David S. Read

For background on this post, please see my last entry, Part 1: Questions and Baselining.

What separates today’s machine learning from human learning? One word: concepts.

“How so?” you might ask. To see what I mean, let’s start by looking at standard machine learning inputs and outputs. I’ll focus on supervised learning.

Supervised Machine Learning

Supervised machine learning is an approach where we start with a set of records. In each record, one field contains the correct answer, known as the target attribute. The other fields in the record contain related information, formally known as descriptive attributes. For example, we might have a set of measurements for flower petals and the flower’s name for each set of measures. We want the computer to learn how to identify different types of flowers. For those of you with machine learning experience, you’ll recognize the Iris data set as the inspiration for my example.

Supervised machine learning is similar to how we might teach children some set of math facts. We give them many examples of addition problems and answers. Over time we would like them to understand the mechanics of addition and solve novel problems. We have a similar goal with supervised learning. We want to give the computer lots of examples with the correct answers and have it figure out how to answer new problems.

Decision Trees

Table 1: Flower Data Petal Length Petal Width Flower (Answer) 2 1.7 Rose 2.5 2.1 Rose 3.2 0.5 Daisy 3.6 0.6 Daisy

Figure 1: Example Decision Tree

We’ll begin looking at what the machine is learning using a basic supervised approach, decision trees. In this case, the computer looks at the correct answer, the target attribute. It uses the descriptive attributes in the record to create a decision that would use that record’s data to arrive at the correct result. In Table 1, there are four records. For each, there are two measurements for rose petals and daisy petals. The resulting decision tree might look like Figure 1.

This is a simple example, but the interesting point is that the computer is limited to making a decision using the data in the record. As discussed in my previous post, the text “Rose” doesn’t mean anything to the system. We could add additional data to the record, such as details about petal color and whether the stem has thorns. But the machine learning process won’t have that information available without explicitly adding it to the data. Since the computer doesn’t know what the text “Rose” means, it can’t incorporate other knowledge about roses into its decision tree.

This is a considerable hurdle in machine learning. As people learn new information, they build a knowledge base and apply it to new learning. That isn’t how these discreet learning processes work. And that limitation is imposed chiefly because the computer isn’t using concepts.

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