AI: Goals

Ambitious goals:
– understand “intelligent” behavior
– build “intelligent” agents
 What is Intelligence?

Intelligence:
– capacity to learn and solve problems”
(Webster dictionary)
– the ability to act rationally

Hmm… Not so easy to define.

 What is AI?

Views of AI fall into four different perspectives

--- two dimensions:

1) Thinking versus Acting

2) Human versus Rational (which is “easier”?)

Human-like “Ideal” Intelligent/

Intelligence Pure Rationality
Which is
2. Thinking 3. Thinking
closest to
Thought/ humanly Rationally a ‘real’ human?
Reasoning
1. Acting 4. Acting
Behavior/ Humanly Rationally Furthest?
Actions
“behaviorism”
[Here]
Different AI Perspectives
2. Systems that think like humans 3. Systems that think rationally

Rational Thinking
Human Thinking
Human Acting Rational Acting

1. Systems that act like humans 4. Systems that act rationally

Note: A system may be able to act like a human without thinking

like a human! Could easily “fool” us into thinking it was human!
 1. Acting Humanly

Human-like “Ideal” Intelligent/

Intelligence Rationally
2. Thinking 3. Thinking
Thought/ humanly Rationally
Reasoning

1. Acting 4. Acting
Humanly Rationally
Behavior/
Actions
Turing Test
 Universality of Computation

Mathematical Formulation of
notion of Computation and Computability
(1936)
23 June 2012
Turing Centenary
Abstract model of a
digital Computer:
rich enough to capture
any computational process.
 Universal Computer

Turing Machine
Description
+ input
Turing Centennial

Universal Information
Processing Model
Turing
Machine
 of a Universal
Computer
Vending Machine

von Neumann architecture (1947)

Architecture of modern computers.
Data and program are stored in the computer's memory.
(inspired by Turing’s model)
 Acting humanly: Turing Test
Turing (1950) "Computing machinery and intelligence”
Alan Turing
"Can machines think?“ "Can machines behave intelligently?"
– Operational test for intelligent behavior: the Imitation Game

AI system passes
if interrogator
cannot tell which one
(interaction via written questions) is the machine.
No computer vision or robotics or physical presence required!
Predicted that by 2000, a machine might have a 30% chance of
fooling a lay person for 5 minutes.
Achieved. (Siri!  )
Anticipated major arguments against AI in following 50 years.
Suggested major components of AI.
 Trying to pass the Turing test:
Some Famous Human Imitation “Games”
1960s ELIZA
– Joseph Weizenbaum
– Rogerian psychotherapist

1990s ALICE

Loebner prize
– win $100,000 if you pass the test

Still, passing Turing test is somewhat of questionable value.

Because, deception appears required and allowed!

Consider questions: Where were you born? How tall are you?
 ELIZA:
impersonating a Rogerian psychotherapist

1960s ELIZA Joseph Weizenbaum

http://www-ai.ijs.si/eliza/eliza.html

You: Well, I feel sad

Eliza: Do you often feel sad?
You: not very often.
Eliza: Please go on.

https://psych.fullerton.edu/mbirnbaum/psych101/eliza.htm
 Recent alternative

See: The New Yorker, August 16, 2013

Why Can’t My Computer Understand Me?
Posted by Gary Marcus

http://www.newyorker.com/online/blogs/
elements/2013/08/why-cant-my-computer-
understand-me.html

Discusses alternative test by Hector Levesque:

http://www.cs.toronto.edu/~hector/Papers/ijcai-13-paper.pdf
 2. Thinking Humanly

Human-like “Ideal” Intelligent/

Intelligence Rationally
2. Thinking Thinking
Thought/ humanly Rationally
Reasoning  Cognitive
Modeling
Acting Acting
Behavior/
Humanly Rationally
Actions
Turing Test
 Thinking humanly:
modeling cognitive processes
Requires scientific theories of internal activities of the brain.

1) Cognitive Science (top-down) computer models +

experimental techniques from psychology
 Predicting and testing behavior of human subjects

2) Cognitive Neuroscience (bottom-up)

 Direct identification from neurological data

Distinct disciplines but especially 2) has become

very active. Connection to AI: Neural Nets. (Large
Google effort.)
 Neuroscience: The Hardware

The brain
• a neuron, or nerve cell, is the basic information
• processing unit (10^11 )
• many more synapses (10^14) connect the neurons
• cycle time: 10^(-3) seconds (1 millisecond)

How complex can we make computers?

• 10^9 or more transistors per CPU
• Ten of thousands of cores, 10^10 bits of RAM
• cycle times: order of 10^(-9) seconds

Numbers are getting close! Hardware will surpass human

brain within next 20 yrs.
 Computer vs. Brain

approx. 2025
Current:
Nvidia: tesla
personal super-
computer
1000 cores
4 teraflop

Aside: Whale vs. human brain

So,
• In near future, we can have computers with as many
processing elements as our brain, but:
far fewer interconnections (wires or synapses)
then again, much faster updates.

Fundamentally different hardware may

require fundamentally different algorithms!
• Still an open question.
• Neural net research.
• Can a digital computer simulate our brain?

Likely: Church-Turing Thesis

(But, might we need quantum computing?)
(Penrose; consciousness; free will)
A Neuron
An Artificial Neural Network
(Perceptrons)
Output Unit

Input Units
An artificial neural network is an abstraction
(well, really, a “drastic simplification”) of a real
neural network.

Start out with random connection weights on

the links between units. Then train from input
examples and environment, by changing
network weights.

Recent breakthrough: Deep Learning

(automatic discovery of “deep” features
by a large neural network. Google/Stanford
project.)
 Neurons in
the News

The Human Brain Project

European investment: 1B Euro (yeap, with a “b”  )
http://www.humanbrainproject.eu/introduction.html
“… to simulate the actual working of the brain. Ultimately, it
will attempt to simulate the complete human brain.”
http://www.newscientist.com/article/dn23111-human-brain-
model-and-graphene-win-sciences-x-factor.html
 Bottom-line: Neural networks with machine learning
techniques are providing new insights in to how to achieve AI.
So, studying the brain seems to helps AI research.
Obviously?
Consider the following gedankenexperiment.
1) Consider a laptop running “something.” You have no idea
what the laptop is doing, although it is getting pretty warm… 

2) I give you voltage and current meter and microscope

to study the chips and the wiring inside the laptop.
Could you figure out what the laptop was doing?
3) E.g. Consider it’s running a quicksort on a large list of integers.
Could studying the running hardware ever reveal that?

Seems unlikely… Alternatively, from I/O

behavior, you might stumble on a sorting algorithm,
possibly quicksort!
So, consider I/O behavior as an information processing task.

This is a general strategy driving much of current AI:

Discover underlying computational process that mimics desired
I/O behavior.
E.g.
In: 3, -4, 5 , 9 , 6, 20 Out: -4, 3, 5, 6, 9, 20
In: 8, 5, -9, 7, 1, 4, 3 Out: -9, 1, 3, 4, 5, 7, 8

Now, consider hundreds of such examples.

A machine learning technique, called Inductive Logic

Programming, can uncover a sorting algorithm that
provides this kind of I/O behavior. So, it learns the
underlying information processing task. Also, Genetic
Genetic programming.
But, sorting numbers doesn’t have much to do with general
intelligence… However many related scenarios.
E.g., consider the area of activity recognition and planning.

Setting: A robot observes a human performing a series of actions.

Goal: Build a computational model of how to generate such
action sequences for related tasks.

Concrete example domain: Cooking. Goal: Build household robot.

Robot observe a set of actions (e.g., boiling water, rinsing,
chopping, etc.). Robot can learn which actions are required
for what type of meal.

But, how do we get the right sequence of actions?

Certain orderings are dictated by domain, e.g. “fill pot with
water, before boiling.” Knowledge-based component (e.g. learn).
But how should robot decide on actions that can be ordered
in different ways? Is there a general principle to do so?

Answer: Yes, minimize time for meal preparation.

Planning and scheduling algorithms will do so. Works quite well

even though but we have no idea of how a human brain actually
creates such sequences. I.e., we viewed the task of generating the
sequence of actions as an information processing task optimizing
a certain objective or “utility” function (i.e., the overall
duration).

General area: sequential decision making in uncertain

environments. (Markov Decision Processes.)

Analogously: Game theory tells us how to make good decision in

multi-agent settings. Gives powerful game playing agents (for
chess, poker, video games, etc.).
Wonderful (little) book:
The Sciences of the Artificial
by Herb Simon

One of the founders of AI. Nobel Prize in

economics. How to build decision making
machines operating in complex
environments. Theory of Information
Processing Systems. First to move
computers from “number crunchers”
(fancy calculators) to “symbolic
processing.”
Another absolute classic:
The Computer and the Brain
by John von Neumann.

Renowned mathematician and the

father of modern computing.
 3. Thinking Rationally

Human-like “Ideal” Intelligent/

Intelligence Rationally
Thinking humanly 3. Thinking
Thought/  Cognitive Rationally
Reasoning Modeling formalizing
”Laws of
Thought”
Acting Acting
Behavior/
Humanly Rationally
Actions
Turing Test
 Thinking rationally:
formalizing the "laws of thought”
Long and rich history!
Logic: Making the right inferences!
Remarkably effective in science, math, and engineering.

Several Greek schools developed various forms of logic:

notation and rules of derivation for thoughts.
Aristotle: what are correct arguments/thought processes?
(characterization of “right thinking”).
Socrates is a man
Syllogisms All men are mortal
Aristotle --------------------------
Therefore, Socrates is mortal
Can we mechanize it? (strip interpretation)
Use: legal cases, diplomacy, ethics etc. (?)
More contemporary logicians (e.g. Boole, Frege, and Tarski).
Ambition: Developing the “language of thought.”
Direct line through mathematics and philosophy to modern AI.
Key notion:
Inference derives new information from stored facts.
Axioms can be very compact. E.g. most of mathematics
can be derived from the logical axioms of Set Theory.
Zermelo-Fraenkel with axiom of choice.
Limitations:

• Not all intelligent behavior is mediated by logical

deliberation (much appears not…)

• (Logical) representation of knowledge underlying

intelligence is quite non-trivial. Studied in the area of
“knowledge representation.” Also brings in probabilistic
representations. E.g. Bayesian networks.

• What is the purpose of thinking?

• What thoughts should I have?
•
 4. Acting Rationally

Human-like “Ideal” Intelligent/

Intelligence Rationally
Thinking humanly 3. Thinking
Thought/  Cognitive Rationally
Reasoning Modeling / ”Laws of
 Neural nets Thought“
Acting
Many current
4. Acting
Behavior/ AI advances
Humanly Rationally
Actions
Turing Test
 Rational agents
• An agent is an entity that perceives and acts in
the world (i.e. an “autonomous system” (e.g.
self-driving cars) / physical robot or software robot
(e.g. an electronic trading system))

This course is about designing rational agents

For any given class of environments and tasks, we

seek the agent (or class of agents) with the best
performance
•
• Caveat: computational limitations may make perfect
rationality unachievable
•  design best program for given machine resources
 Building Intelligent Machines

I Building exact models of human cognition

view from psychology, cognitive science, and neuroscience

II Developing methods to match or exceed human

performance in certain domains, possibly by
very different means

Main focus of current AI.

But, I) often provides inspiration for II). Also, Neural Nets

blur the separation.
 Key research areas in AI
Problem solving, planning, and search --- generic problem
solving architecture based on ideas from cognitive science
(game playing, robotics).
Knowledge Representation – to store and manipulate
information (logical and probabilistic representations)
Automated reasoning / Inference – to use the stored
information to answer questions and draw new conclusions
Machine Learning – intelligence from data; to adapt to new
circumstances and to detect and extrapolate patterns
Natural Language Processing – to communicate with the
machine
Computer Vision --- processing visual information
Robotics --- Autonomy, manipulation, full integration of AI
capabilities