Difference Between Artificial Intelligence, Neural Networks, Machine Learning and Deep Learning You Need to Know

We are living in the era of Artificial Intelligence where machines getting smarter than ever before. Currently, AI and its sub-fields including Machine Learning, Deep Learning, and Reinforcement Learning are already begun to show their's potential in the digital world. Tech tycoons including Google, Microsoft, Facebook are falling behind of this game changer technology to feed their products with extreme intelligence.

In this article, we are going to take a look at AI and related branches.

1. Artificial Intelligence.

It is very difficult to explain technically but in simple terms, Artificial Intelligence is a field of computer science associated with the development of computing systems which exhibit human-like intelligence in making decisions and actions. These computing systems are programmed with advanced algorithms which are specifically designed for AI.

The term 'Artificial Intelligence' was first appeared when John McCarthy( a computer scientist ) was submitted a summer research project named '2 month, 10 man study of artificial intelligence' on 1955. This project was carried out by a set of computer scientists from different universities and organizations. The main aim of this research project was to find ways to turn machines into intelligent systems that could use language and solve kinds of problems reserved for humans.

These systems are programmed in such a way that it can interact with humans in a more natural way and it can learn itself from its previous tasks. Few real-world examples of such AI systems are IBM Watson, Apple's Siri, Google Assistant and more. These systems are very trendy nowadays in providing information to the users in an automated way. These systems are just a few examples which have simple features of AI. But the actual potential of AI is much larger than we can imagine and all of these are yet to be seen in future.

2. Artificial Neural Network (ANN)

An Artificial Neuron is a basic entity of an AI system which mimics the working of actual brain's neuron. Brain's neurons are the real inspiration for modeling and generating such artificial, computer-generated neurons used to process information in AI systems. So let us know how a real neuron works.

In human's brain, there are over 100 billion neurons exists and are connected to each other forming a network called Neural Network. Each neuron has three parts which are dendrites, axon and terminal branches of the axon.

Based on the external environment, neurons accept the status of senses (vision, sound, touch etc) from organs as inputs (data) through their dendrites. These inputs are in the form of electrical signals. Neurons process these electrical signals (data) in axons and based on situations and conditions it will produce the output through terminal branches of the axon which are in turn connected to other neurons. Thus the information is processed and transferred through this network of neurons and at last one or more final outputs are generated as an action.

An  ANN is formed by millions of nodes each of which can have data stored in it. One neuron is connected to another neuron through a link called edge which has a particular weight. Based on the algorithm, each artificial neuron can perform operations and can transfer the calculated data (output) to another neuron. Basically, this output is called as activation. After processing the data the system can generate a reasonable output based on the input that it has.

3. Machine Learning

Machine Learning is a branch of Artificial Intelligence which provides the ability to systems learn themselves from its previous experiences without explicitly programmed.  Here the experiences refer to the tasks it had done. Machine Learning systems are generally used where the computation is required for automated analysis for large sets of data.

Though Machine Learning is not a new concept in computer science, its applications gave different approaches to do analysis on large volumes of data. In recent years, as we all know that data charges are significantly fallen down and resulted in the collection of immense amount data from users. This collected data could be analyzed so that it can be used to understand user behavior and increase organization's productivity.

This kind of analysis could be easily done by Machine Learning systems. Different ML algorithms are imposed on this large data to understand the different patterns that the data has. Each time it makes a decision, it updates itself with the data that it had computed so that it could predict a decision more accurately next time. Like this, the system used to learn from itself so that it can make more reliable and accurate decisions for the future computations.

Nowadays Machine learning is used extensively in almost everywhere where computation is being employed. From pattern recognition to make complex decisions in business world Machine Learning is being used. Apple, Google, Netflix, Amazon are some of the company's who employed Machine Learning in their popular applications.

4. Deep Learning

Deep Learning is another branch of AI which is totally associated with the data and its patterns to recognize another pattern of data. It normally fed with large sets of data and used to detect patterns of another data such as images by analyzing the current data that it has. These data are stored in neural networks which is, in turn, are organized in different layers. The input is passed through such different layers of neural networks to predict the actual content that it has.

Basically, we have to provide a lot of data as possible to get things done accurately. The larger the data is, the accurate the output. For example, to recognize a picture of a dog accurately, we must give at least tens of thousands of different dog images as input. Based on this data, the system uses different patterns on different images to recognize what content the picture has in it.

Deep Learning also has immense practical applications. To recognize the voice in Google Home, Siri and various other voice recognition systems use Deep Learning to get things done. In Medical field Deep Learning is used to detect the structure of DNA in quick time. Different online shopping firms employed Deep Learning to detect what its consumers might like to buy in their products list. Facebook and Google use Deep Learning to detect the patterns of voice and images and the list goes on.

We could say that it is just beginning of Artificial Intelligence era, where great magics are waiting to happen in the digital world. What do you think about these technologies? Leave a comment below.