Artificial intelligence in simple words

The processing speed of a "machine" and the "human" thinking capacity.

When we talk about Artificial Intelligence, we immediately think of robots capable of understanding and choosing the actions to be taken, and of a futuristic world in which machines and men coexist. In fact, Artificial Intelligence and its use are much more real than you can imagine and are used today in different areas of daily life, for example:

• Healthcare and Scientific Research to diagnose diseases based on the patient's symptoms
• Sales and Marketing for predictive, statistical and simulation activities
• Security and Risk Management to prevent possible threats and/or accidents
• Automation and Tasks Processing to optimize management times and to delegate some of the operations once manual to the machines, such as classification or cataloging of data

Artificial Intelligence can generally be defined as a set of multiple theories and techniques that are combined for the development of a system capable of autonomously and creatively addressing specific problems, showing intellectual capacity sometimes superior to human.

From the point of view of these abilities, the functioning of an AI is substantiated mainly through four different functional levels:

1. understanding: through the simulation of cognitive skills of correlating data and events, the AI is able to recognize texts, images, tables, videos, voice and extrapolate their information;
2. reasoning: systems - through logic - manage to connect the multiple information collected;
3. learning: in this case we are talking about systems with specific functionalities for the analysis of data inputs and for their "correct" output return;
4. interaction (Human Machine Interaction): in this case we mean the mode of operation of the AI in relation to its interaction with humans. Here the systems of NLP - Natural Language Processing - are strongly advancing; technologies that allow humans to interact with machines (and vice versa) by exploiting natural language.

Within the AI discipline, two areas of study that deserve some clarity are Machine Learning and Deep Learning.

• Machine Learning: A subset of AI that provides systems with the ability to automatically learn and improve with experience, without being explicitly programmed; the system can "train" itself independently correcting errors thanks to specific external inputs.
• Deep Learning: A subset of Machine Learning inspired by the structure of the biological brain; it does not require the intervention of the programmer to "learn", thus emulating the learning process of the human mind. In this case, the mathematical model alone is not enough, Deep Learning requires artificial neural networks designed ad hoc (deep artificial neural networks) and a very powerful computational power capable of "holding" different layers of calculation and analysis (which is what happens with the neural connections of the human brain).

It may seem like a futuristic technological level but these systems are already in use today, for example in voice or image recognition, autonomous driving, web search and much more.

Any process or activity can be simplified!