AI could replace Olympic referees
AI could replace Olympic referees

The Olympic Games are one of the most important sporting events of the year. It has been successfully implemented in the past few weeks. Due to the COVID-19 pandemic, it has been postponed until 2020. A large number of professional athletes around the world have also won gold medals, be it gold, silver or bronze. Perhaps AI could play a role in this context in the future.

One of the most common scenes is still the one where the judges present medals to the winning athletes. In the future, this scenario may not be repeated often due to artificial intelligence and its tools. It can replace referees in some sports.

Many researchers and mathematicians believe that AI balance will become more accurate and fair. It is also more effective in some sports, such as diving. In fact, sports are played in a small, easy-to-manage environment. Unlike other sports, you need a large training area.

Artificial intelligence based arbitrage

Artificial intelligence technology plays a huge role in the sports arena today. For example, the US Open is starting to move away from manual refereeing and instead rely on artificial intelligence and automated tools.

Several smaller baseball leagues have begun testing similar instruments. Additionally, in recent years, machine learning technology in general has helped improve AI's ability to track players and their performance.

One of the startups in this field is Hawk-Eye, a subsidiary of Sony. It provides referee skills for many tournaments and leagues, including the aforementioned open tennis tournaments. One of the most important of these is ball tracking technology, which is used in football, tennis, rugby, etc.

In addition, the AutoStats Stats Perform system was very successful. Simply set up a camera on the field to collect data and train the machine on the data.

Artificial intelligence can also play an important role in the training process. Because it will be able to give players an accurate estimate of their playing level.

Currently, there are tools that can measure the professional level of a player only by clips recorded for him during the game, moreover, such tools have appeared on smartphones in the form of simple application applications.

For example, the SwingVision application can analyze the capabilities of an athlete only using clips recorded with a smartphone and other applications that offer the same functionality.

The accuracy and professionalism of these applications is expected to increase with the increase in the amount of data collected and the general improvement in machine learning technology. Installing cameras in large stadiums and developing software that can analyze game levels would be a breakthrough in this area.

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