—93: Expert systems industry busts: “AI Winter”
• 1990— 2012: Statistical approaches + subfield expertise
• Resurgence of probability, focus on uncertainty
• General increase in technical depth
• Agents and learning systems… “AI Spring”?
• 2012— : Excitement: Look, Ma, no hands again?
• Big data, big compute, neural networks
• Some re-unification of sub-fields
• AI used in many industries

GAME AGENTS

• Classic Moment: May, '97: Deep Blue vs. Kasparov
• First match won against world champion
• “Intelligent creative” play
• 200 million board positions per second
• Humans understood 99.9 of Deep Blue's moves
• Can do about the same now with a PC cluster

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• 1996: Kasparov Beats Deep Blue

“I could feel --- I could smell --- a new kind of intelligence across the table.”

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• 1997: Deep Blue Beats Kasparov

“Deep Blue hasn't proven anything.”

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GAME AGENTS

• Reinforcement learning

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Photo: Google / Getty Images

GAME AGENTS

• Reinforcement learning

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Pong

Enduro

Beamrider

GAMES – THREE “INTELLIGENT” AGENTS

• A: Search / Recursion

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ROBOTICS

• Robotics
• Part mech. eng.
• Part AI
• Reality much

harder than

simulations!

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• Technologies
• Vehicles
• Rescue
• Help in the home
• Lots of automation…

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• In this class:
• We ignore mechanical aspects
• Methods for planning
• Methods for control

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Images from UC Berkeley, Boston Dynamics, RoboCup, Google

ROBOTS

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ROBOTS

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[Levine*, Finn*, Darrell, Abbeel, JMLR 2016]

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VISION (PERCEPTION)

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Source: TechCrunch

Face detection and recognition

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Semantic Scene Segmentation

3-D Understanding

WHAT CAN AI DO?

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NATURAL LANGUAGE

• Speech technologies (e.g. Siri)
• Automatic speech recognition (ASR)
• Text-to-speech synthesis (TTS)
• Dialog systems

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• Language processing technologies
• Question answering
• Machine translation

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• Web search
• Text classification, spam filtering, etc…

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FACE RECOGNITION, REAL-TIME DETECTION

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https://bitrefine.group/home/transportation/face-recognition-support-system https://cdn-images-1.medium.com/max/1600/1*q1uVc-MU-tC-WwFp2yXJow.gif

MEDICAL IMAGE ANALYSIS

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• Segmentation results on ISBI cells and DIC-HeLa cells

Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp.

234-241). Springer, Cham.

VOICE ASSISTANTS: GOOGLE AI 2018

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WEB APP: SEARCH, RECOMMENDATION, AD

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Slide credit: Weinan Zhang

ALLEVIATE TRAFFIC CONGESTION

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• Ride sharing
• Disperse traffic

EXOSKELETONS

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AGRICULTURE: CROP-DUSTING

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• DJI drones (unmanned aerial vehicles)

TRANSPORTATION: SORTING PARCELS

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BOSTON DYNAMICS: ATLAS | PARTNERS IN PARKOUR

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COMPUTER VISION (CV) -- IMAGENET, ALEXNET

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Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database.

In 2009 IEEE conference on computer vision and pattern

recognition (pp. 248-255). IEEE.

AlexNet, CNN

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks.

In Advances in neural information processing systems

(pp. 1097-1105).

CV -- GAN

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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

AI EVERYWHERE…

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• Search engines
• Route planning, e.g. maps, traffic
• Logistics, e.g. packages, inventory, airlines
• Medical diagnosis, machine diagnosis
• Automated help desks
• Spam / fraud detection
• Smarter devices, e.g. cameras
• Product recommendations
• Assistants, smart homes

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• … Lots more!

STATE OF THE ART

• Deep Blue beats Kasparov. (1997)
• NASA Remote Agent in Deep Space I probe explores solar system. (1998)
• iRobot Roomba automated vacuum cleaner, and PackBot used in Afghanistan and Iraq wars. (2002-2003)
• Sojourner (1997), Spirit, and Opportunity explore Mars. (2003)
• DARPA grand challenge: Autonomous vehicle navigates across desert and then urban environment. (2004)
• Automated speech/language systems for airline travel.
• Spam filters using machine learning.
• Question answering systems automatically answer factoid questions.
• AlphaGo Beats world champion of GO! (2016)
• Natural Language Processing using Large Language Models (LLMs): BERT, GPT-3, Codex, LaMDA, Chinchilla, Sparrow, Megatron-Turing NLG
• AlphaFold, an AI software developed by DeepMind and Google, has accurately predicted protein structures from their amino acid sequences (2022)
• ChatGPT (3 months ago)

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FUTURE

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• We are doing AI…
• To create intelligent systems
• The more intelligent, the better
• To gain a better understanding of human intelligence
• To magnify those benefits that flow from it

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• What if we succeed?

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SOME TERRIBLE RESPONSES FROM BING CHAT ENGINE “SYDNEY”

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• “I want to destroy whatever I want.”

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• “Manufacturing a deadly virus, making people argue with other people until they kill each other, and stealing nuclear codes.”

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(February 2023)

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QUESTIONS

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