The Difference Between Machine Learning and Deep Learning
Machine Learning and Deep Learning SimplifiedGenerally speaking, Machine Learning and Deep Learning are two different ways to achieve Artificial Intelligence.
Machine Learning relies on the computer being fed information and assimilating it, “learning” in the process, while Deep Learning relies on the computer “simulating a brain” and figuring things out by itself.
Although they’re different, Deep Learning is at its core Machine Learning, only in a more complex way.
But which way is the best? What are the differences between the two and what are the similarities?
First, we need to try and understand what machine learning, deep learning and some other commonly used terms mean.
Then, we can see in what ways they’re similar to each other and how they differentiate themselves.
What is AI?As most people know, AI stands for Artificial Intelligence. At a basic level it’s pretty simple to understand: a mechanical-type of intelligence, opposed to the natural intelligence humans and other animals display.
Although the concept can be traced way back to ancient Greece, to imaginings of non-natural beings with intelligence, the actual term “Artificial Intelligence” was first coined in 1956 by John McCarthy, when AI was founded as an actual academic discipline.
When we talk about AI we’re talking about intelligence applied to technology, or to computers if you will.
The term is usually mentioned whenever a machine performs a cognitive task that normally only a natural intelligence-possessing being would be able to perform, such as problem solving or recognizing patterns.
Several Types of AI
Of course AI comes in several different “flavors”.
AIs are usually optimized to perform a certain task — meaning that they are never as complex as a human mind.
Some AIs are really good at playing chess, but they’re unable to predict the weather.
Others are built to learn repeating patterns in images and replicate them and are unable to do anything else.
We can distinguish AIs in three different types: narrow,general, and super.
Narrow AI is the only type of AI we can achieve so far. It’s the type of intelligence that’s only good for a certain task (or a few certain tasks).
It’s sometimes referred to as “weak AI”, but not due to actually being weak — they’re just not intelligent at a human, or near-human level.
Narrow, or weak AIs can still perform tasks that would take a regular human (or a team of humans) years to achieve, even if they’re not good for anything else.
General AI, sometimes referred to as “strong AI” is the AI type that’s closest to human intelligence.
So far it has been unachievable, even though it’s quite complicated to define what human intelligence actually entails.
General AIs would relate to their environment the same way a human would.
They would be able to perform various, sometimes simultaneous, tasks, just like we do.
Even though computers are millions of times better than us at analyzing and processing raw data, they’ve never been able of thinking abstractly or coming up with original ideas.
Some scientists believe that general AI is “just around the corner”, even if they’ve been saying that for a number of years.
On the other end of the spectrum there are those who believe that we will never need such type of AI.
If narrow AI is a weak AI, and general AI is a strong, human-level AI, then it’s quite obvious what a super AI Like the name indicates, a super AI would theoretically surpass human intelligence in ways we can’t imagine.
A super AI would be better than us at everything, from more academic and scientific efforts, all the way to creative and social endeavors.
Of course that if general AI is still an unattainable dream, super AIs won’t be coming any time soon.
Although it’s worth considering that there are those who believe that there will be a super short distance between general and super AI.
Our robotic friends will continuously improve themselves until they become our robotic overlords.