Artificial Intelligence and Data Science – What’s the difference?
Artificial intelligence is everywhere. Advances in AI have benefited nearly every commercial industry – but there is as much (or more!) hype around AI as there is about real AI. In the midst of this are terms – AI, data science, machine learning, deep learning, etc. – that add to the confusion. In this post, I offer perspective on two of these terms – artificial intelligence and data science, and what they mean (in general) in relation to each other.
What is artificial intelligence?
Artificial intelligence (AI) is an umbrella term for any technology where a computer program attempts tasks that come naturally to the human brain. Skills such as understanding written language, detecting speech, recognizing objects from images, and making plans to improve time are all examples of the intelligence that humans display every day. Most of it is learned by our brains naturally as we grow and interact with the world around us, and then refined and developed through formal learning.
These tasks come naturally to humans but are very challenging for computers. Computer algorithms (methods of structuring programs) that can learn and perform these tasks are usually categorized as AI.
What is data science?
In the same way that AI is an umbrella term for intelligence, Data Science is an umbrella term for insights from data. Data science is a set of methods and practices for gathering ideas (information, learning, etc.) from data. The data can be anything (stock prices, audio recordings, sensor data from rainfall meters, satellite images, etc.). Data science can include processing data, performing statistical analysis of data, presenting data in ways that others can understand (called data narration), etc. Sometimes these analyzes are simple (eg average rainfall). Sometimes it’s a lot more complicated. But it’s all about data science.
Does AI need data science?
Often times, yes. Before a computer program attempts to learn from the data, it is often useful for a human (or data analysis program) to study the data. Data scientists often clean up the data, extract the important stuff, and power it up with AI to learn more from it. This intervention often helps AI systems learn better because AI can focus on selected subsets of data, thus improving the learning process.
However, today’s most advanced AI systems are able to scrutinize large data volumes that have undergone minimal or no data pre-processing. There are also automated programs that can help clean up and preprocess/select data for AI. As such, some advanced AI systems do not necessarily require classical data science.
Does data science need artificial intelligence?
sometimes. Data science can be used on its own to understand, interpret, and communicate insights about data. For example, if rainfall data is analyzed to see if average rainfall shows an increasing or decreasing trend, this can be done through statistical analysis that does not require advanced artificial intelligence. However, it is possible to use AI to learn insights from unseen data using simple data science techniques. This is especially true with rich data types (such as video), or when data volumes are particularly large.
Which is better?
Sometimes these two terms seem to be opposing or competing. This is not the case. The field of data and machine intelligence is broad and includes everything from understanding data to helping computers learn from data and automatically solve problems using what they have learned. Both data science and artificial intelligence are critical to businesses and have a complementary relationship. In the future, we can expect a smooth relationship between the two – and no need to choose one over the other.
Check out these additional resources if you’re new to AI and want to learn more, or new to data science and want to learn more.
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