Artificial Intelligence (AI) is an emerging technology that mimics human intelligence, enabling computers and machines to perform complex problem-solving tasks. AI has a very ancient history. Its research began in the 1950s. In the 1960s, the United States Department of Défense used AI by training computers to simulate human reasoning. The term “Artificial Intelligence” was coined by American computer scientist John McCarthy in 1956. He was one of the founders of AI, along with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon.

The first AI programs in Bharat were developed in the 1960s:

Professor H.N. Mahabala of IIT Kanpur conducted one of the first AI programs in Bharat in the 1960s. This laid the foundation for future research. The Knowledge-Based Computing Systems (KBCS) designed by UNDP in 1986 also paved the way for Bharat’s focus on AI. Self-driving cars, virtual personal assistants like Apple’s Siri and Amazon’s Alexa, and the recommendation systems used by Netflix and Amazon to suggest products or content to users are well-known examples of AI in use today.

AI is deeply intertwined with our daily lives

AI is a global phenomenon that is deeply intertwined with our daily lives. It operates silently, from enabling us to unlock our phones with facial recognition to making personalized recommendations on various streaming services. However, have you ever wondered how this “intelligence” is possible for AI? The intelligence of AI is largely driven by Mathematics. Mathematics is the foundation for AI systems to acquire knowledge, engage in logical thinking, make well-informed choices, and strengthen the magic of AI. Thus, Mathematics is the invisible force driving AI’s remarkable capabilities.

Algorithms act as the heart of AI

Algorithms act as the heart of AI. An algorithm is a set of instructions that a computer follows to solve a problem. These algorithms are defined and developed by various branches of Mathematics. “Linear Algebra” in Mathematics is the basis of AI to represent and manipulate data, recognize an image, analyse the pixels in the image, and identify objects. Matrices and Vectors in Linear Algebra are used for data representation and transformation. AI chatbots use Linear Algebra in tasks such as word embeddings, which convert words into numerical vectors for analysis and understanding.

Calculus plays a key role in AI models

“Calculus” in Mathematics plays a key role in optimizing AI models. Techniques like Gradient Descent, rooted in Calculus, are used in AI systems for minimizing errors and enhancing performance. Back propagation, another essential algorithm for training neural networks, relies on Calculus principles. “Multivariable Calculus” in Mathematics helps AI identify desired patterns in vast amounts of data.

Probability and Statistics equip AI to handle uncertainty

AI is based on data, but the data is rarely perfect. A branch of Mathematics called “Probability and Statistics” equips AI to handle uncertainty and make informed decisions even with imperfect data. It is very crucial for AI in spam filtering or detecting fraud, analysing data, identifying patterns, and calculating probabilities. Bayesian inference, for instance, is applied in Natural Language Processing (NLP) to estimate the likelihood of various sentence interpretations.

Statistics provides the theoretical framework necessary for AI

Statistics provides the theoretical framework necessary to understand how well an AI model can learn from data and generalize to unseen examples. It helps prevent over fitting if an AI model performs well on training data but fails on new data.

Optimization Techniques find optimal solutions in AI

“Optimization Techniques” such as Linear Programming, Integer Programming, and Convex Optimization are used in AI to find optimal solutions to complex problems. AI optimizes responses based on criteria such as user satisfaction, relevance, or efficiency using these techniques. Optimization algorithms help AI models navigate a complex “Data Maze” to find the best solution.

Graph Theory helps AI for meaningful conversations

As AI models become more complex, understanding how their decisions are made is crucial. Mathematicians are developing techniques based on “Game Theory” and “Logic” to make AI models more transparent and understandable. Computers need advanced mathematical techniques to understand human language and operate. Disciplines like “Information Theory” and “Graph Theory” work on the complexities of human language and help AI models to have meaningful conversations.

The bond of Mathematics and AI shapes the future of our digital world

The infusion of Mathematics into AI enhances their capabilities in diverse application areas such as Natural Language Processing (NLP), Education, Customer Service, Healthcare, Finance, etc. The deep bond between Mathematics and AI will undoubtedly be at the forefront of technological advancements that will shape the future of our digital world. The brilliant combination of Mathematics and AI is predicted to lead to numerous significant inventions in the future.

AI will never be able to fully replace human intelligence

In conclusion, openness, morality, and objectivity must be rigorously upheld when developing AI models; failing to do so could turn this incredible technology into a threat to human society. Human-made technologies like AI can mimic human intelligence to some extent, but they will never be able to fully replace the creative and emotional capacity that God endowed people with.