What is artificial intelligence?
John McCarthy in 2004 in his article (PDF, 106 KB) defines AI as follows: “It is the science and technology of creating intelligent systems, especially intelligent computer programs. Experimental intelligence is concerned with testing the use of computers to understand how the human mind works, but does not limit the use of methods observed in biology. “
In 1950, Alan Turing published his landmark scientific work Computing Machines and the Mind (PDF, 89.8 KB) (link is external). In this article, Turing, often referred to as the father of computer science, asks the question, “Can machines think?” To test this, how he tests, which today everyone knows as “Turing”: the experimenter determines by written answers. After the publication of the results of this test, they caused heated discussions, but, despite this, it remains a significant milestone in the history of AI and, to some extent, data science services, since it works at the interface with linguistics.
Over time, Stuart Russell and Peter Norvig published Artificial Intelligence: A Modern Approach, which has become one of the most famous and popular textbooks on AI. The authors classify AI into four main categories that characterize computer systems depending on rational thinking and actions:
- Systems that think like people
- Systems that act like people
Why is artificial intelligence important?
AI adapts thanks to progressive learning algorithms so that further programming is carried out on the basis of data in machine learning services company.
How to start building a career
Data analytics stereotypes don’t work – it doesn’t matter if a data analyst has a liberal arts or technical background.
“I have no technical education, I studied at the Faculty of Public Administration. And I studied Python in the course of bioinformatics for biologists. In my opinion, this language is most suitable for a start, the base of skills for working with it is acquired in two to three months. Then it’s worth exploring specialized libraries for collecting and analyzing data. The more libraries you know, the more high-quality analytics is available to you, ”says Sergei Ustinov.
Companies don’t expect an aspiring data analyst to be able to do everything at once. They are ready to train and guide the young specialist. The main thing is interest in solving business problems. A correctly formulated question before research is more important than a long experience with software tools.
“Programming and math can be learned. And softskills are gained by experience and practice. Therefore, hackathons and championships with the solution of practical problems are useful for data analysts. He feels more confident, pumping a style of thinking focused on solving specific business problems, ”says Anna Chuvilina.
IT beginners are most eagerly hired for positions related to data analysis: the share of vacancies for candidates with less than a year work experience here is a quarter higher than in the market as a whole.
Employers expect a beginner to:
- knows at least one programming language: Python or R;
- knows how to write queries to SQL databases;
- can show conclusions and metrics in the form of an understandable dashboard (Tableau, Power BI, Amplitude);
- wants to understand business processes, thinks in terms of business tasks.