Career in Artificial Intelligence (AI)

Artificial Intelligence (AI) empowers computers and electronic gadgets to solve problems the same way a human mind does. According to John McCarthy “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

The AI has gone through many rounds of publicity in the past few years, although many doubted its capabilities including experts but the release of OpenAI’s ChatGPT has marked a turning point in the AI domain. Now the applications of artificial intelligence are growing rapidly. It is expected that by 2030, AI could contribute up to $15.7 trillion to the global economy.

Types of Artificial Intelligence

Weak AI: Also Artificial Narrow Intelligence (ANI) or simply Narrow AI is the AI that is skilled to accomplish specific tasks. Most of the AI that surrounds us today is the example of Weak AI.

Strong AI: The Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI) also called as General AI, is a theoretical form of AI where a machine would have an intelligence equal to humans. The Artificial Super Intelligence (ASI)—also called superintelligence—would outshine the intelligence and ability of the human brain.

The Strong AI is still theoretical; there are no practical examples in use today, but AI researchers are working on its development. The best examples of ASI might be from science fiction, for example; the superhuman, and rogue computer assistant in 2001- A Space Odyssey, etc.

Applications of AI

AI has many applications in Real world. Some of the most common are:

Speech recognition
Customer service
Computer vision
Recommendation Engines
Automated stock trading

Benefits of AI

Automation
Reduce human error
Eliminate repetitive tasks

AI Engineer Qualification/Eligibility

To become an AI Engineer bachelor’s degree in subjects like Computer Science, Information Technology, Data Science, Mathematics and Statistics is required. Further candidates can pursue Master’s degree programme in a related discipline. Knowledge in data science, deep learning, and machine learning will be an added advantage.

AI Job Titles

Some of the common job titles in AI are:

Artificial Intelligence (AI) Engineer
Machine Learning Engineer
Data Engineer
Robotics Engineer
Software Engineer
Data Scientist

AI Engineer Job Responsibilities

The uses of AI are growing. Skilled AI Engineers are in high demand. Some specific job responsibilities of an AI Engineer are:

Create and manage the AI development and production infrastructure.
Conduct statistical analysis and interpret the results to guide and optimize the organization’s decision-making process.
Automate AI infrastructures for the data science team.
Build AI models help product managers and others with analysis and implementation.
Transform machine learning models into APIs that can be integrated with other applications.
Work together with different teams to help with AI adoption and best practices.

AI Engineering Job Prospects

AI Engineering is a promising career option today. Opportunities are limitless for qualified AI Engineers. The Indian Job market is expected to grow at 15 to 20% in the field of AI alone. More and more companies will be turning to AI for assistance in the coming days and therefore job options for AI professionals will grow.

AI Engineers Skills

The AI Engineers should be competent in computers, information technology, mathematics & statistics etc. Following skills are required to become AI Engineer:

Programming: Knowledge and skill in computer programming languages such as Python, R, Java, and C++ etc.
Probability, statistics, and linear algebra: These are required to implement different AI and machine learning models.
Big data technologies: AI engineers work with large amounts of data. You should be efficient working with technologies like Apache Spark, Hadoop, and MongoDB to manage it all.
Algorithms and frameworks: Understanding of machine machine learning algorithms and deep learning algorithms and ability to implement them with a framework, for example; Theano, TensorFlow, Caffe, Keras, and PyTorch.