There’s no question that people all over the world are most interested in artificial intelligence more than ever before.
I mean, is there anything more fun than trying to guess what a famous couple’s baby will look like? Or hearing how the late Freddie Mercury might have played “Paint the Town Red” by Doja Cat?
However, some scientists say that by 2026, people may not have any more of the kind of data that is needed to fully teach AI.
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Losing this data, which powers powerful AI systems around the world, could slow the growth rate of AI models, especially big language models.
This loss could even change the way the AI movement moves forward.
To train reliable, high-quality AI algorithms, we need this data. For example, 570 gigabytes of text data (about 300 billion words) were used to train Chat GPT.
If there isn’t enough data to train these kinds of programs (like DALL-E, Lensa, and Midjourney), they might make mistakes or give bad results.
It is also very important that the data being used is of high quality. It is easy to find low-quality data like blurry pictures and social media posts, but they aren’t enough to train AI models that work well.
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Text taken from social media sites could also be biased or prejudiced, or it could contain false information or illegal material that could then be copied.
One reason for the search is to find high-quality content like text from books, online articles, scientific papers, Wikipedia, and some limited web content.
In a study released last year, a group of researchers said that if things keep going the way they are, we might not have this important data by 2026.
They also thought that between 2030 and 2020, there might not be any more low-quality language data.
An increasing number of computer and data experts around the world are worried about this, since AI is expected to add up to $15.7 trillion to the world economy by 2030.
Other experts are telling tech users that things might not be as bad as they seem because there are still a lot of unknowns about how AI models will work in the future.
They also say that there are a few ways to deal with possible data shortages, such as having AI writers make algorithms that use data more efficiently.
According to these experts, training high-performance models will likely need less data and computing power in the coming years. This will lower AI’s carbon footprint.
People are also talking about the idea of AI making fake data to train systems they’ll need.
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