Artificial intelligence is growing both vertically and horizontally across many sectors and domains. In this article, we explore what AI is, which domains it has made inroads into, and some details on the career opportunities that AI offers to both newbies and hardened professionals with relevant experience.
Man has been on a constant endeavor to build intelligence into the devices he makes, so they do the required tasks independently as possible without human intervention. AI is one such technology that has the power to shape the future of humans. However, as per many experts, AI is still in its nascent stage, and there is a lot left to develop.
More and more businesses are trying to build artificial intelligence into their products to stand out in the marketplace. AI helps them make their products stand out and helps them streamline many processes critical to the design, development, and production of the product.
Table of Contents
- AI for Beginners– A short story of what AI is, for those new to AI and are ready to this complicated beast head-on.
- Skills Required for Artificial Intelligence – A quick summary of skills demanded of any candidate looking for a career in AI.
- Artificial Intelligence Career Paths – A brief discussion about career paths and listing of job roles in AI.
- A Career in Artificial Intelligence in India – Overview of the AI industry for freshers and working professionals.
- How to Start a career in Artificial Intelligence – A high-level guidance on initiating a career in AI.
- How to Switch Career to Artificial Intelligence – A quick guide on switching from an existing career to a career in AI.
- Concluding Thoughts
- FAQs – Frequently Asked Questions
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What Is AI for Beginners?
Artificial Intelligence, a term that has been around for quite a few years (since 1960 to be precise), more of a buzzword from the early 2000s, does not seem to get old at all, and it doesn’t look like it will for at least half a century into the future.
To a commoner, it will suffice to say that intelligence is exhibited by something human-made. According to Wikipedia, a textbook definition can be appropriated to, “Artificial intelligence is a study of intelligent agents which are devices that perceive the environment and take actions that maximize its chances of successfully achieving its goals. AI is often used to describe machines that mimic cognitive functions humans associate with the human mind, such as learning and problem solving”.
Before we start discussing Artificial Intelligence career paths, let’s look at the types of AI we generally deal with. AI generally falls under two broad categories, namely Narrow AI and Artificial General Intelligence.
Intelligence in humans is in everything we do, from watching and learning to carrying out the simplest and mundane tasks while being self-aware and the surrounding. This, when narrowed down to a particular function, like image processing or face detection and face recognition, which is a subset of the general or greater intelligence we humans have, is called Narrow AI. Each function of a human being that exhibits intelligence is being replicated in machines to perform specific functions by a machine instead of a human being.
A few examples of Narrow AI include:
- Autonomous mobility or self-driving cars
- Image recognition
- Face recognition
- Personal assistants on devices like tablets, mobiles, and home automation systems
Machine learning and Deep Learning are ways to achieve this Narrow Intelligence in various domains. Therefore, most of the career opportunities are available for this type of Artificial Intelligence.
Artificial General Intelligence
This type of intelligence is not yet perfected in our world. Still, to define it, we could say, these are machines that can replace a human completely when it comes to being conscious or self-aware, with cognitive capabilities, making it intelligent enough to handle any interaction with its environment, much like the robot Data in the famed television series Star Trek: The Next Generation, or TARS in the Hollywood flick Interstellar.
Industry Penetration of AI
Although AI is making inroads into all industries gradually, industries where AI is making a significant impact are,
- Customer Service
- Airline industry
- Retail and E-Commerce
- Financial Markets and Services
Skills Required for Artificial Intelligence
There are several aspects of human intelligence that AI seeks to mimic human behavior, like Speech Recognition, Image Recognition, Natural Language Processing, Facial Detection and Recognition, Recommendation Systems. In contrast, conventional ways to program a computer to perform specific behavior closely related to human intelligence like Speech Recognition or Image Recognition, Machine Learning, and Deep Learning are the methods closest to how humans learn by exploring and practicing.
Machine Learning and Deep Learning are two techniques used to achieve Artificial Intelligence.
Machine Learning (ML)
ML is a technique by which a computer can learn from data without using a pre-programmed complex set of rules. Instead, ML algorithms build a model based on sample data or training data, which are huge datasets. These models are then used to make decisions and predictions without explicitly programming the rules, as is done using conventional programming methods.
Deep learning is a specialized Machine Learning technique based on Artificial Neural Networks, inspired by the biological neural networks that we humans possess.
Does AI Require Coding?
The narrow intelligence prevalent in the industry is based on Machine Learning and Deep Learning, both of which need extensive libraries to handle and process data quickly and easily. So yes, the answer to that question is yes, coding is a requirement in AI.
What Language is Preferred?
Various programming languages can be used for Machine Learning and Deep Learning, but Python is the most popular language. Python is well suited to ML, mostly due to its ease of use when handling datasets. Python is also a preferred language because of its short learning curve.
What is Python?
Python is one of the most widely used programming languages in Artificial Intelligence, thanks to its simplicity, flexibility, and scalability. You can seamlessly use it with the data structures and other frequently used AI algorithms. Furthermore, being an open-source programming language helps developers modify and evolve it continually and make the syntax easier, contributing to its efficiency. In addition, there are a large amount of in-built libraries that help accelerate AI programming. For example, Tensorflow is widely applied for Mevolving but is; PyTorch for natural language processing. On top of that, python copes well with small scripts as well as easily supports enterprise applications. In short, it’s the most favorable choice of programming when it comes to AI.
What Should We Study for AI?
- Concepts-Computer Science, Statistics, Mathematics, Machine Learning, Deep Learning, Neural Networks, Distributed Computing
- Programming skills required– Python, R, and Scala.
- Database-related skills-SQL, Apache Spark, Hive, No SQL.
- Frameworks/platforms-Hadoop ecosystem or other Hadoop based big data implementation (like AWS Big Data, MS Azure Big Data), Apache Kafka, Apache Spark
Let’s discuss Artificial Intelligence career paths in the next sections.
Artificial Intelligence Career Paths
AI career opportunities have been on the upswing in the past half a decade. If you have a data-driven mind and have a penchant for improving or transforming existing systems, you are well cut out for the AI world. With almost every industry having a few pioneers who have dabbled in AI already, the demand for AI-related jobs has ever increased.
Understanding the job roles in the AI industry will help you understand better the career choices you need to make or the education path you need to take to make it on top of the AI pyramid. The jobs are listed in no particular order or hierarchy, along with the skills required for artificial intelligence.
AI/Machine Learning Engineer/Developer
The key responsibilities of an AI/Machine Learning developer are Statistical analysis, Statistical tests, design, and development of machine learning programs, developing deep learning systems, deciding and implementing a suitable AI/ML algorithm, and training ML systems with the perfect data set.
- Concepts-Computer Science, Machine Learning, Deep Learning, Statistics, Cloud Computing, Natural Language Processing.
- Programming Skills– Python, Scala, and Java.
- Frameworks-Apache Hadoop, Scikit Learn, Spark MLib, H2O, Azure ML Studio, Amazon Machine Learning, Apache Signa.
You may also like to read: How to Become an AI Engineer? Know about Skills, Role & Salary
A data scientist identifies valuable data streams and sources and works with data engineers to automate data collection processes, analyses massive data to identify trends and patterns, build statistical and predictive models that will help to build an appropriate ML system, propose solutions and strategies to decision-makers using compelling visualization tools and techniques.
- Concepts-Computer Science, Statistics, Machine Learning, Deep Learning, Mathematics, Natural Language Processing, Neural Networks.
- Tools-SQL, Python, R, Scala, SAS, SSAS.
- Frameworks-Apache Hadoop, Scikit Learn, Spark MLlib, H2O, Azure ML Studio, Amazon Machine Learning.
Analytics Manager/Data Science Lead
This job role leads a data science team, providing direction and managing resources. This position requires advanced knowledge of big data systems, machine learning architectures, data science techniques, and strong project management and interpersonal skills. An analytics manager sets priorities for the data science and analytics team and communicates its findings to the senior management.
- Concepts-Business Analytics, Data Science, Basic Machine Learning
- Tools-Excel, SQL, Tableau/ PowerBI, R/ Python
- Soft Skills– Strong project management and interpersonal skills.
A research scientist is expected to have mastery in multiple AI disciplines like applied mathematics, computational statistics, machine learning, and deep learning. Like data scientists, a research scientist is expected to have an advanced master’s or doctoral degree in computer science.
- Concepts-Computer Science, Statistics, Mathematics, Machine Learning, Reinforcement Learning, Deep Learning, Natural Language Processing, Neural Networks.
- Programming Skills-Python, R, Scala, SAS, SSAS.
- Frameworks-Apache Hadoop, Scikit Learn, Spark MLlib, H2O, Azure ML Studio, Amazon Machine Learning, Apache Signa.
A Career in Artificial Intelligence in India
How Fast is AI Growing?
Artificial Intelligence has seen seeing rapid adoption across industries. The AI software market is expected to grow at over 54% year on year, for a market size of over 22 billion USD in 2021 and beyond. AI is expected to increase global GDP by 12.5% by the end of 2030.
Building a career in Artificial Intelligence will take some time if you are completely new to it. You need to gain theoretical knowledge of the underlying science before you can grasp the finer details in the field. If you are still on your journey to attain education, you could start by obtaining a Bachelor’s degree in Computer Science/Information Technology/Mathematics and Statistics. This will lay a strong foundation to build on later in your career.
You should also sharpen your analytical, problem-solving skills, learn effective communication, and gain relevant industry knowledge. For beginners, you can start at an entry-level position and work your way up.
For the Data Scientist roles, you have an edge with a Master’s Degree, majoring in computer science/mathematics/economics. You even require a Ph.D. for a Research-based role.
If you are a working professional already in the IT industry, you can gain requisite knowledge and skills in Machine Learning and Deep learning from accredited institutions. If you are not from the IT industry, the learning curve will be a little longer and steep. You will need to pick up computer science basics and Linear Algebra at the least, learn programming languages, learn about database management systems, big data platforms, machine learning algorithms, neural networks, and natural language processing. A master’s degree in computer science will go a long way in cementing the knowledge you gain.
How to Start a Career in Artificial Intelligence
The AI domain, being highly specific and relatively technical, requires one to be proficient in computer science and mathematics concepts. A Bachelor’s degree in Computer Science is preferred, where mathematics relevant to computer science is also part of the course. Still, a degree in mathematics will also work if you can pick up the required skills in computer science later on. Specifically, you should gain thorough knowledge in the below fields.
- Basics of Computer Science
- Programming languages that offer good packages and support for Machine Learning, Natural Language Processing, Deep Learning, and Neural Networks.
- Machine Learning algorithms
- Neural networks
- Mathematics-Linear Algebra, Calculus, Logic and Algorithms
- Statistics-Probability, Combinatorics, Random variables, Bayesian statistics
How to Switch Career to Artificial Intelligence
Before switching a career to Artificial Intelligence, you have to understand that it is a constantly evolving domain. You have to have that constant thirst for knowledge. The knowledge that you gain today might not be relevant 3-5 yrs down the line. So let’s break up the path for you into two streams, one for the people new to IT and the other for people with IT experience but in non-AI technologies.
If you are coming in from a non-IT background, you may be coming in with a wealth of domain knowledge that AI can use to improve the system. However, you could also use your domain knowledge and new skills to give yourself the edge. Let’s list down the skills you should gain before or while on the switch over to AI.
- Firstly, gain enough skill in Python, the easiest programming language. Get the programming DNA right. You can start with simple coding exercises and scale up quickly in Python. To learn to program, you need to know a few concepts in computer science. For example, you could start writing algorithms for smaller programs, giving you a general understanding of ML algorithms.
- Learn about database management systems.
- For Machine Learning, Deep Learning, and Neural Networks, an understanding of mathematics are important. So pick up the required knowledge in mathematics. If you don’t plan to get your hands dirty with ML code, you can skip this one.
- Learn Probability, Basic Statistics, and Random Variables.
- Learn about Machine Learning Algorithms. What is the objective of Machine Learning? How is Machine Learning achieved? What are the packages and tools that assist in programming ML/Deep Learning/Natural Language Processing?
Each of the above requirements might require you to gain further related knowledge.
It would be safe to assume that you have some experience with programming languages and database management systems from an IT background.
- Gain programming experience in Python or R.
- Use Data Visualization packages in Python or R to draw meaningful visualizations. Then, learn how to weave your story around the visualizations.
- Gain knowledge in Machine Learning, Deep Learning, and Neural Networks.
- Brush up on Statistics-Probability, Combinatorics, Bayesian Statistics, Descriptive Statistics.
- Try Machine Learning in Python or R. Learn about pandas, Scikit Learn, Seaborn, the packages available in Python for data science and machine learning algorithms.
Now is time for a few guidelines on adopting AI as a career path.
Choose a specific AI Path
If you choose to be on the technical side, you could be a data engineer, data developer, data analyst, or data scientist. If you do not choose to be on the technical side of things, you could lead a team of data scientists or own an AI product after understanding how AI will be implemented in your product.
Understand the business
No matter what level or path you are in AI, it pays well to understand the business and bring in AI to improve performance.
Take up a suitable AI course
Several online and classroom courses will help you gain skills, starting from a basic level. In addition, you could connect with trainers and fellow trainees and learn more. AnalytixLabs offers comprehensive Artificial programs to suit various types of profiles, namely:
Find a mentor in the AI circle who can guide you on your path while picking up skills.
As is famously said about AI, Artificial Intelligence never stops learning, and that is exactly what will be expected of you once you step into the AI domain. It is an exciting place to be, but it will challenge you at all levels. The question is, are you ready for it? Artificial Intelligence career paths are the most sought-after careers of the 21st century, and there is a lot of intense competition already; start preparing yourself today!
FAQs – Frequently Asked Questions
Q1. What is the future for AI?
In the future, AI will be used extensively to study problems that impact human civilization, space exploration, climate study, disease prevention and control, and much more. In addition, AI is the driving force behind the emergence of today’s technologies like Big Data, Robotics, and IoT and will continue to seed further innovations in the near future.
Q2. Will AI replace all jobs?
AI is feared to take over the jobs of humans. While the potential is there, and it will do so by taking over hazardous and mundane jobs, it only implies that it will push humans to do better, more meaningful jobs in the foreseeable future. With the precision and efficiency that AI-assisted jobs offer, AI is expected to take over some lower-level jobs where the highest degree of precision is required, like automotive assembly or electronic component manufacturing. While AI will replace jobs, there is an increase in workforce sizes predicted after adopting AI technologies, where new AI-based hybrid roles emerge.
Q3. Will AI rule the world?
AI will rule the world, but not in the sense that it is perceived by most. The businesses which master AI will rule the world. The future is for highly efficient and scalable businesses. Future is ruled by AI, with almost all products featuring some level of AI built-in.
Q4. Is python necessary for AI?
Many languages can be used for AI, and the two most prominent ones are C++ and Python. Python seems to win the competition with its popularity, prebuilt libraries, large community support, platform independence, and super flexibility.
Have further questions? Do you have something to share? Post them in the comments section below, and we can help you out.
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