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AI Vs Machine Learning Vs Deep Learning: Understanding The Differences 

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Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are terms that have recently gained immense popularity in the world of technology. They represent the latest advancements in computing, allowing machines to “learn” from experience and evolve their behavior over time. But what is the difference between AI, ML and DL? In this blog, we will explore the distinctions between these three technologies and how they can be applied to various use cases. By the end of this article, you will have a better understanding of what sets AI, ML, and DL apart and how they can be used to drive innovation.  

Here’s an illustration to help you understand the key distinctions between artificial intelligence, machine learning, and deep learning. 

Basically, the concept of artificial intelligence is the creation of smart and intelligent machines; and machine learning is a subset of artificial intelligence that assists in the development of AI-driven applications. Finally, deep learning is a subset of machine learning that uses massive amounts of data and complex algorithms to train a model. 

What Is Artificial Intelligence? 

Definition 

The process of transferring data, knowledge, and human intelligence to machines is known as artificial intelligence, or AI. The creation of autonomous machines with human-like thinking and behavior is the primary objective of artificial intelligence. These machines can mimic human behavior and carry out duties by studying and solving problems. The majority of AI systems mimic natural intellect to handle challenging issues. 

Types of AI 

There are three primary types of artificial intelligence based on capabilities:  

• Narrow AI (weak AI) – Designed to solve a specific task. It focuses on a single, narrow area of expertise. 

• General AI – Capable of recognizing patterns and making decisions based on those patterns. It has the potential to handle any problem or task. 

• Super AI – Much more advanced than general AI, it has the ability to think on its own and make decisions without being instructed by humans. 

Based on functionality, AI can be categorized into four types: 

• Reactive Machines – These systems are designed to respond to the environment or input. They lack memory, making them unable to use their experience for future decisions. 

• Limited Memory – These AI models rely on previous experiences and can make decisions based on the data available. 

• Theory of Mind – AI models with this capability have an understanding of the emotions and beliefs of others. 

• Self-awareness – These models are capable of recognizing their current state, responding to it, and adapting to new situations.  

Applications of AI 

• AI is used in a wide range of applications, including robotics and automation, computer vision, natural language processing (NLP), and virtual assistants.  

• It has also been adopted in healthcare to diagnose and treat diseases, detect cancerous cells, analyze medical images, identify signs of depression and anxiety, etc.  

• AI-powered financial services such as automated investment advisors, fraud detection systems and trading platforms are being increasingly adopted.  

• AI also enables autonomous vehicles to navigate and detect obstacles on their own, thereby improving road safety. 

What Is Machine Learning? 

Definition 

Machine Learning (ML) is a subset of artificial intelligence that enables machines to “learn” from data without being explicitly programmed. ML algorithms can identify patterns in data, learn from them, and make decisions based on what they’ve learned. It is used for predictive modeling and to make automated decisions about data. 

Types of Machine Learning 

There are three primary types of machine learning: supervised, unsupervised, and reinforcement.  

• Supervised Learning – This type of ML requires labeled data sets. It uses input-output pairs to learn how to map input to output. The training data set is used to learn the mapping function and then produce an accurate prediction for a new data set.  

• Unsupervised Learning – This type of ML uses unlabeled data sets. It uses input-output pairs to identify patterns and relationships between the inputs without requiring any labels or targets. 

• Reinforcement Learning – This type of ML requires feedback from the environment in order to learn how to make decisions. It learns by trial and error and is typically used for gaming, robotics, and autonomous vehicles.  

Machine Learning Process 

A basic procedure of machine learning can be described as below: 

Applications of Machine Learning 

A range of industries have adopted machine learning to process large amounts of data quickly and accurately. Some common applications include:  

• Image Recognition – ML algorithms are used to identify objects, faces, landmarks, etc., in images or video recordings.  

• Natural Language Processing – ML algorithms are used to process and analyze large amounts of text. They are able to identify the sentiment in a sentence, detect plagiarism, and suggest accurate spellings or corrections.  

• Predictive Modeling – ML algorithms are used to predict outcomes based on historical data. They can be trained to recognize patterns and make accurate predictions about future events.  

• Recommendation Systems – ML algorithms are used to recommend products, services, or content to users based on their past interactions.

What Is Deep Learning?

Definition 

Deep learning is a subset of machine learning that uses artificial neural networks to train models on large amounts of data. It is capable of recognizing complex patterns and making decisions with minimal input from humans.  

Types of Deep Learning and Their Applications 

• Convolutional Neural Networks (CNN) – These are networks composed of multiple layers, which use convolutions to map data points from the previous layer. They are used in image recognition and natural language processing tasks.  

• Recurrent Neural Networks (RNN) – These are networks that contain loops, which allow them to remember information over time. This makes them useful for tasks such as language translation and text generation.  

• Generative Adversarial Networks (GAN) – These are networks composed of two parts: a generator, which creates data points, and a discriminator, which evaluates the generated data points. They are used for image synthesis and manipulation.  

• Deep Belief Networks (DBN) – These are artificial neural networks that use hierarchical probabilistic graphical models to represent multiple layers of interconnected variables. In other words, DBNs connect each node on the lower layers with all the nodes on the higher layers. This connection exploits uncertainty in deep learning and allows for reliable generalization capabilities by compositing them into better and bigger models. Moreover, DBNs provide interesting advantages in terms of scalability when compared to classical deep-learning techniques due to their capability of compressing salient features in hidden units. As such, DBNs have been deployed successfully in diverse areas such as audio processing and natural language processing. 

Wrap Up 

To summarize, while AI aids in the development of intelligent machines, ML aids in the development of AI-driven applications. DL is a subset of ML that uses complex algorithms to train a specific model on large amounts of data. Because narrow AI is extremely difficult to develop, ML is using rigid computing to address the opportunities in this space. DL aids in the integration of AI and ML, at least in the case of general AI. 

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