Two clear trends show a demand-supply mismatch in tech jobs in cutting-edge IT fields such as Artificial Intelligence and Machine Learning. One is via industry predictions that estimate growth in the AI market from USD 21.46 billion to USD 190.61 billion between 2018 and 2025. Year on year growth is projected to be an impressive 36.62% during the same period. The second trend is more subtle. Big Indian IT firms in the US are reportedly ‘hoarding’ employees in these 2 fields as they foresee a shortage in skilled experts. They also fear a corresponding rise in the cost of hiring employees for tech contracts they have bagged for the future.
AI & Machine Learning Use cases
Unlike the exaggerated robots of the 2001 Steven Spielberg movie of the same name, Artificial Intelligence (AI) in reality is a tamer. AI is understood to mean ways of making computers, computer-controlled robots or program think intelligently mimicking the manner in which humans think intelligently. A computer program with AI can use can solve generic problems it is programmed to instead of just specific ones. They can accommodate new modifications to input without breaking structure. Traditional programmers would have to sort, sift and debug thousands of lines of code to make modifications.
AI finds applications in strategy games such as chess or poker where advance moves are determined by heuristic logic, natural language processing, virtual assistant technology, image and speech recognition and automated robotics. General AI systems which can solve any given problem are rare. Insurance and banking organizations regularly use AI to monitor fraud. Marketers use AI every time you shop online to gather your browsing habits and predict what you are most likely to buy. They will then advertise those products through pop-ups and logos. Self-driving cars, auto-pilot modes and smart homes using sensors all rely on AI and affect daily lives of consumers.
There is also a difference between AI and Machine Learning (ML) although a number of articles on the web club them together or use them interchangeably. “ML is the study of computer algorithms that improve automatically through experience” according to Tom Mitchell of Carnegie Mellon University. It is simply one of the ways we use to achieve AI or something closer.
Acquiring AI & ML skills
By some estimates, AI will create nearly 2.3 million jobs by next year. It might also make 1.7 million jobs obsolete but those would need to be replaced by 0.5 million new jobs. Nearly all forms of enterprise software, factory automation, transport and other industries are increasingly using AI-based interfaces in their daily operation. In fact, by 2030, AI may end up offering USD 15.7 trillion to the global economy.
Mathematical and programming skills are central to acquiring competency in this field. However, for seasoned tech professionals, it is also quite important to develop excellent communication skills. An understanding of how business works and the common processes used in day-to-day operations will help you better utilize your core competencies to improve organizational workflows.
Core Competencies required in AI & ML
For complete beginners, programming using C++ is a mandatory requirement. Also necessary is an understanding of how algorithms are created and executed.
Typically, knowledge and expertise in Bayesian networks, neural networks, cognitive science theory, engineering, physics, robotics, undergraduate algebra, calculus, statistics and probability are very essential to hone your talents in ML.
Graduates in Computer Science need only supplement their knowledge of math and computing with a specialized course in AI & ML.
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