Is AI Good for Data Science?

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Is AI Good for Data Science?

Let's take a look at whether or not Artificial Intelligence is good for Data Science. Also explore what the two terms mean.

· AI Zone ·
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Artificial Intelligence and Data Science continue to be the buzz of the 21st century and with the immense development in both the fields, it is expected to stay for a very long time. While they might seem intimidating to a layman, the truth is, they’re not. Data Science is much correlated with Artificial Intelligence and of course, is driven by data.

What is Artificial Intelligence?

Artificial Intelligence is an attempt to stimulate human consciousness by machines. It involves making machines perform logical reasoning, learn from experiences, and implement that learning in future operations. AI is a wide field and is one of the most complicated and rapidly developing we’re working on. Being a subset of data related technology, it inevitably requires lots of data and computing power to function.

The Artificial Intelligence technology is classified into two categories: General AI and Narrow AI.

1. General AI — As the name might suggest, this branch of AI involves developing machines that can perform a broad range of tasks that involve thinking and decision making at the level of human minds.

2. Narrow AI — This branch of AI involves dealing with a very narrow set of tasks. For instance, if you consider an algorithm built to win a board game as a general AI, then, an algorithm built to make a single move is an example of narrow AI.

Till now, only Narrow AI has been realized as a true “General AI” still remains a distant dream for many scientists and developers.

What Is Data Science?

Data Science is essentially the science of extracting relevant information from data using various techniques that involve statistics, mathematics, data visualization, pattern recognition, data modeling, data engineering, and of course, computer programming. Practitioners of data science are expected to be proficient in mathematics, statistics, and programming to create feasible solutions to problems posed by the industry.

Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms. This implies data science enables AIs to make sense of answers for problems by connecting comparable data for some time later. In a general sense, data science allows AIs to discover relevant and important data from those gigantic pools quicker and all the more productively.

For instance, Facebook's facial recognition framework which, after some time, assembles a ton of information about existing clients and applies similar systems for facial recognition with new users. Another example is Google's self-driving vehicles, which accumulate data from its surroundings continuously and forms those data to settle on logical choices on the street.

Data Science and Artificial Intelligence

Like mentioned earlier, Data Science and Artificial Intelligence are highly correlated and one cannot be implemented without the other. However, it’s the Machine Learning that serves as a link between the two. It would be apt to say Data Science is Artificial Intelligence, which includes Machine Learning.

For example, if a person searches for “best jackets in NY” on Google, then the AI collects this data through Machine Learning.

Now, at the same instant, when the person types “best place to buy” on the search bar, the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most likely suffix to the query that the user had in mind.

Impact of AI

AI and Data Science have already influenced socio-political situations and businesses by changing many industries and displacing jobs.

Anyways, these new technologies will be part of our daily lives, so the best strategy is to be proactive and learn how to control and manage them rather succumb to advancements in them.

artifical intelligence, data science, impact of ai, machine learning

Opinions expressed by DZone contributors are their own.

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