Do You Know the Difference Between Data Analytics and AI Machine Learning?
Artificial intelligence is nothing new. It has been with us for years now, albeit not in the form of life like androids we visualize in the movies, but rather like the IBM chess game program that beat Garry Kasparov in the 1990s, Apple’s chatbot Siri, or the robot vacuum in your house.
To state simply, artificial intelligence is the process of assimilating human intelligence to machines. The “intelligent” behavior seen in machines using programmed algorithms or a set of designated rules that solve problems or complete tasks is known as artificial intelligence.
There are two types of AI: Weak or Narrow AI and Strong AI. Narrow AI deals with one focused task, for example, categorizing images or simple computer games with programmed rules. Strong AI’s purpose is to develop machines that exhibit behavior to a level functionally equal to that of a human, for example, speech recognition, self-awareness and decision making.
Machine learning is an aspect of AI that allows machines to learn, test, and make assumptions using input data. In effect, machines process and provide results on the basis of data patterns without human intervention.
Data analytics is the process of scrutinizing a raw set of data to find trends and answer questions to understand why things happened in the past or what will happen in the future. The goal here is to collect and combine data in order to come up with a result, identify patterns and find relationships between variables.
Data analytics are used in business to predict and measure trends and in science to prove or disprove hypotheses. The information gathered from data analytics applications is used to improve business systems and efficiencies.
What differentiates data analytics from AI machine learning is human intervention which is necessary in the first but not present in the second.
- AI makes it possible for machines to process inputs and perform tasks usually performed by humans.
- Data Analytics is the science of analyzing raw data in order to make conclusions about that information to understand the past and improve systems and efficiencies in the future.
Jaclyn-Mae Floro, BCompSc
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Disclaimer. The material in this post represents general information only and should not be taken to be legal advice.