Data Science vs AI vs ML vs DL

- Overview

Understanding the differences between Data Science (DS), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) is crucial because they represent a hierarchy of concepts, with DS being the overarching field that utilizes techniques like ML and DL to extract insights from data, while AI is the broader goal of creating intelligent machines that can mimic human capabilities, with ML being a key method to achieve this. 

Essentially, DS focuses on data analysis and interpretation, while AI aims to create systems that can "think" and learn, with ML providing the algorithms to enable that learning process, and DL being a specific advanced type of ML that uses neural networks to tackle complex tasks like image recognition and natural language processing. 

Please refer to the following for more information:

• Wikipedia: Data Science
• Wikipedia: Artificial Intelligence 
• Wikipedia: Machine Learning
• Wikipedia: Deep Learning

- Data Science (DS) vs AI vs ML

Artificial intelligence (AI) is the broader field of developing intelligent machines, machine learning (ML) is a subset of AI that involves training algorithms to learn from data, deep learning (DL) is a subset of ML that uses artificial neural networks (ANNs) to model complex patterns in data, and data science (DS) is an interdisciplinary field that involves extracting insights from data.

DS, AI, and ML are interrelated disciplines. DS collects, analyzes and interprets data to gain insights. Meanwhile, AI focuses on creating intelligent systems that mimic human decision-making, while ML, as a subset of AI, enables machines to learn from data.

DS provides the data and analytics that drive AI and ML. AI uses data from DS to drive decisions, while ML algorithms are improved through data provided by data science. 

These three (DS, AI, and ML) work in harmony. DS extracts meaningful information, ML enhances predictive models, and AI leverages these models to make smart decisions, working together to drive advances in technology and automation.

Here are some basic definitions of AI, ML and DL:

• AI: Developing machines to mimic human intelligence and behaviour.
• ML: Algorithms that learn from structured data to predict outputs and discover patterns in that data.
• DL: Algorithms based on highly complex neural networks that mimic the way a human brain works to detect patterns in large unstructured data sets.

- ML vs DL

Machine learning (ML) and deep learning (DL) are both types of artificial intelligence (AI) that use algorithms to learn from data. DL is a subset of ML that uses neural networks, which are modeled after the human brain, to automate complex tasks. 

ML is best for well-defined tasks with structured and labeled data. DL is best for complex tasks that require machines to make sense of unstructured data. ML solves problems through statistics and mathematics. DL combines statistics and mathematics with neural network architecture.

ML and DL are two distinct subsets of AI that have unique characteristics, capabilities, and limitations. Understanding the differences between ML and DL is critical as it can help individuals and organizations determine which approach best suits their needs.

[Istanbul, Turkey - Civil Engineering Discoveries]

- Deep Learning vs Artificial Neural Networks

Deep learning (DL) uses artificial neural networks (ANNs) because they provide a computational structure that mimics the human brain, allowing complex patterns to be learned from data by processing information through multiple layers of interconnected nodes, similar to how neurons in the brain work together to extract meaning from sensory inputs.

Essentially, this enables DL models to handle intricate tasks like image recognition, natural language processing, and speech recognition with high accuracy by learning progressively more abstract features from the data through these layers.

• Structure inspired by the brain: ANNs are designed with interconnected nodes ("neurons") arranged in layers, mirroring the structure of the human brain where neurons communicate with each other to process information.
• Feature extraction: By stacking multiple layers, DL networks can progressively extract increasingly complex features from data, allowing them to identify subtle patterns that simpler models might miss.
• Learning through data: DL models learn by adjusting the connections between neurons based on the input data, allowing them to adapt and improve their performance over time.
• Complex problem solving: This ability to learn complex features makes ANNs ideal for tackling challenging tasks that require sophisticated pattern recognition, like identifying objects in images or understanding natural language.

[More to come ...]