Authored by Sri Divya Muktha K & Team
Explore what goes behind the scenes of AI and Data Science
The era of collecting data, storing, and analyzing through spreadsheets has gone far behind. Today huge and diversified amounts of data are collected through various social media like Twitter, Facebook, and mainly through online shopping sites. Collection of this type of data and analyzing it is not possible through traditional methods like Data Mining, SQL Querying, etc. Hence there is a need for the application of new algorithms to make sense of this increasingly huge quantity of data flowing in real-time. The ability to perform real-time analysis of data for providing insights on trends in market, demand, and supply chain and the targeted audience is essential. Data Science and Artificial Intelligence are buzz words of this generation that are accepted or will be adopted soon by most of the enterprises. Artificial Intelligence is a domain of study which makes machines intelligent and enables decision-making based on the data provided, while Data Science helps to generate meaningful insights from data.
Extracting evocative insights from diversified data is the study of Data Science. This arena involves a wide variety of methodologies, technologies, and algorithms. It demands scalable hardware, technical skills, and efficient algorithms to find solutions to problems through data analysis. The evolution and revolution of technology to move towards data science is depicted in Figure 1.
This emerging methodology of Data Science is based on seven different elements. These include
Figure 2 helps to understand about the amalgamation of all these elements of data science.
Key skills to be a Data Scientist:
Knowledge and skills for working in Data Science domain needs the following tools
Data Science Applications Lifecycle & Examples
Like any other applications or domains, Data Science design also must follow the following phases of its life cycle which is depicted in Figure 3.
Data Science is a multi-faceted domain of study. At present Data Science is used in applications like
Data Science Limitations & Challenges:
Challenges while working with Data Science include
The preparation of intelligent machines is the study of artificial Intelligence. Intelligent machines mean machines that behave similarly to humans, react like humans, learn like humans, give reasoning like humans, and finally solve problems like humans. All this speaks about a “Machine with human skills and power”. Learning AI means knowing what is intelligence and how it is composed, so as to do all brain-related activities together at the same point in time. Making machines behave and work like humans need knowledge of the following domains and disciplines of study which is shown using Figure 4. Domains include
Applications of Artificial Intelligence:
AI is an interdisciplinary study to create a machine with human power. Creation of such a machine helps in
Types of Artificial Intelligence:
Major contributions of Artificial Intelligence to the world include the Replication of humans, solving problems using knowledge intensively, connecting to various sources, and performing actions cleverly. This system contains various agents and environments in which these agents work. The Agent of Artificial Intelligent system works to observe its environment with the help of sensors and acts through effectors. These agents can be classified as Human-agent, Robotic agents and software agents.
Artificial Intelligence can be broadly classified into three levels
At present research and work of Artificial Intelligence are at level 1 called narrow Artificial Intelligence. The subfields of Artificial Intelligence include Machine learning and deep learning which is depicted in Figure 5.
In Practice Artificial Intelligence can be applied in the following applications like
Artificial Intelligence and Data Science
Artificial Intelligence will help in understanding data and helps in identifying relationships in data. The ability of Artificial Intelligence systems to learn by fetching patterns from existing data is called Machine Learning. Machine learning is a subset of Artificial Intelligence which is based on a concept that says that machines will help in accessing data and even have the capability to analyze and learn from data. Data Science and Machine learning work together for the same cause. Data Science helps to estimate the data for Machine Learning. As said above Machine Learning is a subset of Artificial Intelligence. Data Science is an interdisciplinary domain to fetch knowledge from data. Figure 6 depicts the convergence of these three domains of study.
Using Figure 6. We can say that Artificial Intelligence is a discipline that can be combined with Data Science and new applications can be created. For example in the domain of Agriculture, Artificial Intelligence and Data Science can together assist the farmers with crop disease detection and pest management. Traditionally farmers get into losses due to invasion of pests like locust etc, To counter the damages due to these pests, farmers use pesticides. This has to be done at the right time, otherwise, the efforts go in vain, while overuse of pesticides can be counterproductive. This is where Artificial Intelligence and Data Science can come together to provide a solution. While Data Science can be used to define when and how much pesticide to be used, usage of drones can be employed to take images of the crop to correlate with the change in color of the leaves. Based on the change in color, one can diagnose the state of damage to the crop, and recommended solutions can be generated using AI.
Data is like food for today’s and tomorrow’s world. With the use of the right tools, technologies, and algorithms the raw data can be converted to meaningful and distinctive business development. These new upcoming domains help in building better and fastest decision-making systems. Data Science domain helps the Artificial Intelligence domain to find appropriate and meaningful information from the input data in an efficient manner. Taking this meaningful data from the Data Science, the Artificial Intelligence domain makes decisions in real-time. These two domains of the study help to automate many applications, by providing accurate solutions at the point of need. Scientists who work with data are like backbones to most big companies which are by nature data-intensive. In the near future, engineers and technologists who can work efficiently with the Data Science, Machine Learning, and Artificial Intelligence domain will notice numerous demanding opportunities. The actual challenge for these domains is Machine Learning - where we have petabytes of data generated every single day, it is essential to capture the right data while building data models using data science. These data models act as pillars of AI implementation in any field.