Data science, machine learning, and AI are everywhere. We read about them in the news, see mentions on social networks, take courses, and watch edu videos on YouTube.
Now, what do they stand for? What’s the difference between these three terms? We could say they are interconnected, but they don’t share the same meaning.
In this beginner’s guide, we will look at the primary difference between data science, AI, and ML. As well as seeing their uses together.
Data science stands for working with large amounts of unstructured information (otherwise known as big data). Meteorological data or statistics of search queries in Google are examples of this. The key takeaway words here are ‘huge volume’ and ‘unstructuredness.’
In fact, anything surrounding data selection, preparation, and analysis relates to data science. At the core of data science is big data—raw information stored in enterprise data warehouses.
Data Scientists use mathematical statistics and machine learning methods to work with tons of information.
What is data science used for? It’s used in weather forecasts, chatbots and voice assistants like Siri or Alexa. Algorithms that recommend music and video on Netflix, Spotify, or Youtube. Recommended friends on Facebook are also the result of data science. Search engines, face recognition programs—they are all based on algorithms created by Data Scientists!
A Data Scientist understands data insights and sees the figures. Their job is to analyze big data to make predictions. What predictions? Depends on the problem the scientist needs to solve.
The result of their work is a predictive model—a software algorithm that finds the best solution to the problem.
Artificial intelligence is a system or a machine that can mimic human behavior. AI systems show at least some of the following behaviors:
AI is not a format or a function; it’s the ability to think and analyze data.
Artificial intelligence is not intended to replace humans with smart robots. Its goal is actually to expand human skills and capabilities.
For example:
Amazon used AI to create self-driving cars and robots they now use for delivery.
Machine learning is one of AI’s branches. At a high level, ML is about teaching a computer how to make accurate predictions when it is fed with data.
For example, such a system could detect whether an apricot or an apple is in a picture. It can spot people that cross the road in front of a self-driving vehicle. ML can also distinguish regular emails from spam. It can even recognize speech to provide captions on YouTube.
Netflix takes advantage of predictive analytics to improve recommendations to its users. ML-powered algorithms analyze users’ preferences and ‘understand’ which movies they love most.
Let’s sum up the differences.
Data science is not limited to algorithms or statistical aspects; it covers the whole spectrum of data processing. Besides, Data Scientists use AI to interpret the past, present and future.
Artificial intelligence works with models that make machines act like humans. The computer system, in one way or another, imitates human behavior.
AI works on automating business processes and encouraging machines to work like humans. It is mostly associated with human-AI interaction gadgets – Google Home, Siri, Alexa. While recommendation systems used by popular services like Netflix, Spotify, YouTube are ML-powered.
Machine learning is a subfield of AI, which enables a computer system to learn from data. ML algorithms depend on data as they train on information delivered by data science. Without data science, machine learning algorithms won’t work as they train on datasets. No data means no training.
That’s how AI, ML, and data science are interconnected. Let’s see how they could work together on an example of a self-driving car.
Machine learning. The car must recognize stop signs using cameras. We create a dataset with millions of images of streetside objects. We then train the machine learning algorithm to identify the images with stop signs.
AI. After recognizing the sign, the AI enabled car must apply the brakes—right on time, not too early nor too late.
Data science. We run tests and see that in some cases the car doesn’t apply brakes when it should. Once the test data is analyzed we see that there are more failed tests in the night than in the daytime. We add more nighttime images with stop signs to the dataset and get back to running tests.
You can’t use data science and not use machine learning. Machines can’t learn without data, and data science works best with ML.
It’s the same with AI and ML. We cannot use machine learning alone for self-learning or adaptive systems, whilst refusing to use AI. Artificial intelligence represents devices that show/mimic human-like intelligence. Machine learning allows algorithms to learn from data.
Vitaly Kuprenko is a writer for Cleveroad. It’s a web and mobile app development company with headquarters in Ukraine. He enjoys writing about technology and digital marketing.
