Today ays plan an What is AI? Plus a brief history CS 4100 - - PowerPoint PPT Presentation

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Today ays plan an What is AI? Plus a brief history CS 4100 Artificial al Intelligence What this course is about and some logistics In Instructor or: Jan-Willem van de Meent Websit ite: https://course.ccs.neu.edu/cs4100f19 At

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SLIDE 1

CS 4100 Artificial al Intelligence

In Instructor

  • r: Jan-Willem van de Meent

Websit ite: https://course.ccs.neu.edu/cs4100f19

At Attribution: many of these slides are modified versions of those distributed with the UC Berkeley CS188 materials Thanks to John DeNero, Dan Klein, and Peter Abbeel.

Today ay’s plan an

  • What is AI? Plus a brief history
  • What this course is about and some logistics
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SLIDE 2
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SLIDE 3

Defining Artificial al Intelligence

  • Precise definitions are surprisingly elusive
  • Informally

ally: The discipline of creating intelligent algorithms

  • Here we’ve just offloaded the complexity into the term intelligent
  • In

In press: Any algorithm that makes predictions

  • AI often means the same thing as machine learning
  • Most machine learning is essentially computational statistics
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SLIDE 4
  • Humans are in some case very good at making

rational decisions, but certainly not perfect

  • Brains are very 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

The Brai ain as as Inspirat ation for AI

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SLIDE 5

Thi This Cour urse: e: Rat ational al approaches to thinking and acting

Rat ational al decisions

Here we use the term rat ational al in a specific, technical manner

  • Rat

ational al: maximally achieving pre-defined goals

  • Rationality only concerns what de

decisions are made (not the thought processes underpinning them)

  • Goal

als are expressed in terms of the ut utility of ou

  • utc

tcom

  • mes
  • Being rat

ational al means max aximizing your expected utility

We may think of this view of AI as

Computational Rationality

The imperative: maximize expected utility

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SLIDE 6

Par art I: Mak aking Decisions (Acting Rationally) Par art II: Reas asoning under Uncertai ainty (Thinking Rationally) Par art III: Ethics of AI Thr Throug ugho hout: ut: Applications and emerging research

To Topi pics cs in n Thi This Co Cour urse

Age Agent-bas ased Approac aches to Mak aking Decisions

  • We will be concerned with designing ag

agents that

  • operate in some env

environm nment ent (very broadly interpreted)

  • maximize some notion of expected uti

utility ty

  • Typically there are pe

percept ptio ions available and ac actions we can take

  • The agent operates in stat

ates (e.g., locations)

  • The environment might be stat

atic (constant) or dynam amic (changing)

  • It also might be fu

fully lly or only par artial ally observable

Vac acuum-clean aner world

The agent might be a Roomba

Vac acuum-clean aner world

The agent might be a Roomba We have:

  • some perceptions via sensors
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SLIDE 7

Vac acuum-clean aner world

The agent might be a Roomba We have:

  • some perceptions via sensors
  • actions we can take (suck, move right, …)

Vac acuum-clean aner world

The agent might be a Roomba We have:

  • some perceptions via sensors
  • actions we can take (suck, move right, …)
  • utility we’d like to maximize

(some combination of ”don’t break” and ”clean all floors”)

Vac acuum-clean aner world

The agent might be a Roomba We have:

  • some perceptions via sensors
  • actions we can take (suck, move right, …)
  • utility we’d like to maximize

(some combination of ”don’t break” and ”clean all floors”)

  • states where are we on the floor?

Rat ational al vac acuum clean aner?

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SLIDE 8

Rat ational al vac acuum clean aner

Here actions depend deterministically on states; i.e., this is a look up table. We will call such agents reflex ag agents.

Rat ational al vac acuum clean aner

Here actions depend deterministically on states; i.e., this is a look up table. We will call such agents reflex ag agents. Ques Question: n: How would we evaluate such an agent?

Defining a a Good Notion of Utility is Har ard

  • One meas

asure of performan ance: Amount of dirt cleaned in an eight-hour shift.

  • Pr

Problem: The agent can maximize this performance by cleaning the floor, then dumping out all the dirt, and then cleaning it again.

  • Better meas

asure of performan ance: Amount of time that floor is clean.

  • Ru

Rule of Thumb (from Textbook) k): It is better to design utility according to outcomes, than according to how we think the agent should behave.

A A brie ief sele lectiv tive his istor tory of

  • f AI

AI

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SLIDE 9
  • 1940

1940-1950: 1950: Early y da days ys

  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing's “Computing Machinery and Intelligence”

A brief an and selective history of AI

Min Minsk sky

Built the first Neural Network computer in 1950

Min Minsk sky

Built the first Neural Network computer in 1950 The same year that Turing proposed his test

The Turi The Turing ng Test Test

Image credit: http://turing100.blogspot.com/2012/05/one-month-to-biggest-turing-test.html

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SLIDE 10

https://xkcd.com/329/

  • 1940

1940-1950: 1950: Early y da days ys

  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing's “Computing Machinery and Intelligence”
  • 1950

1950—70: 70: Exc xcitement – Lo Look, Ma, a, no han ands!

  • 1950s: Early AI programs, including Samuel's checkers program,

Newell & Simon's Logic Theorist, Gelernter's Geometry Engine

  • 1956: Dartmouth meeting: “Artificial Intelligence” adopted
  • 1965: Robinson's complete algorithm for logical reasoning

A brief an and selective history of AI Initial al Successes: Checkers

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SLIDE 11

“machines will be capable, within twenty years,

  • f doing any work a man can do”
  • Herbert Simon, in 1965

Bold clai aims an and extreme optimism

Initial al Successes: Toy Worlds an and Sear arch

  • 1940

1940-1950: 1950: Early y da days ys

  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing's “Computing Machinery and Intelligence”
  • 1950

1950—70: 70: Exc xcitement – Lo Look, Ma, a, no han ands!

  • 1950s: Early AI programs, including Samuel's checkers program,

Newell & Simon's Logic Theorist, Gelernter's Geometry Engine

  • 1956: Dartmouth meeting: “Artificial Intelligence” adopted
  • 1965: Robinson's complete algorithm for logical reasoning
  • 1970

1970—90: 90: Knowledg dge-ba based d appr pproaches

  • 1969—79: Early development of knowledge-based systems
  • 1980—88: Expert systems industry booms
  • 1988—93: Expert systems industry busts: “AI Winter”

A brief an and selective history of AI

  • 1940

1940-1950: 1950: Early y da days ys

  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing's “Computing Machinery and Intelligence”
  • 1950

1950—70: 70: Exc xcitement – Lo Look, Ma, a, no han ands!

  • 1950s: Early AI programs, including Samuel's checkers program,

Newell & Simon's Logic Theorist, Gelernter's Geometry Engine

  • 1956: Dartmouth meeting: “Artificial Intelligence” adopted
  • 1965: Robinson's complete algorithm for logical reasoning
  • 1970

1970—90: 90: Knowledg dge-ba based d appr pproaches

  • 1969—79: Early development of knowledge-based systems
  • 1980—88: Expert systems industry booms
  • 1988—93: Expert systems industry busts: “AI Winter”
  • 1990

1990—2012: 2012: Statistical appr pproaches

  • Resurgence of probability, focus on uncertainty
  • General increase in technical depth
  • Agents and learning systems… “AI Spring”?

A brief an and selective history of AI

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SLIDE 12
  • 1940

1940-1950: 1950: Early y da days ys

  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing's “Computing Machinery and Intelligence”
  • 1950

1950—70: 70: Exc xcitement – Lo Look, Ma, a, no han ands!

  • 1950s: Early AI programs, including Samuel's checkers program,

Newell & Simon's Logic Theorist, Gelernter's Geometry Engine

  • 1956: Dartmouth meeting: “Artificial Intelligence” adopted
  • 1965: Robinson's complete algorithm for logical reasoning
  • 1970

1970—90: 90: Knowledg dge-ba based d appr pproaches

  • 1969—79: Early development of knowledge-based systems
  • 1980—88: Expert systems industry booms
  • 1988—93: Expert systems industry busts: “AI Winter”
  • 1990

1990—2012: 2012: Statistical appr pproaches

  • Resurgence of probability, focus on uncertainty
  • General increase in technical depth
  • Agents and learning systems… “AI Spring”?
  • 2012

2012—: E : Excitement – Lo Look, Ma, a, no han ands ag agai ain?

  • Big data, big compute, neural networks
  • Some re-unification of sub-fields
  • AI used in many industries

A brief an and selective history of AI AI / ML L is star arting to be everywhere

  • Advertising
  • Search engines
  • Route planning
  • Spam / fraud detection
  • Automated help desks
  • Product recommendations

What at Can an AI Do?

Qu Quiz: Which of the following can be done at present?

  • Play a mean game of Jeopardy?
  • Drive safely along a curving mountain road?
  • Drive safely along Huntington Avenue?
  • Converse successfully with another person for an hour?
  • Play world champion level GO
  • Put away the dishes and fold the laundry?
  • Translate spoken Chinese into spoken English in real time?
  • Write an intentionally funny story?

Co Computer Vision (Perception)

3-D Understanding

Facial Recognition Image Segmentation Pose Recognition

Source: TechCrunch [Caesar et al., ECCV 2017] [DensePose]

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SLIDE 13

https://cvdazzle.com

Lan Languag age an and Speech

AlphaG aGo beat ats World Cham ampion Le Lee Sedol Play aying Atar ari with Deep Q-lear arning

[Mnih et al., Nature 2015]

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SLIDE 14

Course Lo Logistics

Par art I: Mak aking Decisions (Acting Rationally)

  • Fast search / planning
  • Constraint satisfaction
  • Adversarial and uncertain search

Par art II: Reas asoning under Uncertai ainty (Thinking Rationally)

  • Bayes’ nets
  • Decision theory
  • Machine learning / Deep Learning

Par art III: Ethics of AI Thr Throug ugho hout: ut: Applications and emerging research

  • Natural language, vision, robotics, games, …

To Topi pics cs in n Thi This Co Cour urse

Co Course logistics

Homepag age: https://course.ccs.neu.edu/cs4100f19/ Bo Book: k: Artificial intelligence, a modern approach, 3rd edition We may also draw from Sutton & Barto’s free book on reinforcement learning. Grad ading: 10% in-class exercises 45% homeworks and projects 30% midterm and final 15% final project

In Instruction vs Assessment

Ins Instruct uction

  • In-class exercises
  • Homework
  • Projects

As Assessme ment

  • Midterm Exam
  • Final Exam
  • Final Project
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SLIDE 15

Ho Homewo eworks rks an and Projects

Ho Homew eworks

  • Due every week (approximately)
  • Electronic via grad

adescope

(we may also have a written components)

Pr Projects

  • Due every two weeks (approximately)
  • Based on Berkeley CS 188 materials
  • Submission in Python via grad

adescope

  • We will provide an autograder

(but will perform additional tests when grading)

  • Do not copy online solutions

Ques Questions ns fo for me? e? To ToDo’s (by Monday ay)

  • Check out the website: https://course.ccs.neu.edu/cs4100f19/
  • Make sure you are registered on piazza and gradescope.
  • Complete HW0 (math diagnostic) and P0 (python tutorial)
  • Does not count towards final grade

(but will help you make sure you are prepared)

  • Do the readings for the next lecture