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.
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.
Machine LearningThen, as a way of building artificial intelligence, we have machine learning. It’s possible to build AI’s without resorting to machine learning, but it’s simply not worth it.
To manually create AI would require millions upon millions of lines of highly complex code.
So, as an “easier” alternative, we can tell machines to learn the task themselves, without them being necessarily programmed to perform that particular task.
What is Machine Learning?
Machine learning, at a very simple level, is basically feeding data to a computer to train it. The machine — or, more specifically, the algorithm — improves itself based on the data it has been fed and it learns.
Most machine learning requires constant input by humans and being fed mostly-labeled data sets, although unsupervised algorithms exist as well.
How Does it Work and What is it Used For
It’s easy to understand machine learning if we think of a few examples.
Think of a software built to predict the risk of fire in a given area.
This software predicts the possibility of a fire happening in the area based on the data it has (stats from previous fires, correlations between temperature rise and fire, etc.) — the more data you feed the system, the more accurate the model becomes.
The program learns and adjusts its model according to the data you provide.
As it fails to predict a fire, or as it successfully predicts it, you then feed that data back into the system, allowing it to optimize itself and to become better at predicting fires.
There’s an example of machine learning people who use social networks usually encounter when they upload a new photo.
Facebook, for instance, tries to suggest people to tag on photos users have uploaded .
It may seem strangely accurate — how did it know it was Mark in the picture?
Well, after Mark has been tagged a few times on different pictures, the algorithm begins to understand what exactly is Mark by analyzing the pixels that define him in a given photo.
After a while, it’s able to “see” Mark whenever he appears on a photo and suggests his profile for you to tag.
What’s actually happening is that you are feeding the algorithm data sets labeled “Mark”, and whenever you upload a new photo the algorithm checks if the data you now provided bears any resemblance to Mark’s data.
You also encounter examples of machine learning outside of Facebook.
Whenever you have to solve a picture-based Captcha — those where you’re asked to click on every car, or every street sign – you’re effectively optimizing a system that recognizes the objects you’ve tagged.
What is Deep Learning?Deep learning is at its core machine learning. However, it works — pardon the pun — at a deeper level than machine learning. Deep learning tries to mimic the human brain, by using Artificial Neural Networks (ANNs).
As layers stack on top of each other, each with its different purpose, depth is achieved — hence the name.
How Does Deep Learning Work?
While machine learning mostly requires being fed labeled data, deep learning breaks unlabeled data into smaller chunks and hierarchizes it, trying to figure out which parts are more relevant to the task at hand.
For instance, let’s say we want to differentiate pictures of squares from pictures of triangles.
With machine learning we would need to tell the algorithm that squares have four sides while triangles just have three.
The algorithm would then look at the pictures provided, count the number of sides and sort them accordingly.
With a deep learning algorithm we wouldn’t need to tell the algorithm how many sides each geometrical figure has.
A deep learning algorithm would look at a square, break the square down into lines, count the number of lines, figure out how each line relates to each other and then do the same to the triangle, differentiating them.
It would move from the simpler task (breaking figures down into their basic components) to the more complicated ones (telling them apart).
After a while the algorithm would be able to instantly tell if new pictures were of triangles or squares.
Of course that for this to happen, deep learning algorithms need loads of data — way more than regular machine learning algorithms.
How do Machine Learning and Deep Learning Compare?Now that we have a simple understanding of what these terms are and how they behave, lets try to understand how they compare to each other.
Performance vs Amount of Data
This is perhaps the biggest difference between the performance of machine learning and deep learning algorithms.
The smaller the amount of data is, the worse the deep learning algorithm performs.
As we said, deep learning algorithms need large amounts of data to be able to perform optimally.
Machine learning algorithms work better with lower amounts of data due to being handfed information.
Obviously, with larger amounts of data, deep learning algorithms outperform simple machine learning ones.
While machine learning algorithms can work on lower-end machines, deep learning algorithms require complex and sophisticated hardware.
Due to the amount of matrix multiplication operations that deep learning algorithms perform, they require several GPUs, which are built for that very purpose.
So, using deep learning algorithms is much more expensive than using machine learning ones.
Is Labeling Data a Requirement for Deep Learning?
Deep Learning and Machine Learning need different types of data in order to work – Machine Learning, as we’ve seen, needs labeled data while Deep Learning doesn’t.
Feature engineering is basically labeling data in order to make it less complex so patterns become more evident and algorithms can work.
It’s a very difficult and complex process.
In machine learning data needs to be labeled by a human and hand-coded into the algorithm. The algorithm’s performance will vary depending on the labels provided.
Deep learning algorithms work differently. They don’t need to be handfed labels like machine learning algorithms do.
They try to learn lower-level features such as lines and work their way into progressively more complex features.
Machine Learning and Deep Learning Solve Problems Differently
Generally, to problem solve using a more common machine algorithm we break the problem down into smaller chunks to solve each part individually, combining them at the end.
Deep learning works differently by solving the entire problem at once.
Of course that for this to be a viable option you need to take into consideration the drawbacks of deep learning algorithms, such as the need for large amounts of data and the large power consumption.