A Distinctive Introduction to Artificial Intelligence, Machine Learning, and Deep Learning

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Artificial Intelligence, there is a lot of buzz going all around the world for Artificial intelligence, Machine Learning, and Deep Learning. Even though there is so much buzz, this is going to be a great revolution in the coming years. The world’s topmost companies are investing a lot for research in these fields, on what more they can get from AI. But, AI is not something that has been discovered recently. It existed in the world for a very long time. The term AI was first given by John McCarthy in 1956. Yes, you heard it right in 1956. So why is there so much talk and so much work going on now after a very long time? The reason is computing constraints. The constraints were to have proper storage for a large amount of data and the major reason is computing power. There were no such efficient computers that can be used to compute these large amounts of data to solve real-world challenges. The reason why AI is trending now is all because of the computing power we have today. We have seen the transformation in processors too. From CPU to GPU and from GPU to TPU soon we will be witnessing quantum computing too.

So let’s talk about What actually Artificial Intelligence is?

What is Artificial Intelligence(AI)?

AI Robot

As the name itself suggests intelligence which is performed by an artificial material medium( with the use of computers). When you analyze different sites, you will see that there is no particular definition of Artificial intelligence. This is because AI is a very broad field and there is no benchmark, as no one knows what’re the actual capabilities of a computer to perform any task. There are a lot of applications of AI in every field. Be it an automobile, Healthcare sector, Finance sector, E-Commercial websites, and counting. Recently, Tesla cars have launched their first AI-based cars, in which cars are driven by AI in the auto mode. In day to day life even we use AI to assist ourselves, Google Assistant, Alexa, Siri are some fabulous AI examples. We use Facebook frequently, where we upload our pictures, group photos where facebook highlights the face and suggests the name of the person. This is a classic example of AI which is used by Facebook. One of the research which I find amazing is Libratus. The AI researchers at Carnegie Mellon University have developed an AI bot known as Libratus which can play poker and has even defeated the pro poker players for straight 20 days.

AI can help in business in a tremendous way. Fraud Detection in the Financial Sector, based on the transaction data the model is trained using algorithms to detect whether the transaction is fraud or genuine. Similarly, a model can be trained using customer data which would predict what kind of loan can be provided to a particular customer. Now let’s talk about machine learning.

What is Machine Learning(ML)?

Artificial Intelligence is the universe in which Machine learning is a planet. ML is the field that uses advanced mathematics, algorithms, and statistics to deal with real-world problems. In simple terms, machine (computers) learning from its previous experience (provided data) using the search algorithms is what we call machine learning.ML model is developed by using the inferential statistics, descriptive statistics and the patterns the data depict and predict the future based on the provided data-set. Machine learning is used to deliver an AI model.

Machine Learning

Machine Learning Services are categorized into three parts:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning: The learning which includes the labelled feature or dependent variables of data, Where we know what kind of features we are dealing with and have some description of it from the dataset.

Supervised learning is again divided into two parts:

  • Regression: This method is used where we have to predict a continuous value. For eg. Number of sales, predicting the price of the house from the given features.
  • Classification: This method can be applied where you need to classify the output. For eg. Email, spam or not, predicting to offer a loan based on some rules.

Unsupervised Learning: In this, there is no specific name or description of the column. We don’t know what the features have to say about the dataset in predicting the outcome. In this method, mostly clustering is used where a certain observation is grouped with its similar observation and creating different clusters based on some differentiation between the clusters.

Reinforcement Learning: This type of study is based on reward points. For eg. If a child throws a ball at us, we applaud it with a happy gesture which is positive. Thus the child understands that doing this is right. Similarly, when a child does something wrong we give him a negative point from which a child understands that this shall not be done again.

Similarly, the agent is left in an open environment in which it performs the task as suggested. At every good work, we give the agent a positive reward and every wrong or bad work we give a negative reward. Thus using this reward points the agent learns by trial and error case and thus itself increasing the accuracy to perform any task.

We come across ML in our daily life too. For example, Email spam filters, recommendation systems are some classic examples.

What is Deep Learning(DL)?

Deep learning is what lies under the planet. DL is another technique to deliver AI models. Deep learning is an effort to replicate the human brain and how it works. DL makes use of neurons, similar to what our brain has, thus forming a network of neurons to execute a specific task. This is known as artificial neural networks. It is known as Deep learning because it forms several layers of neurons, thus creating deep neural networks. When there is a large amount of data it becomes difficult to work using machine learning algorithms, here comes DL as a solution to this which is easy to work with when it comes to large data and also provides higher accuracy as compared to ML algorithms.

Deep Learning

Deep Learning(DL) can also be used for a wide range of applications and covering almost every field. DL can be used in the Defence sector to identify the unknown objects, in the automobile sector where DL is the reason behind the driverless cars. With the advances in the Industrial IoT (also known as IIoT) DL can be used in the manufacturing sector to ensure the safety near the heavy machinery or the machinery which can risk human life.

Wrapping Up

I hope you all find this blog knowledgeable. If you have any query related to machine learning or you are looking for machine learning services for your business or industry needs. We will be glad to assist you with quality yet performance-driven machine learning development services. Our machine learning experts are also available for machine learning consulting services. Let’s get in touch to discuss all your queries and development requirements.