There’s perhaps nothing that sets the 21st century apart from others more than the concept of data. Every interaction we have with a connected device creates a data record, and beams it back to some data store for tracking and analysis. Internet-connected devices are ubiquitous and growing. In 2018, there were approximately 8 connected devices per person in the United States. That number is expected to grow to 13.6 by 2023.¹
The vast amounts of data that are being collected by organizations and individuals have enabled ever more powerful — and transformational — machine learning algorithms. Machine learning and artificial intelligence (AI) shape our experience when we use a search engine, visit a social media website, or interact with a large company’s customer service. AI enables SpaceX to safely land its rockets back on Earth for reuse. It fuels a growing population of robots in manufacturing, generates novel chemical compositions for drug research, and brings the possibility of fully autonomous vehicles closer every day.
Yes, advances in compute power and better algorithms have also been a critical part of this advancement. But without good data, hardware and mathematical equations can only do so much. “Garbage in, garbage out” as the old adage goes.
Data Science vs. Machine Learning vs. Artificial Intelligence
It’s probably useful at this point to discuss what we mean when we talk about data science, machine learning, and artificial intelligence(AI).
Historically, data science has involved the process of analyzing data to gain insights, typically business insights. As Andrew Ng explains in his Coursera course, AI for Everyone, the output of a data science analysis would typically be a PowerPoint presentation (though this isn’t necessarily the case anymore — more on that in a moment).² Such an output would typically serve key stakeholders in an organization or on a project.
One of its pioneers, Arthur Samuel defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed”. The output of a machine learning project is typically some type of software, for example an algorithm that automatically optimizes listings you see on a job search site based on a variety of factors. Such an output could serve thousands, millions, or even billions of users.
The Evolution of Data Science
Artificial intelligence is the field of study involving how to build intelligent machines, typically with at least human-level performance on a given task (narrow AI) or on a diverse set of tasks (artificial general intelligence — AGI). We don’t know when we will reach AGI, or how we might know when we reach it.³ But in recent years, researchers and practitioners have achieved human-level or better performance on a variety of tasks using a specific type of machine learning called deep learning. Deep learning leverages an artificial neural network architecture, so you might see deep learning, neural networks, and AI used interchangeably in some settings.
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