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Posts Tagged ‘data mining’

Successful Machine Learning: Part 2 (What is Being Learned?)

Thursday, June 10th, 2021

Machine Learning Chip

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

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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Successful Machine Learning: Part 1 (Questions and Baselining)

Monday, May 17th, 2021

Machine Learning Chip

In this series of posts, I’m delving into the limitations of machine learning and AI, hamstrung by current techniques, while considering technologies and practices to transform business intelligence efforts beyond the status quo.

Question of Intelligence

What is intelligence? What underlies intelligence? What aspects of intelligence do we want machine learning to demonstrate? What is artificial intelligence as opposed to intelligence? What capabilities does a computer need to achieve intelligence? Can programs be written to derive intelligence within a modern computer?

Questions delving into intelligent systems go on and on. I’m going to spend a few blog entries exploring machine learning and our quest to create and benefit from intelligent computer systems. Through this discussion, I’ll explore these questions.

Framing the Discussion

Note that my focus is business automation, what are organizations seeking to gain from machine learning and intelligent systems. I am purposefully avoiding a philosophical discussion of intelligence. To that end, a primary assumption is that we are interested in applying human-style intelligence to advance business or operational success. Put another way, animals and plants demonstrate intelligence of differing types; however, mimicking these is not an organization’s goal when employing machine learning.

Key Terms

To begin, I need working definitions for learning and intelligence. These will serve as touchstones for exploring computer-based learning and intelligence. Merriam-Webster’s dictionary provides helpful initial entries for each. The definition for “learn” is “to gain knowledge or understanding of or skill in by study, instruction, or experience.” While “Intelligence” is defined in two parts, “the ability to learn or understand or to deal with new or trying situations” and “the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests).”

The terms knowledge and understanding appear in both definitions and are vital to successful machine learning applications. Knowledge and understanding are based on information.

 

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