Modern technologies like (AI) artificial intelligence, machine learning, data science,... have become the buzzwords that everybody talks about, but so many people can not understand. Sometimes these terms are even used interchangeably. So in this article, we will explain these tech skills in simple words that easily understand the difference between them and how they are being used in business.
1. First, what does Artificial Intelligence mean?
Artificial intelligence refers to the simulation of a human brain function powered by machines. This is achieved by creating an artificial neural network that can simulate human intelligence. The primary human functions that an AI machine performs in recent times are logical reasoning, learning, and self-correction. AI has lots of potential in many applications, but it also one of the most complicated technologies to work on. Machines inherently are not smart to make them function as a part of the human brain, we need a lot of computing power and data.
And Artificial intelligence is typically classified into two roles which are general Artificial Intelligence and Narrow Artificial Intelligence. General AI refers to making machines intelligent in a wide array of activities that involve thinking and reasoning task. On the other hand, Narrow AI involves the use of artificial intelligence for a very specific tech skill task like virtual assistance, improve image processing.
2. What is Data Science?
In simple words, data science is the tech skill extraction of relevant insights from sets of data. It uses so many techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization, pattern recognition and learning, uncertainty modeling, data warehousing, and cloud computing to extract the final data that fit the requirements.
Data science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.
For instance, Netflix uses its data mines to look for viewing patterns. This allows staff to understand users’ interests better and make decisions on what Netflix series they should make next.
3. Finally, what is machine learning?
Machine learning is one part of artificial intelligence. Machine Learning (ML) is the ability of a computer system to learn from the environment and improve itself from the experience it gathered without the need for any explicit programming. Machine learning focuses on enabling algorithms to learn from the data they collected, gather insights, and make predictions on previously unanalyzed data using the information they already have. Machine learning can be performed using multiple approaches. The three basic models of machine learning are supervised, unsupervised, and reinforcement learning.
In the case of supervised learning, labeled data is used to help machines recognize characteristics and it can be used in the future. For instance, if you want to classify pictures of cellphones and tablets then you can feed the data of a few labeled pictures, and then the machine will classify all the remaining pictures for you.
On the other hand, in unsupervised learning, we simply put raw data and let machines find the different characteristics and then classify them.
Finally, reinforcement machine learning algorithms interact with the environment by producing actions and then analyze errors or rewards. For example, to understand a game of chess a machine learning algorithm will not analyze individual moves but will study the entire game.
4. So, what’s the difference between them?
AI is a very wide term with applications ranging from those complex things like robot automation to simple things like text analysis. But it is still a technology under evolution and there are arguments of whether we should be aiming for high-level AI or not because they’re some risk of security. Machine learning is a subset of AI that focuses on a narrow range of activities. It is, in fact, the only real artificial intelligence that has some applications for real-world problems.
And data science isn’t exactly a part of machine learning but it uses it to analyze data and make predictions. As a data scientist, you will need to combines machine learning experience with other disciplines like big data analytics and cloud computing. The big difference between this and AI is data science is a practical application of machine learning with a complete focus on solving real-world problems.
All these technologies are making a huge difference in our lives every day and evolving fast by a magnitude of people learning and working to improve them consistently. These technologies in the future will help companies to make huge cost savings by eliminating human workers from these tasks and allowing them to move to more urgent ones.
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