When we think of artificial intelligence, we usually think of the humanoid robots from movies portrayed as the villains who take over the world. But, in reality, we don't yet have robots that can surpass human intelligence.

That said, AI has already taken over our lives. Your Smart Home devices, the facial ID recognition in your phone, the chatbots you interact with while shopping online, your music, video, and shopping recommendations— are all powered by AI.

What Is AI (Artificial Intelligence)?

Brain showing neural connections and numbers

In simple words, AI is any program that can do 'intelligent' tasks similar to a human. But it's not just simple software.

How Does AI Learn?

In a software program, your output solely depends on what the code says. For example, let's say you've written a code to identify cats. Your code tells that anything with four legs, a tail, and fur is a cat.

It will identify every furry animal as a cat, even if it's seeing a dog or tiger or a polar bear. The only way to correct it is to change the code to include specific characteristics of a cat, like size, shape, color, and skin pattern.

In the case of AI, the machine learning experts train the algorithm to correct itself. They key in a large amount of data (in our case, animal photos), reward the program every time it identifies the cat correctly, and punish if it makes a mistake.

When you train it repeatedly with vast amounts of data, the algorithm will eventually learn to identify the cat. What's more, it will generate patterns from the data and identify other animals too. This is called Machine Learning.

Deep Learning takes machine learning to the next level with a lesser need for human intervention. With the help of complex neural networks, each algorithm can learn and change itself. Artificial neural networks are algorithms modeled after neurons in the human brain. The algorithms run on powerful computers to connect, interact, and learn from each other, just like our neurons.

Making a Career in AI

AI is in most leading industries, from e-commerce to healthcare and agriculture. Companies rely on AI for personalized recommendations, market analysis, fraud detection, and virtual/augmented reality.

It takes a specialized team to build AI projects. To begin with, we need to identify reliable data, analyze them, feed them to the machine, and then train it to learn. So, the opportunities are endless for people who like to work with data and machine learning.

As a dynamic, highly technical, and specialized field, AI jobs are well-paying, and you should be highly skilled and adept at technology to break into the market. If you're eyeing a career in the AI field, you need to act now. Here are your options:

Business Analysis and Research

Research and analysis conceptual image showing lightbulb and words in a chalkboard

Research is the first step of the AI process. The key people driving this will be domain experts, business analysts, and researchers. They are experts in their industry or domain like banking, insurance, manufacturing, etc., and play a pivotal role in identifying opportunities, defining scope, researching the market, and making dynamic decisions. They also liaise between the business and the core AI teams.

Skill Set Required:

To be a domain expert or researcher, you will need an advanced degree in your field. For example, business analysts have a degree in Business, Economics, Statistics, or a closely related field. Critical thinking, problem-solving, and flexibility are essential skills for someone in a research and analysis team. In addition, a passion for technology and a willingness to learn new things will help you ace these roles in an AI project.

Data Science

Human hand showing a ball with binary numbers

Data drives our modern world, and there is no AI without data. The success of any AI project depends on the quality of the data. That is why there is a massive demand for data analysts, data scientists, and data engineers.

Data analysts are responsible for collecting data and analyzing it for business insights.

Data scientists take this to the next step by looking for patterns using different techniques like deep learning and neural networks. The insights help businesses to solve problems and innovate.

A data engineer's job is to build the necessary infrastructure for data handling. The engineers set up the database and communication pipelines for the data to flow.

Most of the time, these roles are loosely defined in a data team, and you may be expected to put on more than one hat.

Skill Set Required:

To get into either of the data handling roles, your basic technical skills will more or less be the same, varying slightly in degrees. You should hone your STEM skills, learn to code, grasp database concepts, and earn a degree in Computer Science, Mathematics, or Statistics. You'll probably start as a data analyst and transition to a scientist or an engineering role with experience. You can check some of our Data Science learning suggestions or learn Python, a popular programming language choice for Data Science.

Machine Learning

A robot wondering at a set of data - Machine Learning conceptual image

Machine Learning programmers, engineers, and architects are the group of people who will design, develop, and test complex AI algorithms. They will also train the algorithms to look for patterns and enhance their outputs over time.

Skill Set Required:

It would help if you had an advanced degree in computer science and analytical skills and creativity. You should be skilled at programming languages and software concepts. If you're already a software engineer, you can get into Machine Learning with short certificate courses in AI. You can use these Machine Learning project ideas to kick-start your learning.

Product Design

Hand showing a robot on a tablet

The end product of an AI design can be a screen or a giant robot, but the product designer's job is to make sure the product is accessible and easy to use.

Skill Set Required:

Product designers are from diverse backgrounds--you can be a UI designer, engineer, or artist. Along with specialization in your field, you should be a tech enthusiast who can empathize with the end-users. Flexibility, adaptability, and a human-centric approach are essential to thrive in an AI design team.

AI Hardware

clouds surrounded by circuits

AI systems need colossal memory and processing power. Thanks to the innovation of cloud computing, AI systems are everywhere now. The cloud data is stored in different servers in various locations. Storing and processing data needs hardware like memory, CPUs, and GPUs. There is also a need for infrastructure like cloud networks.

Skill Set Required:

Consider getting a degree in Electrical, Electronics, or Network Engineering to work with AI hardware.

Other Roles

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If you're not a techie, don't give up your dream of getting into the AI world. There are always other roles like project managers, writers, linguists, and lawyers. As more people-centric industries like healthcare and education embrace AI, new opportunities like ethicists and futurists are also opening up.

AI Is a Future-Proof Career Today

AI is an exciting and upcoming field for you to start your career. However, for those in other areas, you still have an option to choose your career in AI--all you need is the curiosity to learn and upskill yourself.