Understanding the Relationship Between Data Science, Artificial Intelligence and Machine Learning
You’ve probably heard the terms artificial intelligence (AI), machine learning (ML) and deep learning (DL) being used in conjunction with digital transformation and data science. You may be wondering what the relationship is between these subjects. How are businesses in industries ranging from biopharma to chemicals to food & beverage incorporating AI, machine learning and data science to improve their processes? Let’s take a look at what these terms mean and how businesses are using them to make more strategic decisions and improve production processes.
AI and machine learning enable applications such as virtual digital assistants, facial recognition and self-driving cars, as well as applications in healthcare diagnostics and manufacturing production process improvements.
The fields of artificial intelligence (AI), machine learning (ML) and data science have a great deal of overlap, but they are not interchangeable. There are some nuances between them. Here is a very simplified explanation of how these three areas differ:
- Data science produces insights
- Machine learning produces predictions
- Artificial intelligence produces actions
The concept of big data overlaps all of these fields. It refers to finding ways of using the large volumes of data– both structured and unstructured– that businesses generate in ways that can provide insights to support better decisions. It’s a concept that incorporates all of the other practices, but isn’t a specific field itself.
Data science is a field that incorporates some areas of AI, machine learning and deep learning, while having a specific focus of gaining insight from data.
What Is Data Science?
Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. It incorporates techniques of statistics and mathematics, such data mining, multivariate data analysis and visualization, along with computer science and even machine learning to draw knowledge from data and provide both insights and decision paths. It is an area that is being used successfully by many businesses to improve production processes, enable strategic planning and innovate product design.
Data scientists are analytical data experts who have the technical skills to uncover data trends, as well as specific domain knowledge for their industry that helps them solve complex business problems. Many data scientists start out as mathematicians, statisticians or data analysts, but may evolve into roles that incorporate big data, artificial intelligence or process technologies. A good data scientists understands his or her problem domain very well, what questions to answer, and peculiarities of the data associated with it.
Data analytics is the discipline of analyzing raw data in order to make conclusions about that information. Data analytics is a broad term that encompasses a number of diverse techniques to get insights that can be used to optimize processes or to increase the overall efficiency of a business or system.
Data analytics techniques can reveal trends and metrics that would otherwise be lost in a mass of information.
Data Science Produces Insights
One way data science is different from AI and ML is that a human is involved. A person is using data analytics to gain insight and understanding and forming conclusions to make decisions.
Data scientists deal with huge chunks of data to analyze the patterns, trends and more. Using data analytics, data scientists can generate reports that are used to draw inferences. Data analytics tools and software are useful in this process and can be used to make predictions based on patterns.
Two areas that fall under this field and are being implemented in many industries ranging from pharma to chemicals to energy to food and beverage are predictive analytics and real-time analytics.
- Predictive analytics: These are models that predict the possibilities of a particular event happening in the future.
- Real-time analytics: Data analytics can also be used to detect deviations in a process by modeling against historical parameters in real-time. This is also a type of machine-learning.
What Is Artificial Intelligence?
Artificial intelligence, which encompasses machine learning, neural networks and deep learning, aims to replicate human decision and thought processes. Basically, AI is a collection of mathematical algorithms that make computers understand complex relationships, make actionable decisions, and plan for the future.
AI enables computers to interpret the environment around them and make decisions based on what they observe. With a machine learning component, AI can enable machines to adjust their “knowledge” based on new input.
AI can be used for manufacturing process improvement, processing biomedical and clinical data, creating “smart” assistants or chat bots, social media monitoring, financial planning or investing, and many other areas.
Machine learning (ML) is considered a sub-set of AI and is often used to implement AI. Instead of explicitly writing algorithms to dictate a computer’s actions, machine learning is used to "train" the computer to find the right way of solving a task given many examples of the correct solution to a given problem. Once the model is mature enough to give reliable and high accuracy results, it can be deployed to a production setup where it can be used to solve new problems such as predictions or classification.
A number of different regression and clustering algorithms are used in ML, such as simple linear regression, polynomial regression, partial least squares regression (including OPLS and PLS) support vector regression, decision tree regression, random forest regression, K-nearest neighbors, and others. ML is often used for pattern discovery (to find hidden patterns in a dataset) and to make meaningful predictions.
Machine learning can be used to analyze business trends or make financial predictions, create simulations and safety models, review CT scans and support diagnosis, and solve engineering problems in auto manufacturing, to name a few examples.
Deep learning (DL) is an advancement of ML using a specific type of algorithm called deep neural network models. DL excels at learning how to represent unstructured data, such as images, text, protein structures, genome sequences, etc, in a way that is useful for prediction. It can be used when the training datasets are very large, and the relationships to learn are very complex, such as in medical science or with self-driving cars. Most virtual assistants, such as Alexa and Siri, use deep learning to understand requests (using Natural Language Processing, NLP), and social networks use DL to analyze the contents of all images you upload (using Computer Vision, CV).
Data science is a comprehensive process that involves multiple steps for analyzing data and generating insights. It revolves around the idea of building models that use statistical insights to find hidden patterns and make predictions.
Artificial intelligence makes use of computer algorithms to impart autonomy to the data model and emulate human cognition and understanding.
Machine learning and deep learning are subsets and advanced applications of AI that use vast datasets to train computers how to draw conclusions.
On the road to digital transformation and industry 4.0/pharma 4.0, more and more companies will be incorporating data science, advanced data analytics and AI into their development and production processes to improve efficiency, reduce errors and stay competitive.The tools and applications that make this accessible to businesses continue to grow. Methods such as predictive analytics, real-time data analytics monitoring and digital twins for process control are supported by data analytics methods and tools such as the Umetrics Suite.