The term artificial intelligence was used the first time in 1955 by John McCarthy, a math professor at Dartmouth who organized the seminal conference on the topic the following year. In 1957 the economist Herbert Simon predicted that computers would beat humans at chess within 10 years (he was slightly wrong, it took 40). In 1967 the cognitive scientist Marvin Minsky said, “Within a generation, the problem of creating ‘artificial intelligence’ will be substantially solved.” Simon and Minsky were both intellectual giants, but they were wrong about AI badly. These dramatic but wrong claims caused various repercussions in how people in the second half of 20th century thought about AI: more as a subject of a science fiction novel than actual science.

The idea of an Artificial Intelligence, automation of certain repetitive processes, dates back to the Cold War when US intelligence was trying to translate Russian documents...

Let’s now jump into basic terminology related to AI. When we say Artificial Intelligence we mostly mean machine learning - a domain of computer science that uses learning algorithms able to tune themselves on data provided by a user. The fundamental block of machine learning is neural networks. They are algorithmic systems based on simulating connected “neural units,” loosely modeling the way that neurons interact in the brain.

As we have mentioned above, these computational models inspired by neural connections have been studied since the 1940s. They have returned to prominence with the rise of computer processing power able to cope with large training data sets and have been used to successfully analyze input data such as images, video, and speech. Deep learning is a subset of machine learning, where neural networks have many layers of neurons (“deep network”). The more layers you include in your machine...

Following analysis done by AI State Index1, let’s review some statistics related to Artificial Intelligence, that will fully show how important this market is becoming (or already is). It’s crucial to understand that we are still early when it comes to applied AI, and most of those statistics will grow substantially in the upcoming years. The reason for that is most of the cutting-edge research is still far away from day-to-day business applications either because of the costs of the hardware or the required expertise to apply it. I expect the full AI boom to come within the next ten years when every company will need to implement AI elements to be competitive even at the local scale. This will come in pair with the democratisation of AI: the cost and difficulty of implementation of most algorithms will largely decrease. AI applications will be as available as general cloud storage is now.

Looking at Google Trends one can see that “...