CHAPTER 1. INTRODUCTION
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 a 3-D MRI image of the
patient, rather than the doctor’s formalized report, it would not be able to make
useful predictions. Individual voxels
1
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, operations
such as searching a collection of data can proceed exponentially faster if the collec-
tion 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 the pitch. The pitch can be formally specified—it is the lowest frequency
major peak of the spectrogram. It is useful for speaker identification because it
is determined by the size of the vocal tract, and 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 ex-
tracted. 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.
One solution to this problem is to use machine learning to discover not only
the mapping from representation to output but also the representation itself.
This approach is known as representation learning. Learned representations of-
1
A voxel is the value at a single point in a 3-D scan, much as a pixel as the value at a single
point in an image.
3