Transcript Artificial Intelligence

Artificial Intelligence
What is Artificial Intelligence?
• Artificial intelligence (AI) is a branch of
computer science, which is to make
computer intelligent (or as intelligent as
human beings).
• Is the above a good definition of AI?
Discussion
What Does ‘Intelligent’ Mean?
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Calculation?
Thinking? Logical deduction? Under uncertainty?
Memorizing? Integrated memory?
Using language?
Visual capability? – Pattern recognition and
understanding a picture?
• Aesthetical sense?
• Sentimental subtleties? – love, sympathy, passion,
romance, joy, anger, envy, hatred, curiosity,
craziness ... ?
AI Is Yet Defined Strictly
• Strict definition of AI relies on strict
definition of ‘intelligence’.
• AI can be defined by the issues it is
concerned with.
• AI is a study whose major goals include its
own definition.
Discussion
A Unique Gift to Human
• Intelligence, which allows acquiring
knowledge, is a unique gift to human.
• Knowledge has enlightened humanity.
• Does intellectual ambition lead to disaster,
as told in the legends of Prometheus and
Eva?
Mental vs. Physical
• We know less about a mental process than a
physical process.
• No one has affirmed a fundamental
difference between mental states and
physical actions.
• More evidences show that mind and body
are not fundamentally different.
• Mental process can be achieved by a
physical system as brain or computer.
Logic
• Logic is the basis of intellectual thinking.
• Logic is used to infer something to be ‘true’
when given other things that are known
‘true’.
• Syllogism (Modus Ponens):
– Given a truth:
If A then B
– Given a fact:
A
– Inferring the result: B.
Formal Logic
• Conjunction: AND, .
– (P Q) is true only if both P and Q are true
• Disjunction: OR, .
– (P Q) is true if at least one of P and Q is true.
• Negation: NOT, .
– ( P) is true if P is false.
• Implication: If...then..., .
– (A B) is true if A and B are true, or A is false.
A Knowledge Base
• Knowledge can be represented by
‘if...then...’ rules.
• A collection of knowledge rules forms a
knowledge base.
• Computer’s thinking is applying Modus
Ponens on a knowledge base.
A Knowledge Base
• Rule 1: If it is a mammal and it is a carnivore
and it has black stripes, then it is a tiger.
• Rule 2: If it is a mammal and it has black stripes
and it is an ungulate, then it is a zebra.
• Rule 3: If it has hair then it is a mammal.
• Rule 4: If it gives milk then it is a mammal.
• Rule 5: If it is a mammal and it has hooves then
it is an ungulate.
• Rule 6: If it eats meat then it is a carnivore.
Turing Test
• It gives us an objective notion of
intelligence, with reference to a human.
• It evaluates intelligence by ‘result’ rather
than by ‘process’.
• It focuses on intelligence on thinking and
written communication.
Criticisms of Turing Test
• It does not test perceptual skill and manual
dexterity that are important components of
human intelligence.
• It uses human as a standard for intelligence,
which is full of flaws per se. To past the
Turing test, ‘artificial stupidity’ is required.
• It does not test the mechanism of a
intelligence process.
Lady Lovelace’s Objection
• By Ada Lovelacs: “Computers can only do
as they are told and consequently cannot
perform original (hence, intelligent)
actions.”
• Achievements of computers have disproved
it:
– Expert systems can do something unanticipated
by their designers;
– Deep Blue beat International Chess Master.
Informality of Behavior
• “It is impossible to create a set of rules that
will tell an individual exactly what to do
under every possible circumstance.”
• It reflects the flexibility existing in human
intelligence.
Neural Network Model
• It is a model to achieve machine
intelligence, which is alternative to the logic
based approach.
• It mimics the structure of biological brain
and emphasizes the ability to adapt to the
world by modifying the relationships
between individual neurons.
Intelligence in Global
Behaviors
• Intelligence is reflected by the collective
behaviors of large numbers of very simple
interacting, semi-autonomous individuals,
or agents.
• Intelligence emerges when simple
individual persons (or neural cells, or
computer ‘agents’) interact.
Our Humble Successes
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Machine learning – just started;
Natural language – modest;
Representing knowledge,
Commonsense knowledge – just started;
Reasoning or thinking – limited quality and
flexibility.
AI Areas
• Although no one has given an exact
definition of AI, people have
consensus on areas of AI.
• The problems that can be solved by
pure calculation or algorithmic process
are not AI problems.
Game Playing
• Intelligence in dealing with ambiguities and
complexities in game/chess playing is
relatively easy to be represented on a
computer.
• Heuristic
– A heuristic is a practical problem-solving
method / algorithm, which works effectively
and efficiently on some instances, but may fail
or be slow on some other instances.
Automated Reasoning
• It enables a computer to think logically by
applying formal logic.
• Propositional logic and predicate logic are
basis of formal logic.
• Inference is a process of searching in the
knowledge base.
• Inference and Inference-guiding.
Expert System
• An intelligent computer system that
possesses deep but narrow knowledge and
acts as a human expert in a domain.
Natural Language
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Translation from one language to another.
Recording oral speech into paper document.
Reading a paper document.
Understanding the meaning of a speech.
Face-to-face conversation.
Modeling Human Performance
• To simulate the process of human internal
mental process.
• Purposes:
– To reach the level of human intelligence;
– To formulating and testing theories of human
cognition for psychology, psychoanalysis, and
philosophy.
Autonomous Planning and
Robotics
• A robot (not the ‘robot’ on an assembly
line) is an autonomous computer agent that
is able to accomplish some missions
independently.
• Necessary capabilities of a robot:
– Planning based on incomplete information;
– Implementing the plan;
– Correcting the plan in execution.
AI Languages
• AI need its own computer languages to
express the knowledge and thinking
process.
• LISP and Prolog are examples.
Machine Learning
• Machine learning refers to the ability of
computer to accumulate knowledge by
itself.
• From where to learn:
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Experience
Analogy
Examples
Being taught or told to do.
Neural Network and Emergent
Computation
• To realize intelligence by mimicking the
structure of neurons in human brain.
• Each computational unit computes some
function of its inputs and passes the result
along to the connected units in network.
Instead of using explicit symbols and
operations, the knowledge emerges out of
the entire network of neural connections.
Is Human Mind a ‘Machine’
• Machine is something that is (1) composed
of parts that follows the known physical
laws, and (2) able to accomplish certain
tasks/assignments.
• A human brain gives rise to thoughts,
feelings, and consciousness, and is
composed of cells that follow the physical
laws.
• So, a brain is a machine. Mind is machine
either?
Implications of
“Human Mind Is a Machine”
• If human mind is a machine, then:
– Human mind can be decomposed,
analyzed, and eventually understood.
– Human mind can be duplicated or
‘manufactured’.
Comments
AI vs. AS
• Artificial intelligence (AI) is to make a
machine do things at which people are
better.
• Artificial stupidity (AS) is to make a
machine do things at which people are
worse.
• AI + AS is to make a machine more
like a person.
Intelligence = On/Off Switches?
• Most feel that thinking or true
intelligence / consciousness is not an
automatic technique based on on/off
switches.
• Recent AI achievements showed at
least ‘some’ intelligence can be
realized through on/off switches.
Comments
A Psychological Myth
• A magic is not magical once we know
its trick.
• When we figure out how an expert
thinks and operates, what once seemed
very intelligent somehow seems less
so.
• The level of intelligence is inversely
related to the extent you understand.
Can We Understand Ourselves?
• It could be a fate that our brains are too
weak to understand themselves, which may
be completely comprehensible to more
intelligent beings.
• The Gödel’s incompleteness theorem:
– The system of formal arithmetic is not
complete. That is, using the theorems of this
system only cannot prove or generate all the
theorems of this system.
Kursweil
Three Processes of Intelligence
• Learning
– Self-accumulating knowledge.
• Reasoning
– Inferring logical implications from
known facts.
• Symbolic reasoning
– Abstract reasoning, and applying abstract
symbols to individual cases.
Evolution vs. Entropy
• Entropy is a theorem in physics that
the elements in a closed system will
eventually go complete random
(disordered).
• Evolution has created very ordered
intelligence.
• How has evolution beat entropy so far?
Class End Comments
• We have covered computers and AI, and their impact on us at present
and in the future.
• We may have posed more questions than we have solved. - That is a
difference between this course and other courses.
• The world is complex, marvelous, and mystical. We, as highly
educated humans, ought to be sophisticated.
• Sophistication is not chaos or confusion. We have our basis values
and logic, as the progress I have seen in the recent assignments.
• What you have learned from this course:
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Knowledge about computers and AI;
Knowledge about impacts of AI on society and us;
Realized that the complication of this world;
Thinking complex problem in a sophisticated way;
Thinking logically and in depth.
Organize your thoughts into writing;
Answer the questions to the point.