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Inventors have long dreamed of creating machines that think. This desire dates
back to at least the time of ancient Greece. The mythical figures Pygmalion,
Daedalus, and Hephaestus may all be interpreted as legendary inventors, and
Galatea, Talos, and Pandora may all be regarded as artificial life ( , Ovid and Martin
2004 Sparkes 1996 Tandy 1997 ; , ; , ).
When programmable computers were first conceived, people wondered whether
they might become intelligent, over a hundred years before one was built (Lovelace,
1842). Today, artificial intelligence (AI) is a thriving field with many practical
applications and active research topics. We look to intelligent software to automate
routine labor, understand speech or images, make diagnoses in medicine and
support basic scientific research.
In the early days of artificial intelligence, the field rapidly tackled and solved
problems that are intellectually difficult for human beings but relatively straight-
forward for computers—problems that can be described by a list of formal, math-
ematical rules. The true challenge to artificial intelligence proved to be solving
the tasks that are easy for people to perform but hard for people to describe
formally—problems that we solve intuitively, that feel automatic, like recognizing
spoken words or faces in images.
This book is about a solution to these more intuitive problems. This solution is
to allow computers to learn from experience and understand the world in terms of a
hierarchy of concepts, with each concept defined in terms of its relation to simpler
concepts. By gathering knowledge from experience, this approach avoids the need
for human operators to formally specify all of the knowledge that the computer
needs. The hierarchy of concepts allows the computer to learn complicated concepts
by building them out of simpler ones. If we draw a graph showing how these
1
CHAPTER 1. INTRODUCTION
concepts are built on top of each other, the graph is deep, with many layers. For
this reason, we call this approach to AI deep learning.
Many of the early successes of AI took place in relatively sterile and formal
environments and did not require computers to have much knowledge about
the world. For example, IBM’s Deep Blue chess-playing system defeated world
champion Garry Kasparov in 1997 ( , ). Chess is of course a very simple Hsu 2002
world, containing only sixty-four locations and thirty-two pieces that can move
in only rigidly circumscribed ways. Devising a successful chess strategy is a
tremendous accomplishment, but the challenge is not due to the difficulty of
describing the set of chess pieces and allowable moves to the computer. Chess
can be completely described by a very brief list of completely formal rules, easily
provided ahead of time by the programmer.
Ironically, abstract and formal tasks that are among the most difficult mental
undertakings for a human being are among the easiest for a computer. Computers
have long been able to defeat even the best human chess player, but are only
recently matching some of the abilities of average human beings to recognize objects
or speech. A person’s everyday life requires an immense amount of knowledge
about the world. Much of this knowledge is subjective and intuitive, and therefore
difficult to articulate in a formal way. Computers need to capture this same
knowledge in order to behave in an intelligent way. One of the key challenges in
artificial intelligence is how to get this informal knowledge into a computer.
Several artificial intelligence projects have sought to hard-code knowledge about
the world in formal languages. A computer can reason about statements in these
formal languages automatically using logical inference rules. This is known as the
knowledge base approach to artificial intelligence. None of these projects has led to
a major success. One of the most famous such projects is Cyc ( , Lenat and Guha
1989). Cyc is an inference engine and a database of statements in a language
called CycL. These statements are entered by a staff of human supervisors. It is an
unwieldy process. People struggle to devise formal rules with enough complexity
to accurately describe the world. For example, Cyc failed to understand a story
about a person named Fred shaving in the morning ( , ). Its inference Linde 1992
engine detected an inconsistency in the story: it knew that people do not have
electrical parts, but because Fred was holding an electric razor, it believed the
entity “FredWhileShaving” contained electrical parts. It therefore asked whether
Fred was still a person while he was shaving.
The difficulties faced by systems relying on hard-coded knowledge suggest that
AI systems need the ability to acquire their own knowledge, by extracting patterns
from raw data. This capability is known as machine learning. The introduction
2
CHAPTER 1. INTRODUCTION
of machine learning allowed computers to tackle problems involving knowledge
of the real world and make decisions that appear subjective. A simple machine
learning algorithm called logistic regression can determine whether to recommend
cesarean delivery (Mor-Yosef 1990 et al., ). A simple machine learning algorithm
called can separate legitimate e-mail from spam e-mail. naive Bayes
The performance of these simple machine learning algorithms depends heavily
on the representation of the data they are given. For example, when logistic
regression is used to recommend cesarean delivery, the AI system does not examine
the patient directly. Instead, the doctor tells the system several pieces of relevant
information, such as the presence or absence of a uterine scar. Each piece of
information included in the representation of the patient is known as a feature.
Logistic regression learns how each of these features of the patient correlates with
various outcomes. However, it cannot influence the way that the features are
defined in any way. If logistic regression was given an MRI scan of the patient,
rather than the doctor’s formalized report, it would not be able to make useful
predictions. Individual pixels in an MRI scan have negligible correlation with any
complications that might occur during delivery.
This dependence on representations is a general phenomenon that appears
throughout computer science and even daily life. In computer science, opera-
tions such as searching a collection of data can proceed exponentially faster if
the collection is structured and indexed intelligently. People can easily perform
arithmetic on Arabic numerals, but find arithmetic on Roman numerals much
more time-consuming. It is not surprising that the choice of representation has an
enormous effect on the performance of machine learning algorithms. For a simple
visual example, see Fig. . 1.1
Many artificial intelligence tasks can be solved by designing the right set of
features to extract for that task, then providing these features to a simple machine
learning algorithm. For example, a useful feature for speaker identification from
sound is an estimate of the size of speaker’s vocal tract. It therefore gives a strong
clue as to whether the speaker is a man, woman, or child.
However, for many tasks, it is difficult to know what features should be extracted.
For example, suppose that we would like to write a program to detect cars in
photographs. We know that cars have wheels, so we might like to use the presence
of a wheel as a feature. Unfortunately, it is difficult to describe exactly what a
wheel looks like in terms of pixel values. A wheel has a simple geometric shape but
its image may be complicated by shadows falling on the wheel, the sun glaring off
the metal parts of the wheel, the fender of the car or an object in the foreground
obscuring part of the wheel, and so on.
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