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Terminology and Related Concepts

 


        

        Terminology and Related Concepts


Let's differentiate some of the closely related terms and concepts of AI: 


  • Artificial intelligence.
  • Machine learning.
  • Deep learning.
  • Neural networks. 


These terms are sometimes used interchangeably, but they do not refer to the same thing. 


Artificial intelligence is a branch of computer science dealing with a simulation of intelligent behavior. AI systems will typically demonstrate behaviors associated with human intelligence such as planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation, and to a lesser extent social intelligence and creativity. 


Machine learning is a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned, without being explicitly programmed. Machine learning algorithms are trained with large sets of data, and they learn from examples. They do not follow rules-based algorithms. 


Machine learning is what enables machines to solve problems on their own and make accurate predictions using the provided data. Deep learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making. 


Deep learning algorithms can label and categorize information and identify patterns. It is what enables AI systems to continuously learn on the job and improve the quality and accuracy of results by determining whether decisions were correct. Artificial neural networks often referred to simply as neural networks take inspiration from biological neural networks, although they work quite a bit differently. 


A neural network in AI is a collection of small computing units called neurons that take incoming data and learn to make decisions over time. Neural networks are often layered deep and are the reason deep learning algorithms become more efficient as the datasets increase in volume, as opposed to other machine learning algorithms that may plateau as data increases. Now that you have a broad understanding of the differences between some key AI concepts, there is one more differentiation that is important to understand, that between artificial intelligence and data science. 


Data science is the process and method for extracting knowledge and insights from large volumes of disparate data. It's an interdisciplinary field involving mathematics, statistical analysis, data visualization, machine learning, and more. It's what makes it possible for us to appropriate information, see patterns, find meaning from large volumes of data, and use it to make decisions that drive business. Data Science can use many of the AI techniques to derive insight from data. 


For example, it could use machine learning algorithms and even deep learning models to extract meaning and draw inferences from data. There is some intersection between AI and data science, but one is not a subset of the other. Rather, data science is a broad term that encompasses the entire data processing methodology. Well, AI includes everything that allows computers to learn how to solve problems and make intelligent decisions. Both AI and Data Science can involve the use of big data that is significantly large volumes of data. 



Avinash C. Pillai

Technology Director

syniverse® 

The world’s most connected company™ 

Website / Twitter / LinkedIn/ connected company™  


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