Why is machine learning equivalent to AI¡¯s brain? Let¡¯s again take a look at the term. Firstly, ¡®machine¡¯ means unmistakably computer. And ¡®learning¡¯ is the difficult part. It is actually an abbreviation of
¡®statistical learning¡¯ or
¡®inductive learning¡¯, which mean
learning something new out of patterns within data.
Let¡¯s say, we want to learn the total sum of angles in a triangle. The first approach is to use basic geometry for deductive reasoning (refer to the below for detail). Without observing evidence solely dependent on logical reasoning, we learned that the answer is 180 degrees and it is applicable to any triangle. This approach of learning is known as deductive learning.

The second way is to actually measure and sum all three angles of a large number of triangles. Out of theoretically infinite number of triangles, you pick 100 of them randomly and do the measuring and summing. The result will be consistently same, 180 degrees. But the sample size of 100 is too few to decisively say that this is a general rule. If you increase the sample size up to 1 million, perform the same process, and get the same outcome, now your finding gains far greater credibility due to far more evidence enough to conclude that the same can be applied to the entire population of triangles. Learning generally applicable rules from the patterns hiding in sample data is known as
inductive learning. Since statistics is to extract sample, find patterns, and generalize patterns, it is also called
statistical learning.

So in one sentence, machine learning is a computer-run statistical learning, which means
a learning process for a computer to find generalizable patterns out of given sample data. People learn both deductively and inductively. Computers can only learn inductively, but they excel in it so overwhelmingly enough to show far superior learning ability and intelligence to humans in certain tasks such as chess and go. Measuring angles of 1 million triangles is a daunting task for human requiring months of time, but for computers it is an easy job worth of less than an hour. Why do computers excel in statistical learning? Because it is by nature math and statistics, and they are in the end computation. Machine learning requires iterative computation on massive data, which computers are designed to do. After all, computer is ¡®compute¡¯ + ¡®er¡¯.
The history of statistical learning goes much farther back than the history of computers. Researchers of statistical learning realized computer¡¯s potential, and believed that it would be possible to build an artificial intelligence smarter than human intelligence if computers perform statistical learning. That idea coined the term of
machine learning in 1950s, a combination of ¡®machine¡¯ and ¡®statistical learning¡¯.
Methods of statistical learning to find patterns out of data can be transformed into computer-executable methods, namely algorithms. We call these
machine learning algorithms. As there can be many approaches to tackle any given issue depending on the situation, there are a number of machine learning algorithms. And one of them is artificial neural network (ANN) that mimics human neural network. In many cases, ANN is consisted of multiple layers forming a complex network of nodes. This complicated multi-layered ANN has a well-known alias,
deep learning indicating a long and deep course from input to output.
