CS代考 CS188 Intro to AI at UC Berkeley]

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Introduction to Artificial Intelligence
Introduction to AI and Agent Design

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[These slides are partially adapted from the UC Berkeley. CS188 Intro to AI at UC Berkeley]

Introduction
What is artificial intelligence?
What can AI do?
What is this course?

Agent design

Sci-Fi AI?

Sci-Fi AI?

Star War (1977)

i, Robot (2004)

Iron Man (2008)

EX Machina (2015)

Star Trek (1994)

Bicentennial Man (1999)

Sci-Fi AI?

Bicentennial Man (1999)

Some definitions of AI
“The scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines”
– American Association of Artificial Intelligence (AAAI)

“Artificial Intelligence is the branch of computer science that is concerned with the automation of intelligent behavior”
– Luger and Stubblefield

“AI strives to understand and build intelligent entities (also helps us to learn more about ourselves)”
– Russell and Norvig

What is AI?
The science of making machines that:
Think like people
Act like people
Think rationally
Act rationally

What is AI?

How can we test it?

The Turing Test
Ingredients:
Human Judge, unknown Entity
2 computer terminals in different room

Have human Judge sit at one computer
Judge converses with Entity via chat window for 5 minutes
Judge decides if Entity is human or computer

Left hand side
Judge: Hi what’s your name?
Entity: I don’t know
Judge: My name’s Jane. I live in London. Where do you live?
Entity: I live in London as well
Judge: Do you like living in London?
Entity: Yes, I like it because there are a lot of things to do, and it is the capital city of England
Judge: What’s my name?  
Entity: sorry?  
Judge: What’s my name?
Entity: : And what’s your name?
Entity: I don’t know

Right hand side
Judge: What color is the carpet in your room
Entity: I like pink; what do you like?
Judge: I like pink. My name is John. What’s your name?
Entity: Red. Hello John. Its nice to meet such a fine person as yourself.
Judge: Red is my favorite color. What’s yours?
Entity: My favorite color is red.
Judge: You use American spelling. Are you in the USA?
Entity: But you are not even American. Just remember atheism is only a belief system. And it too can be forced on people who don’t agree.
Judge: How do you like your eggs – ever easy or sunny side up?
Entity: How I wonder if I like my eggs-over easy or sunny side up? This is so explainable. That list makes me feel unsure John.

 Which is which?
Example taken from “Turing’s Imitation Game,” by Warwick and Shah (2016)

How to do well in the Turing Test?
Chatting “like a human” involves:
Generating coherent language
Responding appropriately to unexpected inputs

What is not shown?
The Turing Test does not show
Understanding or thinking
Ability to learn
Interaction with an unconstrained world
Processing sensory input
Having knowledge
Much of anything useful, really

Act rationally; rational decisions
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
Rational behavior = doing the right thing, does not necessarily involve thinking

Act rationally; rational decisions
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
Rational behavior = doing the right thing, does not necessarily involve thinking

Goal: do well in the CSE 3521 course
Utility: final grade
Being rational: review math material!

A (** Short **) history of AI
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, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine
1956: Dartmouth meeting: the term “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—: Statistical approaches
Resurgence of probability, focus on uncertainty
General increase in technical depth
Agents and learning systems… “AI Spring”?

2000—: Where are we now? (Machine learning, neural networks, deep learning, …)

Practice: Which of the following can be done at present?

Play a decent game of table tennis?
Play a decent game of Jeopardy/Go/Atari/Star raft?
Drive safely along a curving mountain road (w/o other traffic agents)?
Drive safely along High Street?
Buy a week’s worth of groceries on the web?
Buy a week’s worth of groceries at Market?
Discover and prove a new mathematical theorem?
Converse successfully with another person for an hour?
Perform a surgical operation?
Put away the dishes and fold the laundry?
Translate spoken Chinese into spoken English in real time?
Write an intentionally funny story?

What can AI do?

Questions?

Important research areas related to AI
Speech processing
Natural language processing
Computer vision
Game playing
Decision making
Autonomous driving

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

Language processing technologies
Question answering
Machine translation

Web search
Text classification, spam filtering, etc…

Speech processing & Natural language processing

Computer vision
[Source: Detectron2]
Object and face recognition
Scene segmentation
Image classification

[Source: /Popular Science]

Part mechanical engineering
Reality much harder than simulations!

Technologies:
Lots of automation …

In this class:
We ignore mechanical aspects
Methods for planning (and/or control)
[Images from UC Berkeley, Boston Dynamics, RoboCup, Google]

Logical systems:
Theorem provers
NASA fault diagnosis
Question answering

Deduction systems
Constraint satisfaction
Satisfiability solvers (huge advances!)

[ Image from ]

Game playing
Classic Moment: May, ’97: Deep Blue vs. Kasparov
First match won against chess 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

Open question:
How does human cognition deal with the
search space explosion of chess?
Or: how can humans compete with computers at all?

1996: Kasparov beats Deep Blue
“I could feel — I could smell — a new kind of intelligence across the table.”

1997: Deep Blue beats Kasparov
“Deep Blue hasn’t proven anything.”

Huge game-playing advances recently, e.g., in Go, Atari, and Computer games!

[ Text from , image from IBM’s Deep Blue pages, Google DeepMind]

Decision making
Applied AI involves many kinds of automation
Scheduling, e.g., airline routing, military
Route planning, e.g., Google maps
Medical diagnosis
Web search engines
Spam classifiers
Automated help desks
Fraud detection
Product recommendations
… Lots more!

Autonomous driving

Perception

Prediction & Planning

Action & decision

This course
Search: How do I (efficiently) find a solution?
Logic and Knowledge: How do I determine what is/isn’t known?
Decision Policies: How do I choose the best next action?
Probability: How do I handle dependency and non-determinism?  
Machine Learning: How do I learn from data and/or experience?

This course (topics)
AI agent design
Symbolic AI & Logical Inference
Data-to-features and dimensionality reduction
General parameter estimation techniques
Probability basics and probabilistic methods
Unsupervised learning
Supervised learning
Neural networks and deep learning
Special topic?

The Fundamental question for this lecture
(and really this whole AI field!):

How do you turn a real-world

problem into an AI solution?

AI – agents and environments
Much (though not all) of AI is concerned with agents operating in environments.
Environment – the problem setting
Agent – an entity that perceives its environment through sensors and acts upon that environment through effectors (actuators)

AI – agents and environments
Environment

perception

Sensors: eyes, ears, etc.
Effectors: hands, legs, mouth, etc.
Sensors: cameras, 3D sensors, etc.
Effectors: various motors, robot arms, etc.
Cross walks

AI – agents and environments
Environment

perception

Sensors: eyes, ears, etc.
Effectors: hands, legs, mouth, etc.
Sensors: cameras, 3D sensors, etc.
Effectors: various motors, robot arms, etc.
Cross walks
Percept: Agent’s perceptual inputs at any given instant
Percept sequence: Complete history of everything agent has perceived
Agent’s choice of action (e.g., walk forward for a step, push the bottom) can depend on entire percept sequence

Fleshing it out
Performance – measuring the agent’s success

Environment – what populates the problem’s world?

Actuators – what can the agent act with?

Sensors – how can the agent perceive the world?

Peas in autonomous taxi
Performance – Safe, fast, legal, and comfortable trip; maximize profits

Environment – Roads, other traffic agents (e.g., pedestrians), customers

Actuators – Steering, accelerator, brake, signals, horn, display

Sensors – Cameras, sonar, LiDAR, radar, speedometer, GPS, odometer,
accelerometer, engine sensors, microphone/keyboard

Peas: Other examples
Agent Type Performance measure Environment Actuators Sensors
Medical diagnosis system Healthy patient, minimize costs/lawsuits Patient, hospital, staff Display questions, tests, diagnoses, treatments, referrals Keyboard entry of symptoms, findings, patient’s answers
Satellite image analysis system Correct image classification Downlink from orbiting satellite Display classification of scene Color pixel arrays (cameras)
Part-picking robot Percentage of parts in correct bins Conveyor belt with parts, bins Jointed arm and hand Camera, joint angle sensors
Refinery controller Maximize purity, yield, safety Refinery, operators Valves, pumps, heaters, displays Temperature, pressure, chemical sensors
Interactive English tutor Maximize student’s score on test Set of students, testing agency Display exercises, suggestions, corrections Keyboard entry

In class exercise
Give a ‘PEAS’ description of the task environment for the following vacuum-cleaner world with four locations.

In class exercise
Give a ‘PEAS’ description of the task environment for the following vacuum-cleaner world with four locations.

Performance – cleanness, efficiency, distance traveled

Environment – room with 4 squares

Actuators – wheels, brushes, vacuum extractor

Sensors – dirt detection

Introduction to AI
Act like humans vs. act rationally
AI applications

Agent design (part 1)
Agents and environments
PEAS: performances, environments, actuators, sensors.

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