4  Machine Learning, Artificial Intelligence and Data Science

The hype is real. Words like Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) are frequently thrown around. This short chapter will explore the relations between these different concepts.

4.1 Artificial Intelligence

Artificial Intelligence is the design of computational (i.e., non-human) systems that can perform tasks typically requiring human intelligence (see Russell and Norvig 2021, 1–4). These tasks include learning, reasoning, problem-solving and planning, among others. Anything that we commonly associate with our own cognitive capabilities. AI is concerned both with the understanding of intelligence and the building of intelligent systems.

The reason the new Generative AI models like ChatGPT or Claude feel so “AI” is that they replicate aspects of human intelligence. They can generate helpful answers to user queries, in a human-like way.

4.2 Machine Learning

Machine Learning (ML) is a subset of Artificial Intelligence focused on designing algorithms that learn from data. These Machine Learning algorithms, also called “models”, can generalise the rules learnt on training data to new, unseen data points. As opposed to rule-based systems, ML algorithms are not explicitly programmed; they learn rules from the data. The next chapter will explore Machine Learning in more detail.

4.3 Deep Learning

Deep Learning is a subset of Machine Learning concerned with the development of multi-layer Neural Networks to perform tasks like classification and regression. These Neural Networks are (roughly) inspired by the workings of the human brain. Since the 1990s they have pushed the state of the art in Machine Learning research and are the backbone of the current AI revolution. As these models are more complex, they will not be covered in this book.

4.4 Data Science

Machine Learning is closely related to Data Science. For a few years (my first years on the job market), Data Scientist was the sexiest job of the 21st century (Davenport and Patil 2012).

Data Science is the extraction of generalisable knowledge from data. This is not knowledge at a given point in time, but generalisable knowledge. Knowledge that can be used on unseen data to predict the future.

Whereas Data Analysis is the study of what happened, Data Science focusses on predicting future trends. Looking at the case of property pricing, Data Analysts would create reports and analyse what happened. On the other hand, Data Scientists would build models to predict the price of new properties.

Beyond the hype, Data Science is a field concerned with extracting and extrapolating knowledge from data (Dhar 2013).

• Extract: to obtain something from something else, to get information from data.
• Extrapolate: to predict by projecting past experience or known data (Merriam-Webster Dictionary 2024).

To summarise, Data Science uses data to extract useful knowledge from data to inform the future.

4.5 Final Thoughts

This chapter defined some important concepts:

• Artificial Intelligence (AI): design of intelligent systems, emulation of human cognition
• Machine Learning (ML): algorithms that learn from data
• Deep Learning: branch of ML focused on Neural Network modelling
• Data Science (DS): extraction of generalisable knowledge from data

Now that these definitions are out of the way, the next chapters will show how Machine Learning models work. The next chapter will explore the role of data in Machine Learning.

Davenport, Thomas H., and D. J. Patil. 2012. “Data Scientist: The Sexiest Job of the 21st Century.” Harvard Business Review 90 (10): 70–76. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century.

Dhar, Vasant. 2013. “Data Science and Prediction.” Communications of the ACM 56 (12): 64–73. https://doi.org/10.1145/2500499.

Merriam-Webster Dictionary. 2024. “Extrapolate.” Merriam-Webster. 2024. https://www.merriam-webster.com/dictionary/extrapolate.

Russell, Stuart J., and Peter Norvig. 2021. Artificial Intelligence: A Modern Approach. 4th ed. Hoboken: Pearson.