An article on Wired.com, by Matt
Kennedy, talks about artificial intelligence. It introduces this concept called
machine learning. Machine learning (ML) is a field that intersects computer
science and mathematics. The goal of ML is designing software systems that can
make use of data without being explicitly programmed with information about the
structure or contents of that data. ML is supposed to be counterintuitive to
the general understanding that we have about how computer programs work.
Computer programs are known by everyone to be a set of instructions and a
computer executes that set of instructions. Even a person who does not know how
to program a computer would know that a computer program is basically that.
However, with ML, a machine learns from the input data through mathematics.
It is interesting what the author
writes. Math does have the tendency to scare people. That could be the reason
why some people would hate for a robot to be built with all this complex math
and being able to understand it. Then, it would seem they know more than most
people do and that could set up some extreme domination of robots over humans.
However, understanding how it works through simple examples is completely
possible. Here is what the article posted as a simple example:
“Imagine that you’re a real estate
agent in a posh neighborhood and you have a new 5000 square foot house to list.
How do you determine the listing price on your house? There have been three
recent sales in the neighborhood: a 4000 sq ft house for $4M, a 4500 sq ft
house for $4.5M and a 6000 sq ft house for $6M. With nice gigantic round
numbers like this, it’s pretty clear that a reasonable price for the 5000 sq ft
house is $5M. If you worked that out in your head, mathematically speaking, you
just performed an algorithm called linear regression, and then performed a
predictive analysis by using the known feature (5000 square feet) to ascertain
the target information ($5M). Aren’t you clever?”
When I read this example, it was
very simple. Anyone could understand this. Linear regression is the algorithm
that allows computers to analyze data in the same way. We make predictions
based on linear regression and that is how a computer does it. However, it is
not that simple. What if the data from the previous example was not aligned so
perfectly like that? He goes further into his example about real estate here.
The amount of square footage is not the only determination of a household. The
quality of it plays a factor as well. There could also be other factors such as
age and location too. That would not allow for such a straight line, but does
that mean that we as humans cannot understand? Certainly not. We are capable of
learning anything a machine can. After all, we create the machines.
What this example explained was
supervised learning. The other class of ML algorithm is unsupervised learning.
Unsupervised learning is when a machine is given data to find patterns on its
own, whereas supervised learning is when the patterns are actually already
known by its programmers. The reason I
bring up this article is because there have been movies about artificially
intelligent machines becoming self-aware and taking over the world. Some people
may actually believe in that stuff. However, it is easy to understand how a
robot is being made to be intelligent. The harder stuff is for those teams of
programmers, mathematicians, domain experts, and data scientists. All the
common person needs to know is that these machines are only being made to think
like they do already. They won’t be smarter than you.
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