15/03/2023
Learning, reasoning or solving problems are some of the characteristics of human beings. Today, thanks to artificial intelligence, there are computer applications suitable for performing such tasks. The speed of this technology brings with it a wealth of opportunities in various fields. In the banking sector it can be used to improve customer service, optimize credit approval processes, or prevent crime, among other applications. Similarly, it is appropriate to identify several challenges to artificial intelligence.
A few years back, Artificial Intelligence (AI) was seen as the future. But now it is common to find tools and applications from AI algorithms in different areas of daily life and for different purposes: in the form of generative content generators (which are generative AI and developed in programs like Chat GPT, Microsoft or Bard, cf. Google went); able to beat computers and humans playing chess; Personal digital assistant, navigation indicating the best route and thousands of other examples.
More broadly, artificial intelligence refers to the development of systems that are capable of learning, planning, or solving problems, just as humans do. For an electronic device or software to have artificial intelligence, it needs data and algorithms to make decisions. This can be achieved through direct connectivity to the Internet, mass information applications, or other devices for the exchange of information. On the other hand, the second series of instructions, by which they are programmed, are the behavior or forms, according to which they receive different data.
The development of artificial intelligence is mainly driven by disruptive technologies such as machine learning, deep learning, big data or quantitative computing. What differentiates AI from ordinary software is that it can autonomously improve its processes. That is, it learns from what has been done in the past without the need for human intervention. This feature is commonly used in machine learning or machine learning in English.
Imagine you have a robot vacuum cleaner. The goal is to go through all the rooms in your house each day, guided by paper created and saved with your integrated navigation system on the day before (the previous task). But you decide to move some stuff around. The robot then starts colliding with them and automatically activates its browser to update the map (it decides what information it receives). That way, next time you don’t stumble because you can think of a new way.
