Artificial intelligence (AI) is a broad field of computer science concerned with the development of intelligent machines capable of performing tasks that would normally require human intelligence. If you are finding yourself to be attracted towards this field M.Tech in Artificial Intelligence is the right course for you.

In the upcoming years Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would be capable of understanding everything on the internet. It would understand exactly what you want and respond accordingly. Due to such an advancement in technology, the demand for it is increasing rapidly day by day, which on the other hand is making it difficult to enter into this field because of vast competition. 

In this blog we will be discussing how can you land into AI World:-

• What is Artificial Intelligence?
• Examples of AI
• Educational criteria for careers in AI
• Skills required to enter the domain

What is Artificial Intelligence?

Artificial intelligence, in general, refers to processes and algorithms that can mimic human intelligence, including cognitive functions like perception, learning, and problem solving. AI encompasses both machine learning and deep learning (DL).

Examples of AI

Machines can learn from experience, adapt to new inputs, and perform human-like tasks (AI). Using these technologies, computers can be trained to perform specific tasks by processing large amounts of data and recognising patterns in the data.Modern web search engines, personal assistant programmes that understand spoken language, self-driving vehicles, and recommendation engines like those used by Spotify and Netflix are all examples of practical applications of AI.

Educational criteria for careers in AI

Most artificial intelligence programmes are built on fundamental computer technology and math. Positions at the entry level require at least a bachelor’s degree, while positions requiring supervision, leadership, or administrative responsibilities frequently require a master’s or doctoral degree. Typical coursework entails the study of:

• Core math concepts , such as probability, statistics, algebra, algorithms, calculus, and logic.
• Bayesian networking and graphical modeling, including neural nets, are two examples. Engineering, physics, and robotics
• Coding, programming languages, and computer science
• Theory of cognitive science

Skills required to enter the domain

• Hands-on experience in data science and statistics

One thing to keep in mind is that machine learning or artificial intelligence is not a theoretical or academic concept. As a result, deep research and understanding the theoretical nitty-gritty of statistical concepts is not required to become a successful AI engineer.

• Fundamentals of computer programming

As the role of an AI engineer is to simulate a machine to behave like a human, in-depth knowledge of computer software fundamentals such as data structures, trees, graphs, optimization algorithms, linear programming, and computer architecture is required. As a result, it would be difficult to cope without understanding the working principles of systems

• Probability distribution and statistics

Statistics are the foundation of data science, which is an essential component of machine learning. ML engineers must understand probability concepts such as conditional probability, Bayesian principles, Markov models, and so on. Furthermore, they should be familiar with both univariate and multivariate statistical analysis, as these are the foundations of machine learning techniques.

• Data modeling and model verification

Machine learning makes extensive use of data modeling techniques (a subset of statistics) to identify valid patterns and classifications on datasets. Anyone interested in a career in AI and Machine Learning should be familiar with these skills.

•  Design and software development

Although this skill may appear to be the least relevant for a machine learning or artificial intelligence engineer, professionals rely on it. As a result, basic knowledge of system design and deployment is required to be a successful ML/AI engineer.

Conclusion

We are at the beginning of a golden age of AI. Recent advancements have already led to inventions that previously lived in the realm of science fiction – and we have only scratched the surface of what’s possible.