Artificial Intelligence (AI) has swiftly become a driving force across all aspects of human life. This captivating field combines various disciplines to create marvels of intelligent machines, capable of performing tasks that were once exclusive to human intelligence. Unlocking the power of AI relies on harnessing the potential hidden within vast datasets through the remarkable realm of data science (DS). While the buzz around DS is palpable, pursuing a career in this field demands an unwavering commitment to perpetual learning and adapting to cutting-edge technologies and techniques. So, let’s delve into the captivating world of AI and unravel the key terms that pave the way to AI enlightenment!
The first question to answer for a newbie here in this field is:
What is AI?
The huge scientific literature is available with most authentic definitions. But for a layman it’s simply providing human-like intelligence to a machine to simplify day to day tasks. Few Examples are Self-driving Cars, Chatbots(SIRI,CORTANA,ALEXA), Humanoid robots(SOPHIA) and many more.
Now the question arises
How it works?
An important parameter without which AI won’t exist is the DATA. To deal with data there is a field called DATA SCIENCE. Huge amount of data is generated every second of time. Example in everyday life is suppose we are downloading an image and uploading it somewhere or sending it to somebody is nothing but a data is created, collected and generated. Now here the data is the information we are using to design AI based applications. This data can be of various types depending on the specifications (Examples: text, images, time series data,categorical data, numerical data, etc). These data are generated and then fed to the machines to process using machine models and resultant is an AI based solution. So, AI is providing solutions to real world problems. As it is interpreted that AI comprises more subfields. So next is:
What are the necessary fields for AI?
Please use here diagram from MS Word file
As the above Venn diagram shows how the vast net of AI has subsets of Machine Learning (ML) then ML has a subset called Deep Learning (DL). Data science (DS) uses all three fields to work i.e.AI, ML and DL.
Machine Learning: It is the process or way of developing a computer-based system that is capable of learning and developing by using a certain set of Algorithms and drawing inferences from the patterns of available application specific data. In order to forecast new output values, machine learning algorithms use historical data as input. It facilitates the creation of new goods and provides businesses with a picture of trends in consumer behaviour and corporate operational patterns. A significant portion of the operations of many of today’s top businesses, like Facebook, Google, and Uber, revolve around machine learning.
Deep Learning: Deep learning is a machine learning method that instructs computers to learn by doing what comes naturally to people. Driverless cars use deep learning as a vital technology to recognise stop signs and tell a pedestrian from a lamppost apart. A specific type of machine learning that allows AI to learn and improve by processing data. Deep Learning uses artificial neural networks which mimic biological neural networks in the human brain to process information, find connections between the data, and come up with inferences, or results based on positive and negative reinforcement.
Data Science: Data methods, scientific analysis, and statistics are all topics covered under the subfield of AI known as “data science,” which is used to extract insight and meaning from data. Data science works with enormous amounts of data using cutting-edge tools and methods to uncover hidden patterns, glean valuable information, and make business decisions. Finance organisations, for instance, can evaluate a customer’s creditworthiness and loan risk using their banking and bill-paying history.
There are two broad Areas in AI:
- Natural Language Processing: A crucial step in the AI process, natural language processing enables computers to recognise, examine, comprehend, and really grasp spoken and written human language. Any AI-driven system that interacts with humans in some manner, whether through text or spoken inputs, needs to have a strong understanding of natural language processing.
- Computer vision: Reviewing and interpreting the content of a picture using pattern recognition and deep learning is one of the many applications of AI technologies. Like the captchas you’ll find all over the web that learn by asking humans to help them identify automobiles, crosswalks, bicycles, mountains, etc., computer vision enables AI systems to recognise components of visual input.
FUTURE OPPORTUNITIES IN AI & DS
From the data explosion to the expansion of the internet of things (IoT) and social media, the future of data science is predicted to see some of the biggest advances seen in the previous ten years. According to experts, the rise of machines will cause computer usage and mobile device utility to increase over the course of the next ten years.
In addition, analysts predict that as people consume enormous volumes of online data, social media use will significantly rise. Social media will be used by consumers for entertainment, business, surveillance, etc. According to some analysts, machine learning algorithms will also experience a sharp increase.
Artificial intelligence is proving to be a stand-in for the human brain. It carries out a number of commercial tasks without the need for human intervention, including consumer interaction and raising brand awareness on social media. Many researchers think AI will surpass humans in nearly all cognitive tasks. By automating tasks like managing employee or patient records, doing market research, and engaging with potential clients, among others, artificial intelligence applications are revolutionising the healthcare, insurance, finance, and marketing sectors.
After knowing the potential of AI and DS let’s explore what are the career opportunities and how to build a successful career in data science and AI systems.
TRENDING CAREER ROLES AVAILABLE IN THE FIELD OF AI&DS
- Data analyst
- Data engineer
- Business intelligence analyst
- Marketing analyst
- Big data engineer
- Machine learning engineer
- Research scientist
- AI data analyst
- AI engineer
- Data Scientist
HOW TO AVAIL THESE CAREER OPPORTUNITIES
Businesses are mining a wealth of data and turning it into useful information. They have data scientists working for them. With each passing day, there is a greater and greater need for a professional and skilled data scientist.
AI&DS is an interdisciplinary area and can be opted by any enthusiastic and passionate individual as a career option. It has been observed that non tech people are also switching into the career of AI & DS by having some diploma courses after bachelor’s degree.
Here are the following PRIMARY STEPS to dive in the field of AI&DS:
- Take a bachelor’s degree or a master’s degree in the field of AI&DS or Diploma Courses.
- Master any one of programming languages for coding Python,R,SCALA.
- Master technical tools like DBMS,SQL, Data Analytics tools like TABLEAU, APACHE, SPARK, SAS.
- Taking up the good internship opportunity after honing the above skills.
- Participate in Coding Hackathons available on various online platforms.
- Make your networking with people working in similar areas over different social platforms and find a mentor.
THE FINAL THOUGHTS
In conclusion, the field of Artificial Intelligence (AI) and Data Science has touched almost every aspect of life and it is the future. Hence demand for qualified AI &DS professionals is growing rapidly, leading to a hike in the availability of professional courses in both online and offline modes. Thinking of opting for any job roles in this field, basic steps will be the same but to outstand the rapid industry revolution, the adaptation to evolving technologies is a key parameter to excel in future.
[Prof. Iram Nausheen is Assistant Professor in Department of Artificial Intelligence and Data Science at Anjuman College of Engineering & Technology, Nagpur, Maharashtra, India. With 9-year Teaching Experience, she is Pursuing PhD at SAGE University, Indore. Her Research Interests are in Wireless Communication, Machine Learning, AI, Google Scholar indexing and two Patents Published.]