
According to a study by McKinsey Global Institute, AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even today the Artificial Intelligence technologies are generating a tremendous amount of revenue, but it is mostly in the software field.
However, by 2030, the revenue that will be generated will be outside the software industry, especially in sectors such as retail, travel, transportation, automotive, materials, manufacturing, and so on.
There are several areas where AI will be highly impactful, but there is a lot of unnecessary hype surrounding it as well. Goldilocks Rule of AI states that one should not be optimistic or too pessimistic about AI technology.
While it is awesome to acknowledge the realistic expectations that AI will embrace the world with, it is equally significant to understanding and debunk the myths surrounding AI.
The buzz-words Artificial Intelligence, Machine Learning (ML), and Deep Learning (DL) are used quite frequently in recent times. Let us introspect about each aspect individually to really appreciate these concepts.
In this article, we will uncover all the concepts of artificial intelligence and understand each aspect of it perfectly so that there remains no confusion on this subject.
So, without further ado, let us begin to crack down – Artificial Intelligence.
Narrowing the field of AI

The subject of artificial intelligence is humungous similar to the massive milky way galaxy. Artificial Intelligence (AI) is a broad field with many sub-categories such as natural language processing (NLP), artificial neural networks, computer vision, machine learning, deep learning, robotics, so on and so forth. The formal definition of AI is –
"The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages."
However, even before we start diving deeper into these other fields, Artificial intelligence can be narrowed down to two separate broader fields as follows:
- Artificial Narrow Intelligence – Perform one or a few particular tasks that they are programmed to do. Examples of these are self-driving cars, next word predictions, autocorrect, etc. This is the main concept that we will be focusing on.
- Artificial General Intelligence – These perform human-like activities and tasks. General AI is a type of intelligence that could perform any intellectual task with efficiency like a human. There is still a lot of progressions to be made in this field.
There is also another term called super AI or strong AI intelligence which is the AI that is believed to surpass humans. However, it is only a hypothetical concept and will not be discussed in this article as we are still far away from reaching this level of intelligence in the upcoming years.
To understand the concepts of AI and the various aspects surrounding it, I have developed a simple analogy of the universe with respect to the universe.
The humongous Milky Way galaxy is similar to the broad field of AI. It contains billions of solar systems similar to how AI consists of so many sub-fields. Our solar system is similar to one of the sub-fields in AI which is "machine learning". The earth which is the only habitable planet in our solar system can be referred to as "deep learning".
With our analogy, we can conclude that artificial intelligence is similar to the vast milky way galaxy which is a broad field consisting of sub-topics such as machine learning which can be compared to our solar system, and deep learning which is a sub-topic of machine learning is similar to the earth in the solar system. Below is a simple reference to understand this analogy better.
AI = Milky Way Galaxy | ML = Solar System | DL = Earth
Let us discuss the other significant aspects of artificial intelligence, namely, machine learning, deep learning, and Data.

Machine Learning:
Machine Learning is the ability of a program to learn and improve its efficiency automatically without being explicitly programmed to do so. This means that given a training set you can train the machine learning model and it will understand how a model exactly works. Upon being tested on a test set, validation set, or any other unseen data, the model will still be able to evaluate the particular task.
Let us understand this with a simple example. Assume we have a dataset of 30,000 emails out of which some are classified as spam and some are classified as not spam. The machine learning model will be trained on the dataset. Once the training process is complete, we can test it with a mail that was not included in our training dataset. The machine learning model can make predictions on the following input and classify it correctly if the input e-mail is spam or not.
The three main types of machine learning are as follows:
- Supervised Learning – This is the method of training the model with specifically labeled datasets. The datasets can either be a binary classification or multi-class classification. These datasets will have labeled data specifying the correct and incorrect options or a range of options. The model is pre-trained with supervision i.e. with the help of these labeled data.
- Unsupervised Learning – Unsupervised learning is the training of the model on an unlabeled dataset. This means the model is given no prior information. It trains itself by the grouping of similar characteristics and patterns together. An example of unsupervised learning can be the categorizing of dogs and cats.
- Reinforcement Learning – Reinforcement learning is a kind of hit and try method model. This is the method where the model learns with repeated failures. When a model does not achieve the desired result then the model will re-train. This can be applied to concepts like playing chess where after playing millions of games the model will be able to learn appropriate patterns and moves.
With the idea and perception of machine learning clear, we can now move ahead to the essential concept of data and why it is so crucial to us.

Data:
Data can be considered as any useful resource or information available that is suitable for performing machine learning or deep learning tasks. There is a ton of data available for every model you want to construct. It is important to scrape and find only the valuable data required for the completion of the assessment.
A data set is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.
The popularity of AI is rising faster than ever in present times, and for this, we have to thank the abundance and increase of Data. Due to this increase in the amount of data, the growth of AI is highly accelerated.
More data results in better training of the machine learning or deep learning models because we are able to train the models on bigger datasets, which help the model to learn better during training and usually perform the task at hand better.
Data Science is all about data. The term may be loosely thrown out sometimes, but it is the most valuable resource for any project. The fields of big data, data science, and data analytics are growing tremendously. Tech giants are investing more in the collection of useful data.
Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. To learn more about data collection and other essential techniques required for every machine learning and data science project, check out the below article.
10 Step Ultimate Guide For Machine Learning And Data Science Projects!

Deep Learning:
Deep Learning is a sub-field of machine learning which works on concepts of artificial neural networks to perform specific tasks. Artificial neural networks withdraw inspiration from the human brain.
However, it is paramount to note that they do not function theoretically like our brains, not even close! They are named artificial neural networks as they can complete precise tasks while achieving a desirable accuracy without being explicitly programmed with any specific rules.
A few decades ago, deep learning was extremely popular, but it eventually lost most of its hype due to the lack of data and technologies to compute complex computations.
However, this has changed significantly for the past few years. The abundance of data is surging every day because big tech giants and multi-national companies are investing in this data. The computational power is also no longer such a big issue due to powerful graphics processing units (GPUs).
The aggrandizement of deep learning is rapidly increasing each day especially, with vast improvements. Deep learning is extremely popular today and has an excessive potential to outclass most Machine Learning algorithms of the modern day.
If you are interested in learning more about the history of deep learning and artificial neural networks, then feel free to visit the following article.
The Complete Interesting And Convoluted History of Neural Networks!

Conclusion:
Artificial Intelligence is the fastest-growing field in the present-day. According to fortune, the statistics say that the hirings for AI specialists have grown by 74% over the last 4 years. Artificial Intelligence is regarded as the "Hottest" job of the present generation.
The demand for skilled AI specialists is growing faster, like never before. Requirements and open positions for experts in the sub-fields of AI like machine learning, deep learning, computer vision, statistics, and natural language processing are surging each day.
We are lucky to be in the age of rising artificial intelligence due to the enormous opportunities present around us. We are surrounded by Artificial Intelligence, and I find the fast pace of progress in this field extremely fascinating. I am excited about the newer technologies in the future and the rise of AI.
I am curious to know how all of you feel about the continuous progression and rise of artificial intelligence. Does it excite you, or you don’t really care much? Either way, it would be cool to know. 😄
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Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!