In conjunction with data science and digital transformation, you have probably heard the terms of artificial intelligence, machine learning, and deep learning is used. You might wonder what the relationship between those topics is. How do companies in industries range from biopharma to chemicals to food & beverage that incorporate AI, machine learning, and data science to enhance their processes? AI and machine learning allow applications such as virtual digital assistants, facial recognition, and self-driving cars, as well as improvements in healthcare diagnostics and process manufacturing. Are you interested in making a career in these? There are many AI certification courses, data science certification courses, and ML certifications available online. Check out!
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- Where They All Overlap
Let’s glance at what these terms mean and how companies use them to make more strategic choices and improve production processes.
Even though the fields of data science vs. machine learning vs. artificial intelligence overlap, their distinct functionalities differ and have individual areas of application. Let’s look at their basics.
Data science is a vast discipline of study related to data systems and processes, intending to maintain data sets and derive meaning from them. It incorporates statistical and mathematical techniques, such as data mining, multivariate data analysis, visualization, computer science, and even machine learning to draw data knowledge and provide insights and pathways for decision making. It is an area that many businesses are using successfully to improve production processes, to enable strategic planning, and to innovate product design. Data analytics is the discipline of raw data analysis in order to draw conclusions about that information.
Machine learning is regarded as an AI sub-set and is often used to implement AI. Instead of writing algorithms explicitly to dictate a computer’s actions, machine learning is employed to “train” the computer to decide the right way to solve a task, given many examples of the correct solution to a particular problem. Once the model is mature enough to deliver reliable and high accuracy results, it can be deployed to a production setup where new problems such as predictions or classification can be solved. A number of different clusterting and regression algorithms are used in ML, such as simple linear regression, polynomial regression, partial least square regression (including OPLS and PLS), supporting decision tree regression, vector regression, K-nearest neighbors, random forest regression, and others. ML is often used to discover patterns (discover hidden patterns in a dataset) and make meaningful predictions. Machine learning can be employed to analyze business trends or make financial predictions, create simulations and safety models, review CT scans and support diagnostics, and solve engineering problems in automotive manufacturing.
Artificial intelligence, which includes machine learning, neural networks, and deep learning, aims to replicate human decision-making and thinking processes. AI is a compilation of mathematical algorithms that allow computers to understand complex relationships, make actionable choices, and plan for the future. AI allows computers to interpret the surrounding environment and to make decisions based on what they observe. Based on new input, AI can enable machines to adjust their “knowledge,” with a machine learning component. AI can be used to improve process manufacturing, process biomedical and clinical data, create “smart” assistants or chatbots, monitor social media, financial planning or investment, and many other areas.
Where They All Overlap
There is overlap within the fields of artificial intelligence, machine learning, and data science, but they are not interchangeable. There are a few nuances in between. Here is an exceptionally clarified explanation of the difference between these three areas:
- Data science brings insights
- Machine learning works out predictions
- Artificial intelligence yields action
All of these fields overlap with the concept of big data. It refers to finding ways to use the large volumes of data that businesses generate – both structured and unstructured – in ways that can provide insights to support better decisions. It’s a concept that incorporates all the other practices but is not itself a specific field. In our attempts to build learning machines, the most straightforward way to describe the relationship and overlap among ML and AI is that ML is one of the current state-of-the-art methods. While AI is a somewhat fluid term used to describe a general concept, ML is an AI methodology, and consequently, an AI sub-set. Conducting ML involves building algorithms that can be trained on data, rather than being explicitly instructed by a human, usually a data scientist on how to perform a task.
While the concept is not new – it was discussed seriously for the first time in the mid-20th century – it relies on access to large volumes of data as well as a lot of computing power. Both are required to train algorithms until they are good enough at their task. And it is just in comparatively recent years that this has become a viable reality for business, thanks to the emergence of the internet and the falling cost of processing hardware.
On the road to digital transformation and Industry 4.0/pharma 4.0, more and more companies will incorporate data science, advanced data analytics, and AI into their development and production processes to improve efficiency, reduce errors, and remain competitive. The applications and tools that make this accessible to enterprises keep growing. Data analytics methods and tools like the Umetrics Suite support methods such as predictive analytics, real-time data analytics monitoring, and digital twins for process control. It is an in-demand field, and you can become a machine learning expert or an artificial intelligence developer.