The race for data in the development of artificial intelligence is heating up, with tech giants like OpenAI, Google, and Meta going to great lengths to obtain more valuable information. As online data becomes increasingly crucial for the advancement of A.I., companies are using up publicly available resources faster than new data is being produced.
One of the key challenges in the development of artificial intelligence is the need for vast amounts of data. A.I. models become more accurate and powerful with more data, similar to how a student learns by reading more books and essays. Large language models, such as OpenAI’s GPT-3, have been trained on hundreds of billions of tokens, with more recent models trained on trillions of tokens.
However, the availability of high-quality digital data is predicted to be exhausted by 2026, leading tech companies to explore new ways to obtain data. OpenAI, Google, and Meta have been using tools like converting YouTube audio into text and revising privacy policies to access more data for their A.I. models.
In the midst of this data race, one potential solution being explored is the use of “synthetic” data. This involves using A.I. models to generate new text that can be used to improve A.I. systems. While synthetic data has the potential to create more data for development, there are risks involved as A.I. models can make errors that may be compounded by relying on such data.
As the demand for data continues to grow in the development of artificial intelligence, tech companies are facing ethical and legal challenges in their quest for more information. The future of A.I. development may rely on finding a balance between utilizing existing data and creating new synthetic data to fuel innovation in this rapidly evolving field.
