Data Science and AI (Artificial Intelligence) are the two most important technologies in the world today. Although Data Science uses Artificial Intelligence in its operations, it certainly does not imply AI. This article will give an overview of the concept of Data Science and AI.
Thereby, the article will provide a preliminary understanding of how researchers around the world are developing modern AI. Data Science and AI (Artificial Intelligence) are often confused concepts. While Data Science can contribute to some aspects of AI, it does not reflect all of AI.
While Data Science has gained global popularity, Artificial Intelligence is still quite vague to many people, even confusing the concept with Data Science. To correctly distinguish these two concepts of technology, please refer to the article!
What is the definition of AI (Artificial Intelligence)?
AI (Artificial Intelligence) is the "intelligence", or the ability to programmatically think of a block of machines. It is modeled after the thinking model of humans or animals in general.
Artificial intelligence turns algorithms into action sequences in a real environment manipulated by machines. Intentional manipulations are repeated by machines with precision and a high success rate.
Many traditional AI algorithms are used for clear and simple purposes such as pathfinding algorithms like A*. With the development and high requirements of technology, modern AI algorithms such as Deep Learning (one of the techniques of Machine Learning) and Word Embedding (a group of special techniques in natural language processing) born to serve more complex requirements.
In addition, AI is also used for some key software engineering to become solutions to current manufacturing problems.
Recently, many tech giants like Google, Amazon and Netflix, Facebook, are using advanced AI (Artificial Intelligence) to develop their automation systems. The most famous example is Google's AlphaGo. This AI Go program defeated Ke Jie, a world No. 1 in chess. AlphaGo used an Artificial Neural Network modeled to learn information over time and perform actions.
How is Data Science and AI (Artificial Intelligence) different?
There are a few basic differences between Data Science and AI
1. Upper bound of AI
Data Science and AI are often confused, however, you can completely distinguish these two concepts through the must-have elements in AI. The contemporary AI used in the world today is “Narrow AI”.
By recognizing images, classifying on the data system, the computer system has certain control rights, but it is not like a fully human conscious mechanism.
Instead, the machine just performs the programmed action. For example, AlphaGo can beat the world No. 1 Go champion but it has absolutely no strategy to win, it is simply programmed to play this Go game.
2. Data Science is a comprehensive concept
Data Science is the analysis and study of data. A data scientist is responsible for making decisions that benefit companies. Furthermore, the role of the data scientist varies by industry.
In the day-to-day tasks and responsibilities of a data scientist, the primary requirement is data preprocessing, that is, performing data filtering and transformation.
He/she then analyzes patterns in the data and uses visualization techniques to draw graphs that underline the analytical processes. He/she then developed models that predict the likelihood of future events.
3. AI is one of the tools for Data Scientist
For a Data Scientist, AI is a tool or method of primary data analysis to achieve desired results. Applying Maslow's Hierarchy of Needs model, each part of the pyramid represents a data activity performed by the Data Scientist.
Source Image: Maslow's Hierarchy of Needs model
Each business will evaluate the importance, as well as the proportion of using Data Science and AI differently. For example, some companies require pure AI specialist positions such as Deep Learning Scientist, Machine Learning Engineer, NLP Scientist, etc. to serve the business process.
In these positions, employers are required to use Data Science tools like R and Python that are used to perform various data-related operations but also require expertise in computer science.
On the other hand, a Data Scientist will help businesses make decisions based on available data. They are responsible for extracting data using SQL and NoSQL queries, resolving various anomalies in the data, analyzing patterns in the data, and applying predictive models to generate future insights. future.
Furthermore, based on the requirements, Data Scientist also uses AI tools like Deep Learning algorithms to perform accurate classification and prediction using the data.
- Data Science is an overarching process from preprocessing, analysis, visualization and prediction stages. On the other hand, AI requires the application of a predictive model to predict the future.
- Data Science includes various statistical techniques while AI uses computer algorithms.
- The tools are more related to Data Science than AI. The reason is that Data Science requires more step-by-step data analysis.
- Data Science is about finding aggregates within data. AI is about imparting autonomy to the data model.
- With Data Science, we will build models that exploit information through statistics. On the other hand, AI was born on the basis of simulating human perception and thinking.
- Data Science does not refer to the high level of processing and response to information compared to AI.
In short, AI (Artificial Intelligence) is a vast land containing many things to explore. Meanwhile, Data Science is a field that uses AI to generate predictions but also focuses on transforming data for analysis and visualization.
While Data Science is about doing data analysis, AI is still an essential tool to create better products and communicate them by automated coded manipulation.
The source of this information is summerized from Data Flair and Data Camp.