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  • Mariane Bunn

AI executives chase data, but one leader sees greater value elsewhere

It is a big and colourful tree, center on the image. The tree is formed by cables, to show they are a source of information.

In a recent interview, Mark Zuckerberg, the CEO of Meta, shared his perspective on the ongoing competition among tech giants to gather data for Artificial Intelligence (AI) development. Unlike the common focus on accumulating vast amounts of data, Zuckerberg believes the real game-changer for AI will be the use of "feedback loops." These loops help AI models learn from their mistakes and improve over time by analyzing how people use them and adjusting based on that feedback.

The tech industry is currently obsessed with finding new data to feed AI models, hoping to enhance their capabilities. Some companies have gone to great lengths, including Meta's consideration of purchasing a publishing company and exploring the creation of "synthetic data." Synthetic data is artificially produced to simulate real-world data, which Zuckerberg sees as a promising area for AI development.

However, relying solely on feedback loops and synthetic data comes with its challenges. If the initial data or the feedback is flawed, it could lead AI models to reinforce their errors or biases. Despite these risks, Zuckerberg's emphasis on the importance of how AI models adapt and learn over time offers a different perspective on what will drive the future of AI technology, suggesting that the ability to evolve through feedback may be more crucial than the sheer volume of data available for training AI systems.


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