In India alone, AI will create 4.5 million jobs by 2025, which is 100 times the current job vacancies in AI, i.e. 45,000.
Industries using AI:
Healthcare: AI is being used in healthcare to improve patient outcomes, reduce costs, and increase efficiency. Applications of AI in healthcare include medical imaging, drug discovery, virtual care assistants, and personalized medicine.
Financial Services: AI is being used in financial services to improve risk management, fraud detection, customer service and investment analysis. Applications of AI in financial services include chatbots, robo-advisors, algorithmic trading, credit risk analysis, and insurance.
Retail: AI is used in retail to improve customer experience, increase sales and streamline operations. Applications of AI in retail include recommendation engines, chatbots, inventory management and supply chain optimization.
Manufacturing: AI is being used in manufacturing to improve production efficiency, reduce downtime, and streamline supply chain management. AI applications in manufacturing include predictive maintenance, quality control, and process optimization.
Transportation: AI is being used in transportation to improve safety, reduce congestion and increase efficiency. Applications of AI in transportation include self-driving vehicles, traffic prediction, and logistics optimization.
Education: AI is being used in education to improve student outcomes, personalize learning, and optimize educational resources. Applications of AI in education include personalized learning platforms, intelligent tutoring systems, and predictive analytics for student performance.
Travel and Tourism: AI is used in the travel and tourism space to provide personalized recommendations, chatbots and virtual assistants, price optimization, ticket booking, fraud detection, security measures and language translation.
Banking: AI is being used in banking to improve customer service, fraud detection, risk management and compliance. Applications of AI in banking include chatbots, credit scoring, fraud detection, and regulatory compliance.
Insurance: AI is used in insurance to improve risk assessment, fraud detection, recommendations, and claims management. Applications of AI in insurance include underwriting, claims processing and customer service.
A new report finds that by 2030, more than 85 million jobs could remain unfilled because there aren’t enough qualified people to fill them.
As the demand for AI professionals is high in various industries, it is important to note that the field of AI is still developing and there are many specialties. Pursuing a career in AI may require a solid foundation in math, computer science, programming, and even specialist knowledge. While there is certainly an opportunity to make a difference in this space, it is important that individuals carefully consider their interests and skills before pursuing a career in AI.
Roles in the AI:
Machine Learning Engineer
A machine learning engineer is responsible for creating algorithms that allow machines to learn and make decisions based on data. Machine learning engineers typically have a background in computer science, mathematics, or a related field. They use their expertise in programming, data analysis and statistics to develop and improve machine learning models.
Data scientists collect, analyze, and interpret complex data sets to help organizations make better decisions. You will work closely with business leaders to understand their needs and design data-driven solutions. Data scientists usually have a background in statistics, mathematics or computer science and need to be skilled in data analysis and machine learning.
AI research scientists are responsible for developing new AI algorithms and techniques to solve complex problems. You will work on cutting-edge research projects and collaborate with other scientists to develop new AI models. AI research scientists typically have a Ph.D. in computer science, mathematics or a related field and they must be highly skilled in machine learning, programming and data analysis.
Robotics engineers are responsible for the design and development of robots and other automated systems. They work on a variety of projects including manufacturing, healthcare, and defense. Robotics engineers typically have a background in mechanical or electrical engineering and must have knowledge of programming, electronics, and automation.
Natural Language Processing/ChatBot Engineer
NLP engineers are responsible for developing algorithms that enable computers to understand and interpret human speech. They work on a variety of projects including chatbots, virtual assistants, and language translation. NLP engineers typically have a computer science or linguistics background and are required to have knowledge of programming, linguistics, and machine learning. Generative AI (LLMs) also need human trainers/labellers. A recent Gartner report estimates that the conversational AI platform market is growing 55% year over year. Currently, chatbots are the most widespread type of AI in companies. In the next two to five years, their adoption rate is expected to almost double. By 2023, AI-supported chatbots are expected to save up to 5 billion working hours. Contact center deployments with conversational AI will save $80 billion in labor costs by 2026.
Computer vision engineers are responsible for developing algorithms that enable computers to interpret and analyze visual data such as images and videos. They work on a variety of projects including autonomous vehicles, security systems, and medical imaging.
Specialist in AI ethics and governance
AI ethics and governance specialists are responsible for ensuring that AI systems are developed and deployed in a responsible and ethical manner. They work closely with business leaders and policymakers to develop policies and regulations for the use of AI. AI ethics and governance specialists typically have a legal, philosophical, or related background and need to be trained in ethics, policy development, and risk management.
In summary, a career in artificial intelligence (AI) offers numerous opportunities for individuals with a background in computer science, mathematics, and data analysis. The increasing demand for AI experts is widespread across various industries including healthcare, finance, retail, manufacturing, transportation, education, travel and tourism, banking and insurance. There are various roles in AI including machine learning engineer, data scientist, AI research scientist, robotics engineer, NLP/chatbot engineer, computer vision engineer, and AI ethics and governance specialist. A career in AI requires a solid foundation in math, computer science, programming, and subject matter skills, and individuals should carefully assess their interests and skills before pursuing a career in AI. With the rapid growth and evolution of AI, skilled AI professionals are essential to harness their power to transform the way we live and work.
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The views expressed above are the author’s own.
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