JNN Institute of Engineering, Tiruvallur Reviews on Infrastructure, Campus, Placement, Facilities & Faculty

Hello aspirant,

Artificial Intelligence is the ability of machines to seemingly think for themselves. AI is demonstrated when a task, formerly performed by a human and thought of as requiring the ability to learn, reason and solve problems, can now be done by a machine. A prime example is an autonomous vehicle. The vehicle is able to perceive its surroundings and make decisions in order to safely reach its destination with no human intervention. Converging technologies along with Big Data and the Internet of Things (IoT) are driving the growth of AI. Machines communicate with one another and are now capable of advanced perception, capturing millions of data points in seconds, processing the information and making decisions, all in a matter of seconds. As AI evolves, machines will have more capability to physically act based on their intelligence, eventually leading to machines that can build better versions of themselves.

The field of Artificial Intelligence (ai systems) and machine learning algorithms encompasses computer science, natural language processing, python code, math, psychology, neuroscience, data science, machine learning and many other disciplines. An introductory course in AI is a good place to start as it will give you an overview of the components bring you up to speed on the AI research and developments to date. You can also get hands-on experience with the AI programming of intelligent agents such as search algorithms, games and logic problems. Learn about examples of AI in use today such as self-driving cars, facial recognition systems, military drones and natural language processors.

Go further with courses in Data Science, Robotics and Machine Intelligence. Learn the fundamentals of how robots operate, including how to represent 2D and 3D spatial relationships, how to manipulate robotic arms and plan end to end AI robot systems. In Machine learning, explore unsupervised learning techniques for data modeling and analysis including data clustering, computer vision, reinforcement learning, problem solving, machine learning algorithms, image recognition, data mining, speech recognition matrix factorization and sequential models for order-dependent data.

Start with Artificial Technology and get an overview of this exciting field. If you are unfamiliar with basic computer science and programming, it will be helpful to take and introductory class to learn Python, R or another programming language commonly used in data analysis.

I hope it helps.