It is difficult to estimate the exact proportion of news articles generated by machine learning, as this varies widely depending on the type of news organization, its size, and its geographic location.
However, the use of machine learning and AI to generate news articles is becoming more common, and it is estimated that a significant proportion of news content today is generated by machines.
One area where machine learning is widely used is in the production of financial news, where algorithms can analyze large amounts of data to generate reports and summaries of market events.
Major news organizations, such as Bloomberg and Reuters, have been using AI to generate financial news for several years, and these systems are now an integral part of their newsrooms.
The goal of these systems is to provide faster and more comprehensive coverage of financial markets, so that investors and traders can stay informed in real-time.
Another area where machine learning is used to generate news is in sports journalism. Algorithms can be trained to analyze large amounts of data to generate match summaries, player statistics, and news updates.
For example, the Associated Press has been using AI to generate earnings reports and other financial news for several years, and more recently, it has started using machine learning to generate sports news.
These systems can quickly analyze large amounts of data and generate news articles in real-time, which allows news organizations to provide faster and more comprehensive coverage of sporting events.
In addition to financial and sports news, machine learning is also being used to generate other types of news content, such as weather reports, stock market updates, and business news.
In these cases, algorithms can analyze large amounts of data and generate reports that are more accurate and up-to-date than traditional methods.
While the use of machine learning to generate news is becoming more common, it is important to note that these systems are still in the early stages of development, and their output is not yet fully reliable.
AI algorithms are only as good as the data they are trained on, and they are susceptible to biases and errors. As a result, machine-generated news articles often require human editors to check for accuracy and fairness.
It is difficult to estimate the exact proportion of news articles generated by machine learning, as this varies widely depending on the type of news organization and its location.
However, it is estimated that a significant proportion of news content today is generated by machines, particularly in the areas of financial and sports journalism.
While the use of machine learning to generate news is becoming more common, it is important to understand that these systems are still in the early stages of development, and their output is not yet fully reliable. As a result, machine-generated news articles often require human editors to check for accuracy and fairness.