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Artificial Intelligence: what it is, how it works, how it has changed

Artificial Intelligence is attracting the attention and investment of the leading companies in the world in the last two years. But what does this mean exactly and what is the difference with Machine Learning?

In 2016 Alpha Go, the software developed by Google DeepMind and programmed to play Go, the famous oriental strategic board game, beat Lee Se-dol, a professional with 18 world titles, a true legend of the game. It was, in essence, the first software able to defeat a human master in a game on a “goban”, that is a standard-sized board: so the Artificial Intelligence (AI) and Machine Learning (ML) achieved the honors of the chronicle, after there having been arguments from insiders for a long time.

Immagine che ritrae la partita a Go, fra Alpha Go, il software sviluppato da Google DeepMind e Lee Se-dol, campione da 18 titoli del mondo

The AI sector is experiencing an important impetus: in the last two years, it has attracted a great deal of investment. From Amazon to Facebook, through Google and Microsoft, it is estimated that in 2022 the value of investments in the sector will exceed 16 billion dollars, with a growth of 62.9% from 2016.

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Use it as a test or as a memorandum for your weekly activities. Share it with your department and build your Data-Driven Strategy day after day.

The extraordinary performance of AlphaGo – which has not stopped at that result but has continued to beat world champions, winning over 50 matches in a row at the beginning of 2017 – is just one of the most brilliant applications of this research area: in reality we are surrounded by devices born thanks to AI, which continue to evolve thanks to Machine Learning. Just think of self-driving cars, smart appliances and speech / facial recognition solutions in our daily lives. But what is the difference between AI and ML?

A bit of history: how Artificial Intelligence was born

AI has a long history, which has its roots in the Aristotelian syllogism. But the birth of AI in a philosophical sense occurs in the 40s and 50s of the last century, when mathematicians, engineers, psychologists, economists and politologists give voice to the idea of ​​creating an artificial brain. This leads to the foundation of the “Artificial Intelligence” field as an academic discipline in 1956.

It is during this period that Alan Turing publishes his reflections on the Touring Test in the article “Computing machinery and intelligence” published in 1950 in the journal Mind, in which he speculates on the possibility of creating thinking machines. To pass the test, the computer had to be able to conduct an indistinct conversation from a conversation with a human being. This is the first formal formulation of the basic principle of AI: a science that develops technologies with the aim of imitating the response of the human being to certain circumstances. In simpler words, Artificial Intelligence involves machines that behave – and think – like humans.

A decidedly vast field, later divided into sub-problems like:

  1. Deduction, reasoning, problem solving
  2. Representation of knowledge
  3. Planning and scheduling
  4. Automatic learning
  5. Development of natural language
  6. Perception (Computer vision)
  7. Creativity
  8. General Intelligence, or Strong AI

Further on, each of these sub-problems has become an autonomous sector. To give an example, a distinction was made between Strong AI (or General), a technology able to replicate human intelligence thanks to Machine Learning – able to perceive, classify, learn and reason, predict and interact – and the Weak AI (or Narrow), which instead focuses on specific use cases: some examples may be Google search, the recommendations or chatbots, which are able to analyze Big Data to improve and optimize a range of daily activities.

In fact, ML is therefore a sub-problem of AI: the sector will be closely linked to the AI field, but will grow autonomously outlining in turn sub-themes that will be developed separately.

Source: Exastax

If you are convinced that your Artificial Intelligence strategy is going well, do not continue reading this article and download our checklist to see if you really have everything under control:

download the checklist

Use it as a test or as a memorandum for your weekly activities. Share it with your department and build your Data-Driven Strategy day after day.

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