Data Science vs ML vs AI
Theme of the month : Foundations of Data Science
Data Science, Machine Learning (ML) and Artificial Intelligence (AI) are often used interchangeably to represent the same context. Although they are very similar, there are some fundamental differences that should be understood. In today’s newsletter we will explore the similarities and differences between these terms.
What is Data Science?
Data Science is an all-encompassing term used for all the methods, algorithms, or statistical systems used to uncover the underlying patterns in the data, which helps in making ‘intelligent’ business decisions.
Data Science extends beyond ML and AI systems. The information uncovered can come from machines, simple data visualizations, mechanical systems or even IT systems. It’s not confined to learning alone. AI and ML (as a subset of AI) can be leveraged to uncover information but that is just a subset of Data Science. The term ‘Data Science’ encompasses a broad spectrum ranging from data collection to analysis and is not just limited to algorithmic or statistical aspects.
What is Artificial Intelligence?
Artificial Intelligence is a subset of Data Science. These are ‘smart systems’ that carry out tasks assigned by mimicking human behavior. Following are a few examples of AI systems
Virtual Assistants : Siri, Alexa, Google Assistant, which understands commands in natural language and perform tasks such as making a call, setting alarms, reminders and so on.
Self-driving cars : Cars that can form a perception of their surroundings and navigate safely without human intervention.
What is Machine Learning (ML)?
Machine Learning (ML) is a subset of Artificial Intelligence (AI). It allows machines to learn by using algorithms from the available data and uncover patterns in the data. This enables an ML system to generate decisions or values (also known as ‘predictions’) without explicit programming or rules.
Similarities between Data Science, ML and AI
Data : All three fields have their foundation in Data, they are data-driven techniques to make smart decisions
Pattern Recognition : Recognizing or learning underlying patterns in the data is the basis of all three fields. AI and ML systems employ algorithms that learn the underlying data patterns, while Data Science extends beyond ML/AI to statistical systems and visualizations for pattern recognition.
Iterative Process : All three areas follow an iterative process of data collection, data processing, modelling, evaluation, and refinement for pattern recognition.
Multidisciplinary Applications : All three areas have applications in a variety of industries, ranging from edtech to fintech an every industry in between.
Predictive Power: One of the key commonalities uniting these domains is their shared pursuit of predictive power. Data science is used to forecast future trends. Artificial intelligence anticipates user preferences and behavior. Machine learning enables algorithms to make informed guesses based on patterns they’ve discerned.
Differences between Data Science, ML and AI
Despite the similarities, there are some key differences between the three fields:
Scope of Focus: Data science encompasses a broader spectrum, encompassing the entire data life cycle. AI’s scope extends beyond data manipulation to cognitive tasks like natural language understanding, computer vision, and problem-solving. ML’s scope emphasizes enabling systems to evolve and make predictions based on data-driven patterns.
Objective and Functionality: The primary goal of data science is to extract insights and knowledge from data to inform decision-making. AI aims to enable machines to perform tasks that typically require human intelligence. ML’s objective is to develop algorithms that allow computers to learn from data and make predictions or decisions.
Human Interaction: Data science often involves human analysts who use their expertise to curate and manipulate data and then interpret the results of their analyses. AI strives to reduce human intervention by enabling machines to perform tasks autonomously. Machine learning algorithms autonomously improve their performance over time, but their effectiveness depends on human decisions at critical junctures.
Interview questions
What are the differences between Machine Learning and Artificial Intelligence?
Machine Learning (ML) is a subset of Artificial Intelligence (AI). AI refers to ‘expert systems’ that may or may not contain machine learning as a component. While Machine Learning refers to a particular type of AI that self-learns data patterns using algorithms.What is the difference between inductive Machine Learning and Deductive Machine Learning?
Inductive learning is a bottom-up approach where it starts with learning from specific premises and forms a general conclusion, whereas Deductive learning is a top-down approach where it learns the general premises to form a specific conclusion.
What’s New Today?
Hugging Face introduces LeRobot, which is an open-source ML Model aimed at Robotics [LINK]
NASA has as its first Chief AI Officer [LINK]
Meta shuts down Workspace to shift focus on Metaverse & AI [LINK]
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