MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science

Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make predictions. Data science serves as the overarching discipline that includes artificial intelligence and machine learning, focusing broadly on extracting knowledge and actionable insights from data using scientific and computational methods.

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Data Science Overview

• Data science encompasses any professional role that deals extensively with data, including but not limited to artificial intelligence and machine learning.
• The data science pipeline includes data ingestion, storage, cleaning (feature engineering), and outputs in data analytics, business intelligence, or machine learning.
• A data lake aggregates raw data from multiple sources, while a feature store holds cleaned and transformed data, prepared for analysis or model training.
• Data analysts and business intelligence professionals work primarily with data warehouses to generate human-readable reports, while machine learning engineers use transformed data to build and deploy predictive models.
• At smaller organizations, one person ("data scientist") may perform all data pipeline roles, whereas at large organizations, each phase may be specialized.
• Wikipedia: Data Science describes data science as the interdisciplinary field for extracting knowledge and insights from structured and unstructured data.

Artificial Intelligence: Definition and Sub-disciplines

• Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. (Wikipedia: Artificial Intelligence)
• The AI discipline is divided into subfields:
  • Reasoning and problem solving
  • Knowledge representation (such as using ontologies or knowledge graphs)
  • Planning (selecting actions in an environment, e.g., chess- or Go-playing bots, self-driving cars)
  • Learning
  • Natural language processing (simulated language, machine translation, chatbots, speech recognition, question answering, summarization)
  • Perception (AI perceives the world with sensors; e.g., cameras, microphones in self-driving cars)
  • Motion and manipulation (robotics, transforming decisions into physical actions via actuators)
  • Social intelligence (AI tuned to human emotions, sentiment analysis, emotion recognition)
  • General intelligence (Artificial General Intelligence, or AGI: a system that generalizes across all domains at or beyond human skill)
• Applications of AI include autonomous vehicles, medical diagnosis, creating art, proving theorems, playing strategy games, search engines, digital assistants, image recognition, spam filtering, judicial decision prediction, and targeted online advertising.
• AI has both objective definitions (automation of intellectual tasks) and subjective debates around the threshold for "intelligence."
• The Turing Test posits that if a human cannot distinguish an AI from another human through conversation, the AI can be considered intelligent.
• Weak AI targets specific domains, while general AI aspires to domain-independent capability.
• AlphaGo Movie depicts the use of AI planning and learning in the game of Go.

Machine Learning: Within AI

• Machine learning (ML) is a subdiscipline of AI focused on building models that learn patterns from data and make predictions or decisions. (Wikipedia: Machine Learning)
• Machine learning involves feeding data (such as spreadsheets of stock prices) into algorithms that detect patterns (learning phase) and generate models, which are then used to predict future outcomes.
• Although ML started as a distinct subfield, in recent years it has subsumed many of the original AI subdisciplines, becoming the primary approach in areas like natural language processing, computer vision, reasoning, and planning.
• Deep learning has driven this shift, employing techniques such as neural networks, convolutional networks (image processing), and transformers (language tasks), allowing generalizable solutions across multiple domains.
• Reinforcement learning, a form of machine learning, enables AI systems to learn sequences of actions in complex environments, such as games or real-world robotics, by maximizing cumulative rewards.
• Modern unified ML models, such as Google’s Pathways and transformer architectures, can now tackle tasks in multiple subdomains (vision, language, decision-making) with a single framework.

Data Pipeline and Roles in Data Science

• Data engineering covers obtaining and storing raw data from various data sources (datasets, databases, streams), aggregating into data lakes, and applying schema or permissions.
• Feature engineering cleans and transforms raw data (imputation, feature transformation, selection) for machine learning or analytics.
• Data warehouses store column-oriented, recent slices of data optimized for fast querying and are used by analysts and business intelligence professionals.
• The analytics branch (data analysts, BI professionals) uses cleaned, curated data to generate human insights and reports.
  • Data analysts apply technical and coding skills, while BI professionals often use specialized tools (e.g., Tableau, Power BI).
• The machine learning branch uses feature data to train predictive models, automate decisions, and in some cases, trigger actions (robots, recommender systems).
• The role of a "data scientist" can range from specialist to generalist, depending on team size and industry focus.

Historical Context of Artificial Intelligence

• Early concepts of artificial intelligence appear in Greek mythology (automatons) and Jewish mythology (Golems).
• Ramon Lull in the 13th century and Leonardo da Vinci constructed early automatons.
• Contributions:
  • Thomas Bayes (probability inference, 1700s)
  • George Boole (logical reasoning, binary algebra)
  • Gottlob Frege (propositional logic)
  • Charles Babbage and Ada Byron/Lovelace (Analytical Engine, 1832)
  • Alan Turing (Universal Turing Machine, 1936; foundational ideas on computing and AI)
  • John von Neumann (Universal Computing Machine, 1946)
  • Warren McCulloch, Walter Pitts, Frank Rosenblatt (artificial neurons, perceptron, foundation of connectionist/neural net models)
  • John McCarthy, Marvin Minsky, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon (Dartmouth Workshop, 1956: "AI" coined)
  • Newell and Simon (Heuristics, General Problem Solver)
  • Feigenbaum (expert systems)
  • GOFAI/symbolism (logic- and knowledge-based systems)
• The “AI winter” followed the Lighthill report (1970s) due to overpromising and slow real-world progress.
• AI resurgence in the 1990s was fueled by advances in computation, increased availability of data (the era of "big data"), and improvements in neural network methodologies (notably Geoffrey Hinton's optimization of backpropagation in 2006).
• The 2010s saw dramatic progress, with companies such as DeepMind (acquired by Google in 2014) achieving state-of-the-art results in reinforcement learning and general AI research.
• The Sub-disciplines of AI and other resources:
  • AI on Wikipedia
  • Machine Learning on Wikipedia
  • Data Science on Wikipedia

Further Learning Resources

• Artificial Intelligence (Wikipedia)
• Machine Learning (Wikipedia)
• Data Science (Wikipedia)
• AlphaGo Movie
• AI Sub-disciplines