The term Artificial Intelligence (A.I.) was coined in 1956. But A.I. has become more popular today due to the increased volume of data, advanced algorithms, computer power, and data storage impovents. Artificial Intelligence makes machines capable of learning from experience, adapting to newly imported data, and performing anthropomorphic tasks. Most of the A.I. ​​examples we hear today – from computer games that play chess to self-driving cars – are based mainly on deep learning and Natural Language Processing (NLP).

Using these technologies, computers can be trained to perform specific tasks by processing large amounts of data and recognizing forms in the data. There is a high demand for A.I. skills in every industry, especially in question-and-answer systems that can be used for legal aid, patent research, emergency alerts, and medical research. A.I. applications can provide personalized drug management and x-ray readings. Personal health care providers can affect as lifelong counselors, reminding you to take your medication, exercise, or eat healthier. Artificial Intelligence is going to change every industry but we need to understand its limits.

The main limitation of A.I. is that it learns from the data. There is no other way for knowledge to be integrated. This means that many inaccuracies in the data will be reflected in the results. Additional prediction or analysis levels must be added separately. Artificial Intelligence works by combining large amounts of data with the fast, repetitive process and intelligent algorithms, allowing software to learn from data forms or features automatically. Artificial Intelligence is a field of study that includes many theories, methods, and technologies.