These meanings have some shortcomings when it comes to talking about com-
puters. For the first two, it is virtually impossible to test whether learning has
been achieved or not. How do you know whether a machine has got knowledge
of something? You probably can’t just ask it questions; even if you could, you
wouldn’t be testing its ability to learn but would be testing its ability to answer
questions. How do you know whether it has become aware of something? The
whole question of whether computers can be aware, or conscious, is a burning
philosophic issue. As for the last three meanings, although we can see what they
denote in human terms, merely “committing to memory” and “receiving
instruction” seem to fall far short of what we might mean by machine learning.
They are too passive, and we know that computers find these tasks trivial.
Instead, we are interested in improvements in performance, or at least in the
potential for performance, in new situations. You can “commit something to
memory” or “be informed of something” by rote learning without being able to
apply the new knowledge to new situations. You can receive instruction without
benefiting from it at all.
Earlier we defined data mining operationally as the process of discovering
patterns, automatically or semiautomatically, in large quantities of data—and
the patterns must be useful. An operational definition can be formulated in the
same way for learning:Things learn when they change their behavior in a way that makes them
perform better in the future.
This ties learning to performance rather than knowledge. You can test learning
by observing the behavior and comparing it with past behavior. This is a much
more objective kind of definition and appears to be far more satisfactory.
But there’s still a problem. Learning is a rather slippery concept. Lots of things
change their behavior in ways that make them perform better in the future, yet
we wouldn’t want to say that they have actually learned.A good example is a
comfortable slipper. Has it learnedthe shape of your foot? It has certainly
changed its behavior to make it perform better as a slipper! Yet we would hardly
want to call this learning.In everyday language, we often use the word “train-
ing” to denote a mindless kind of learning. We train animals and even plants,
although it would be stretching the word a bit to talk of training objects such
as slippers that are not in any sense alive. But learning is different. Learning
implies thinking. Learning implies purpose. Something that learns has to do so
intentionally. That is why we wouldn’t say that a vine has learned to grow round
a trellis in a vineyard—we’d say it has been trained.Learning without purpose
is merely training. Or, more to the point, in learning the purpose is the learner’s,
whereas in training it is the teacher’s.
Thus on closer examination the second definition of learning, in operational,
performance-oriented terms, has its own problems when it comes to talking about8 CHAPTER 1| WHAT’S IT ALL ABOUT?