Overview
As we have been witness to technological upgradation, we can’t even imagine what so far we can achieve from technology. We’re human beings and the most active living organisms on the earth who invented/found technology such as Artificial Intelligence, Machine Learning, Deep Learning, etc. Today we can see some of the biggest inventions of such technology found us from starting from ordering food online to flying in the sky! But did we ever think how far we can go with technology?
What’s Next?
In this comprehensive guide, we will go behind the scenes and explore the terms Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. We will go through the practical examples a little further deep into each term.
So, aren’t you excited to go deeper and explore such revolutionary terms of the technology world? Beginning from AI to DS, we will cover up each term in a bit more depth and leave better footprints of understanding.
Table of Contents
What is Artificial Intelligence?
In simple language, Artificial Intelligence stands for AI, which is the technological way to teach a particular object to mimic human behavior such as teaching a robot how to stand up, pick up an object, and even walk two steps ahead.
In technological language, Artificial Intelligence refers to intelligent computer systems that generally need human intelligence to perform tasks such as learning, problem-solving, reasoning, etc.
AI systems aim to mimic the human brain functions to some limit which allows machines to make new decisions, adapt the situations, and improve their performance during the training period.
Example: Self-Driving is one of the examples of AI, however, AI is not solely in Self-Driving but AI plays an important role in Self-Driving. It uses various sensors like cameras, radar, and ultrasonic sensors to gather data about the nearest surroundings which helps to detect the other cars.
What are the types of Artificial Intelligence?
However, AI is a broad concept and includes a few types of AI but what are the most broadly used AI are:
- Narrow or Weak AI (ANI): Narrow AI refers to a specialty in a specific field. In this, AI devices are limited to learning under a specific task and are unable to go beyond its field.
Example: Virtual personal assistants like Siri or Alexa, which are trained on recommendation algorithms to always suggest the best answers to the users’ questions, but beyond its limit Siri and Alexa can’t ever recognize a face to unlock the face.
Another example is Image recognition, in smartphones, Face Recognition is the example of image recognition which recognizes the person’s face to unlock the phone. - General or Strong AI (AGI): General AI refers to learning beyond its range. It can adapt to new situations, understand, learn from experience, and apply the knowledge in real-world activities across a wide range of tasks. It is special to perform tasks in its field but enables it to learn beyond it.
Currently, the AGI does not exist, and almost all the AI is dependent on Narrow AI.
Example: An AGI system might have the ability to perform tasks the same as humans do like problem-solving, thinking, reasoning, and even more beyond its range like NLP and creativity.
What is Machine Learning?
Machine Learning (ML) is the subset of Artificial Intelligence, i.e., one of the components of the AI. ML aims to develop the algorithms to train computers to learn from the data and improve their performance over time. Its algorithm makes the computer system capable of performing tasks without human intervention.
Example: A robot in the restaurant is designed on the algorithms to welcome the customer at the gate or serve a glass of water to the customers.
What are the types of Machine Learning?
However, there are a few types of ML, but let’s take a look at the 3 most common types of ML.
1. Supervised Learning: In Supervised learning, the model is trained on the labeled data, i.e., input and output data are already defined to train the model. The input data is defined as X whereas the output data as Y. The goal of the model is to learn from the relationship of data X and Y to make a new prediction based on the connection of X and Y.
Example: One of the common examples of Supervised learning is image recognition. A model is provided with a couple of fruit images and is trained to predict the right identity of fruit images, based on the trained algorithms.
2. Unsupervised Learning: In Unsupervised learning, the model is provided with unlabeled data i.e., only input data and no output data. The main aim of the model is to find the pattern, understand the structures, and draw the pattern between the data to predict the desired output.
Example: One of the common examples of Unsupervised learning is to, Clustering i.e., grouping. Where the model is trained to create a group of people who made the exact Rs. 100 of transactions. The model groups similar data based on customers’ purchase behavior and activities.
3. Semi-Supervised Learning: In Semi-Supervised Learning, the model is trained on the combination of both Supervised and Unsupervised algorithms. It means that the model is provided with labeled and unlabeled data. This method is applied when the training model on labeled data is expensive while unlabeled data is easily available.
Example: Let’s say a model is provided with some image dataset. Among the dataset, a couple of data are labeled data (Supervised Learning). In semi-supervised, the model is trained with both labeled and unlabeled datasets. The model reads labeled data images to understand the patterns, and the unlabeled data images to further understand the comprehensive features.
What is Deep Learning?
The concept of Deep Learning is driven by Neural Networks. The main goal of Deep Learning is to mimic the human brain activities to perform tasks like humans. The model is trained on the Neural Networks, which are the brain cells neurons, enabling models to predict the best results even in more complex and large datasets. As with many Neural Networks layers, the model is trained on, more chances arise to get the desired results.
In simple words, Deep learning is the subset of Machine Learning. Machine Learning is used to deal with structured data whereas Deep Learning is used to handle complex and large-scale structured and unstructured data. It works on Neural Networks which are characterized as layers. The layers are composed of input layers, hidden layers, and output layers.
Conclusion
As we have looked at what is Artificial Intelligence, Machine Learning, and Deep Learning, then we can think ahead about how technology is changing the world! These concepts are one of the pillars of the technology world and sum up the capability of innovating something new every day. As we move ahead, we will explore how these concepts are making their role important in real life and helping humans to solve complex problems easily.
