"The last 10 years have been about building a world that is mobile-first, turning our phones into remote controls for our lives. But in the next 10 years, we will shift to a world that is AI-first, a world where computing becomes universally available." (Sundar Pichai, CEO of Google).
What we call AI is Artificial Intelligence, a term created in 1956 by John McCarthy, an assistant at Dartmouth College, which describes a machine capable of performing tasks usually requiring human intelligence. The field of Artificial Intelligence is so wide that it is difficult to measure its density. This area covers different disciplines including understanding, calculation, reasoning, learning, perception and natural language dialogue.
There are two types of Intelligence: Strong Artificial Intelligence and Weak Artificial Intelligence.
The concept of strong Artificial Intelligence refers to a machine capable of producing intelligent behaviour, of experiencing an impression of real self-awareness. In other words, the intelligence is combined with an understanding of its accomplishments.
Weak Artificial Intelligence is a pragmatic approach to engineering. This time, the machine simulates intelligence.
Machine Learning, a sub-discipline of AI
Artificial Intelligence comprises sub-disciplines such as machine learning (ML). All ML is AI, but all AI is not ML. The apparatus performs an unrequested task. It has its rules. This system allows the machine to learn from its mistakes. Concretely, the machine is fed big data to help it "become intelligent".
The data revolution
Data is more than ever present! 90% of all data available in the world have been collected in the last two years. Today, two major breaks give us the ability to collect data: the real-time capture of a significant amount of data and its reliability.
We are living in the big data era! Big data means an awful lot of information regarding volume (amount of data), variety (type and sources of data), speed (the frequency at which they are collected).
To give you an idea, segmenting consumers according to the pages viewed on your website is probably not considered as big data. However, updating this segmentation every hour by adding details related to purchases, analysing responses to advertising messages and attaching images of the products seen can be extremely relevant.
Deep learning, another branch of AI
Let us turn to deep learning, another aspect related to Artificial Intelligence. This method is a learning technology based on artificial neural networks. For example, deep learning allows a programme to recognise the content of an image or to understand the spoken language. This learning and classification system based on digital artificial neural networks is used by Siri, Cortana and Google Now to understand the voice and be able to remember faces.
To fully grasp the significance of deep learning, it is necessary to talk about supervised learning, a common AI technique allowing machines to learn. Concretely, a programme learns to recognise a car by processing tens of thousands of car images, a "training" which may require hours or even days. Once trained, the programme can recognise cars based on new images.
In conclusion, the data collection allows feeding Artificial Intelligence. AI will no doubt help you drive your car, run errands and create the slogan for your next product that will likely be AI-stamped. The future, therefore, is the control of our environment that will be increasingly more often flooded with data that must be interpreted, processed and put in interaction with others. This can all contribute to automating and improving our production.