We’re Running Out of Data to Train Artificial Intelligence

As the field of artificial intelligence rapidly advances, the demand for data to feed AI models continues to grow. Despite their limited consciousness, AI models are becoming increasingly intelligent and seem to be developing in a similar way to human intelligence. However, there is a significant difference between the two: AI models can learn at a much faster rate than humans. Although we remind ourselves that we cannot know everything, it seems that someday AI models might be able to.

Researchers are warning that data exhaustion will be a major bottleneck in the future of AI

AI is growing at an unprecedented pace, and its intelligence is increasing daily. However, the amount of knowledge that we can teach it is limited. Researchers have conducted studies to predict when data exhaustion might occur in the future. According to their research, high-quality language data, such as research papers and articles, may face data exhaustion between the years 2030 and 2040. If AI companies and engineers decide to incorporate low-quality data into AI, it might delay the exhaustion of language data. Additionally, the exhaustion of visual data is expected to occur around the 2050's.

The major bottleneck for AI's improvement in the future will be its knowledge improvement, as AI models are currently learning and growing at a rate faster than any organism in the world. However, we must keep in mind that this research paper was published in October 2022, and current predictions may differ.

What does all this mean?

In summary, AI will continue to improve and increase in speed until it reaches the peak of the data we have created as humans so far. However, the AI models' ability to create their own knowledge and learn at an even faster pace might change this prediction.

Source:

• https://arxiv.org/pdf/2211.04325.pdf

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