Title: AI: A Beginner’s Guide

Dr. Nidhi Goenka

Assistant Professor

Faculty of Commerce and Management

Kalinga University

Email-id. nidhi.goenka@kalingauniversity.ac.in

Artificial Intelligence (AI) is a fundamental technology that simulates human intelligence in machines, enabling them to perform tasks that typically require human cognition. These tasks can range from recognizing patterns in data to making decisions and learning from experience. AI systems are powered by algorithms, mathematical instructions that enable machines to process data and execute tasks. Machine learning, a branch of AI that focuses on learning from data without explicit programming, is one of the main elements of AI. In conventional programming, programmers create code to instruct computers to perform tasks, but big datasets are used to train algorithms in machine learning, enabling them to recognize patterns and anticipate outcomes based on fresh data.

Machine learning algorithms include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training algorithms on labelled data, allowing them to learn patterns and structures. Unsupervised learning involves training on unlabelled data, revealing hidden patterns. Reinforcement learning involves agents learning to make decisions through interaction with the environment and receiving feedback in the form of rewards or penalties. Neural networks, modelled after the human brain, consist of layers of linked neurons that process input and output. They are useful for tasks like image identification and natural language processing due to their ability to learn intricate patterns and correlations in data through training.

AI has numerous applications in healthcare, finance, transportation, and entertainment. In healthcare, AI systems aid doctors in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. In finance, AI algorithms analyse financial data to detect fraud, optimize trading strategies, and detect fraud. In transportation, AI drives innovations in autonomous vehicles, traffic management systems, and logistics optimization. However, AI raises ethical and societal concerns, such as algorithm bias, job displacement due to automation, and privacy and security implications. Understanding AI basics helps appreciate its potential and contribute to responsible development and deployment. Exploring AI is an exciting journey into the future of innovation and discovery.

References

1. Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
4. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
5. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. In Advances in Neural Information Processing Systems (pp. 1-9).
6. Ng, A. (2014). Machine Learning. Coursera.
7. (2019). Deep Learning. edX.