Artificial Intelligence, Machine Learning, and Data Science Technologies eBOOK FULL 2022

 Artificial Intelligence, Machine Learning, and Data Science Technologies


AI vs. Machine Learning vs. Data Science:

Artificial Intelligence: So... this one is not obvious because it is widely used in different fields and has different meanings. To put it simply: the main objective of artificial intelligence is to bring human intelligence to machines.

The goal of AI is to enable intelligent devices to think and act like humans. In this regard, a machine using AI performs tasks by mimicking human intelligence. For example, machines that can identify products with a defect.

Within the manufacturing, AI can be thought of as the ability of machines to understand/interpret data, learn from data, and make "intelligent" decisions based on information and patterns derived from that data. Often, AI goes beyond what is humanly possible in terms of computing capabilities.

AI is primarily used for the following activities:

Preventive maintenance
Predictive capability
Prescriptive recommendations
Real-time monitoring and adjustment
Distinction of defect recognition models

Data Science: As the name implies, data science is all about discovering insights from data. In fact, everything related to collecting, preparing and analyzing the data you generate for various purposes is data science.

The central aspect of data science is getting new results from data: finding meaning, revealing problems you didn't know existed, and solving complex problems. Data science relies strictly on analytical evidence, works with both structured and unstructured data, and brings about a cultural shift in businesses toward data-driven decisions. 

Today, the availability of huge volumes of data allows for increased revenue generation from data science. With this opportunity to save money, reduce risk, and empower employees with data, Braincube offers a combination of Edge and Cloud solutions with out-of-the-box applications in a fully integrated and interoperable IIoT platform. As a result, anyone can become a Data Scientist citizen and understand contextualized databases to achieve the best production standards through real-time tracking and insights, as well as Big Data analysis.

Data Science in the involves the processes :

data extraction and collection;
data cleansing ;
and the generation of actionable insights... through AI!

Machine Learning: It is the science of training computers to learn and act like humans while improving their autonomous learning over time. This is a subset of AI…often used by data science. Machine Learning uses aspects of statistics and algorithms to work on data from multiple sources.

Instead of writing code, you feed data into a generic algorithm, and then the Machine Learning learns and builds its logic based on that information. In simple terms, with Machine Learning, computers learn to program themselves.

As we have seen in our definitions, data is generated in considerable volumes and this makes their use tedious by data scientists, process engineers or management teams. That’s when Machine Learning comes into play. The Machine Learning model goes into production mode only after having sufficiently tested its reliability and accuracy. 

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