What is the difference between AI, machine learning and deep learning?
1. Introduction
Recently, I often see and hear the word AI in news and
books. It is treated like a savior with a shortage of manpower, or written like
a devil who robs people of their jobs, but its substance is not easy to
understand. Moreover, I often hear people say that they don't know exactly what
they will do for the work they are involved in.
Another confusing thing is its name. AI, machine
learning, and deep learning are all unclear what they are and what they are
related to, and I can't help but ask people. In this article, I would like to
explain the substance, focusing on machine learning.
2. What is AI?
AI is an abbreviation for Artificial Intelligence,
which is an old and new word for artificial intelligence in Japanese. The dream
of building intelligent intelligence like humans on a computer has disappeared
in the past several times.
In fact, AI itself does not have a formal definition.
AI is indebted when using some nifty applications and services. The Japanese
input that you use to input sentences is also one of the AI applications that
convert characters by considering the surrounding words and frequency of use. A
long time ago, it was sometimes called AI conversion. Another type of AI is an
application that plays an opponent in computer games such as shogi, chess, and
go.
In this way, AI is just the name of an application
that "looks intelligent" like a human being. It doesn't matter what
the logic in it is. Maybe it's just fetching from a huge database, or maybe
it's using machine learning, which I'll explain later.
3. Why AI is booming now
Research to realize AI has been underway for a long
time, but there are four reasons why it has been booming recently.
The first is a dramatic increase in computer
processing power and recording capacity, as well as a dramatic reduction in its
utilization costs. Second, the spread of Web services and smartphones has
dramatically increased the amount of digitized data required to learn and use
AI. Third, it has had a great impact on the business in marketing, advertising
and mail-order sales. AI is now taking on important functions that are
indispensable for recent web services and services targeting smartphones, such
as product recommendations, appropriate ad matching, and categorization of
customer behavior. .. And fourth, with real-world feedback and financial
back-up from the business world, talented researchers and engineers are working
on AI to improve algorithms and develop great software one after another. ..
Due to these virtuous circles, AI has become unprecedentedly successful.
The excitement of AI has spread to the industrial
world. Until now, the main use of AI has been IT services for consumers
centered on smartphones. Services with many users such as Google (Google),
Amazon (Amazon) and Facebook are supported by analyzing digital data such as
personal behavior and information collected in large quantities and utilizing
it for marketing and advertising. ing.
However, the wave of AI is rushing to industries that
have not benefited much from IT, especially manufacturing and service
industries. This is because it has become possible to collect digitized data
through the IoT (Internet of Things). Especially in Japan, due to the
combination of rapid labor shortages and the aging of skilled workers, efforts
to save labor by utilizing data have not been waited for. Tools for general
engineers are appearing at the right time, and the hurdles are gradually
decreasing.
4. What we can understand by machine learning is "correlation", not "causal effect"
Machine learning is a technology that uses large-scale
data for prediction using statistical algorithms. It is the most popular
technology used in recent AI. By combining statistical analysis and simulation
with a large amount of data, the computer can automatically analyze / predict
the target thing.
However, because it is a "statistical
analysis", it does not understand things. To the last, it predicts the
transition of the numerical value expressed in the data and classifies the
character string or the image. There is no formal definition for this either,
but statistical analysis emphasizes understanding of the subject, and machine
learning emphasizes future prediction.
The important point is that machine learning reveals
"correlation", not "causal". Correlation can show that
multiple variables are involved, but it is unclear if they are really related.
It is human work to find meaning and prove "causality". Fortunately,
human work is not lost and becomes more important. Here's an interesting
example, so take a look.
As shown in Figure 1 , there is a correlation between
divorce rates in Maine, USA and per capita margarine consumption. But are there
any consequences to reducing margarine consumption and divorce rates? It's a
joke because it's a story that can be understood by thinking for a moment, but
it may be difficult to make this distinction when considering the main business
of a manufacturing engineer. For the time being, it's best to keep in mind that
machine learning deals with correlations.
It may sound negative to machine learning, but of
course it makes a lot of sense if you only know the correlation. Machine
learning comes into play because there are too many factors involved and the
cause and effect are unknown in the first place. In addition, it is meaningful
to be able to make comparative studies by making numerical values, or to have
an idea of analysis of a huge amount of data. Also, it takes a long time to
understand the cause and effect. The reality is that you can't spend that much
time in business, so even if it's not the correct answer, you can often use it
enough if it's a good solution.
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