CS代写 THE HISTORY OF ARTIFICIAL INTELLIGENCE

THE HISTORY OF ARTIFICIAL INTELLIGENCE
School of Computing and Information Systems
Co-Director, Centre for AI & Digital Ethics The University of Melbourne @tmiller_unimelb

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LEARNING OUTCOMES
Discuss key eras of the development of artificial intelligence
Apply lessons from the past to discussions in the present.
Make new links between the way we currently build AI, and the effects it has on the people that use it

A BRIEF HISTORY OF

IN WHAT YEAR WAS THE FIRST PROGRAMMABLE COMPUTER BUILT?

British mathematicians & engineers build the Bombe and Colossus for cracking codes during World World II
The first AI winter. The hype of the golden era did not eventuate. Funding dried up and excitement dwindled.
The second AI winter. The hype of the knowledge era did not eventuate. Funding dried up and excitement dwindled.
1956-1974 1980-1987 1974-1980
1994-now 1987-1993
The Dartmouth Conference is held. The proposal coins the term ‘artificial intelligence’
invents the Analytical Engine. recognises it is a general purpose calculating machine
proposes the Turing Test as a goal for artificial intelligence, rather than defining ‘intelligence’
The Golden Era. Foundational search algorithms and artificial neural networks are invented
The Knowledge Era. Knowledge- based systems, such as Prolog and expert systems invented.
The Revival Era. Intelligence agents, computational power, the Internet, and machine learning

THE ERAS OF
ARTIFICIAL INTELLIGENCE

THE TURING TEST (1950)
“The new form of the problem can be described in terms of a game which we call the ‘imitation game’”
. Computing Machinery and Intelligence, Mind,
LIX(236):433–460, 1950. https://doi.org/10.1093/mind/LIX.236.433

DARTMOUTH WORKSHOP (1956)
THE FOUNDERS OF AI
“We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, . The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. 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 in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” – Dartmouth Summer School on AI Proposal
JOHN McCARTHY
MARVIN MINSKY
CLAUDE SHANNON
RAY SOLOMONOFF
NATHANIEL ROTCHESTER
ALAN NEWELL
TRENCHARD MOORE
HERBERT SIMON
ARTHUR SAMUEL
OLIVER SELFRIDGE

DARTMOUTH OUTCOMES
Perception
Knowledge Representation
The divide and conquer model of artificial intelligence

THE GOLDEN AGE OF AI
(1956-1974)

GOLDEN AGE: REASONING AS SEARCH Start
2 4 9 10 11
3 6 7 14 15

GOLDEN AGE: REASONING AS SEARCH Initial state: At(A), Level(low), BoxAt(C), BananasAt(B)
Goal state: Have(bananas)
// move from X to Y
_Move(X, Y)_
Preconditions: At(X), Level(low) Postconditions: not At(X), At(Y)
// climb up on the box
_ClimbUp(Location)_
Preconditions: At(Location), BoxAt(Location), Level(low) Postconditions: Level(high), not Level(low)
// climb down from the box
_ClimbDown(Location)_
Preconditions: At(Location), BoxAt(Location), Level(high) Postconditions: Level(low), not Level(high)
// move monkey and box from X to Y
_MoveBox(X, Y)_
Preconditions: At(X), BoxAt(X), Level(low) Postconditions: BoxAt(Y), not BoxAt(X), At(Y), not At(X)
// take the bananas
_TakeBananas(Location)_
Preconditions: At(Location), BananasAt(Location), Level(high)
Postconditions: Have(bananas)
Stanford Research Institute Problem Solver (STRIPS) — Fikes and Nilsson (1971)
Shakey the robot and A* — Hart, Nillson, and Raphael (1968)

GOLDEN AGE: PERCEPTRONS AND NEURAL NETWORKS
Single-layer perceptron – Rosenblatt (1958)

THE FIRST AI WINTER
(1974-1980)

WHAT WENT WRONG?
Outcomes failed to live up to the hype
SCALABILITY
COMMONSENSE KNOWLEDGE PERCEPTRON LIMITATIONS MORAVEC’S PARADOX

WHAT WAS THE RESULT? Lack of progress meant:
FUNDING DRIED UP
GLOBAL INTEREST IN AI DIED DOWN
CRITICISM FROM PHILOSOPHERS AND COGNITIVE SCIENTISTS

THE KNOWLEDGE ERA
(1980-1987)

KNOWLEDGE ERA: KNOWLEDGE-BASED SYSTEMS
Employee Customer
Casual Fixed- Permanent term
Prolog – Colmerauer and Roussel (1972) Formal onologies
mother_child(trude, sally). father_child(tom, sally). father_child(tom, erica). father_child(mike, tom).
sibling(X, Y) :- parent_child(Z, X),
parent_child(Z, Y).
parent_child(X, Y) :- father_child(X, Y). parent_child(X, Y) :- mother_child(X, Y).
?- sibling(sally, erica).

KNOWLEDGE ERA:
EXPERT SYSTEMS
MYCIN expert system for diagnosis of blood diseases – Shortcliffe, Buchanan, and Cohen (1970s)

THE SECOND AI WINTER
(1987-1993)

WHAT WENT WRONG?
Outcomes failed to live up to the hype
SCALABILITY
MAINTENANCE
THE QUALIFICATION PROBLEM MORAVEC’S PARADOX

WHAT WAS THE RESULT? Lack of progress meant:
FUNDING DRIED UP (DARPA DECLARED AI WAS `NOT THE NEXT WAVE’)
GLOBAL INTEREST IN AI DIED DOWN AI COMPANIES WENT BANKRUPT
THIS SLIDE IS NOT A COPY FROM THE FIRST AI WINTER

THE AI REVIVAL
(1994-present)

CURRENT ERA: INTELLIGENT AGENTS AND DECISION THEORY
Bayesian Networks – Pearl (1988)

CURRENT ERA: COMPUTATIONAL POWER Moore’s law
1980 1985 1990
NUMBER OF TRANSISTORS (000s)

CURRENT ERA: THE INTERNET AND BIG DATA
Image source: https://www.promptcloud.com/blog/want-to-ensure-business-growth-via-big-data-augment-enterprise-data-with-web-data/

CURRENT ERA: MACHINE LEARNING
Backpropagation in deep neural networks – Rumelhart, Hinton, and Williams (1986)

THE THIRD AI WINTER?

WE’VE BEEN HERE BEFORE
“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.”
— Gray Scott (2017)
“We will have fully self- driving cars on the road by 2017” — Elon Musk (2014)
We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
Amara’s Law
“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, and we will have multiplied the intelligence – the human biological machine intelligence of our civilization – a billion-fold.”
— (1999)
“In from three to eight years we will have a machine with the general intelligence of an average human being.”
— (1970)
“Machines will be capable, within twenty years, of doing any work a man can do”
— (1956)
“… the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”
– Times on the Perceptron (1958)

WHAT ARE SOME OF THE RISKS?
“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”
The beauty of #AI and what we can do with it is currently being overshadowed by reckless #hype, monotheistic techniques, and absurd deification. Yet, it’s not the first time in 65+ years nor it’ll be the last one. We haven’t learnt a thing. ―

HISTORY AND
REPRESENTATION

UNDER-REPRESENTATION IN AI
“Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices”. , , . In ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021

EFFECTS OF UNDER-REPRESENTATION Lack of diversity means lack of:
Design decisions, data, attitudes, etc., are all influenced by who we are as individuals, as teams, and as societies
PRIVACY FAIRNESS
ACCESSIBILITY & INCLUSION
SAFETY TRANSPARENCY FUNCTIONALITY
The History of AI ⊆ The History of Culture and Society

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