As little as a few decades ago, people were predicting that computers would develop a mind of their own. For some, fears of dalek-like robots wiping out humanity was enough to send them into a hysterical frenzy.

It would appear that those predicting the evolution of computers were right. Of course, they haven’t quite managed to grow legs and annihilate all of us. Instead, they have become something to appreciate.

At the very hard to all of artificial intelligence, machine learning and deep-learning are algorithms. So to be able to understand the differences between the three you first need to understand what an algorithm is.

What is an algorithm?

Algorithms are rules that machines use to solve problems. They take the data, calculate it and then come up with an answer. It’s all about efficiency. After all, they would be of no use if the calculations were worked out incorrectly. They would also be useless if a human could analyze the data faster than a machine.

Every type of algorithm involves an element of training. This training helps information to be classified and processed. The better the algorithm is trained the better the accuracy and efficiency will be. When an algorithm is used to perform a calculation, it doesn’t mean that artificial intelligence or machine learning was used.

Today, it is not uncommon to see artificial intelligence and machine learning being used to indicate that algorithms were used for analyzing data and making predictions. Machine learning is not using algorithms for predicting outcomes of events. It is taking those outcomes off your prediction and using them for improving future predictions.

AI vs Machine Learning vs Deep Learning

It is not uncommon for people to use machine learning and artificial intelligence interchangeably. It is important to note that they are different things and are used in different ways.

AI and Machine Learning

When looking at AI and machine learning, AI covers a broader concept.

AI is when computers are used for mimicking human cognitive functions (thinking), so, when a machine intelligently performs a task based on a set algorithm, this is AI. While artificial intelligence requires set algorithms to be able to perform properly, machine learning works differently.

Machine learning is when machines are able to take data and learn on their own. As they begin to learn more during the information processing, they can change algorithms.

The process of training a computer to be able to think like a human uses neural networks. A neural network uses a set of algorithms that have been modeled on the human brain. When you look at a pattern, your brain helps you classify and categorize information. This is exactly the same way that a neural network works for a computer.

The human brain is forever trying to understand information and does so by labeling and assigning it into different categories. When we experience something for the first time, our brains try to help us understand it by comparing it to a past experience or a known item. This is the same process that neural networks do for computers.

  • Neural networks take complicated data and extract its meaning.
  • They are able to identify patterns and detect trends beyond the understanding of the human brain.
  • They also learn by example, and are able to workout complicated data, patterns and trends at lightning speed.

Basically, artificial intelligence is a broad concept, while machine learning can be seen as a subset of artificial intelligence.

Deep Learning vs Machine Learning

Now we have explained the differences between artificial intelligence and machine learning. Let’s take a look at deep learning vs machine learning.

Just as machine learning is a subset of artificial intelligence. Deep learning can be seen as a subset of machine learning.

While machine learning uses a single layer of data in its neural network. Deep learning uses multiple layers that allows it to answer the first question while also formulating a series of deeply related questions.

For a deep learning network to be trained, it needs to be exposed to a large quantity of items. Exposure to millions or even billions of data points helps the system to learn. One can’t simply program it using a the items defining edges.

One of the earliest examples of deep learning is Google Brain. After it was shown more than ten million images of cats, it was able to learn to recognize them on its own. Smart right!

Ultimately, deep learning networks are exposed to huge quantities of data. This data helps them to identify item edges without they need to program them with the items defining criteria.

In a Nutshell

Hopefully, this has cleared up any confusion that you may have been having between artificial intelligence, machine learning and deep-learning. With the three different types becoming more common for everyday use, understanding what is what can help you find exactly what it is that you want.

Artificial Intelligence

AI is the broader spectrum encompassing deep learning and machine learning. It is what makes it possible for machines to be able to perform human cognitive functions, switch to new day tercets and learn from experience.

Machine Learning

This is when the machine is not only able to memorise items. But to recognise and to learn set laws, patterns and behaviors using a neural network.

Deep Learning

This is when machine learning is taken to a deeper level with the use of a multiple layered neural network. Deep learning enables a machine to understand the big picture while solving problems using predictive reasoning and no programming.

From understanding algorithms, providing security and answering complex questions. All three of them do this to a certain degree with artificial intelligence being at the lower end of the range and deep learning at the upper end. And there’s no need to worry. Artificial intelligence doesn’t look set to start locking us in smart homes and torturing us anytime soon.