AI models are increasingly growing in size, with the belief that bigger is better. However, research shows that larger models are not always more effective. For example, a small model might perform poorly due to noise (uncategorized data), causing it to hallucinate or make incorrect predictions. Simply increasing the model’s size doesn’t resolve this issue; it may just magnify the noise, making the model less accurate.

There are also significant concerns associated with scaling AI models. Computational resources become a major challenge, as larger models require more processing power, generate excess heat, and consume substantial amounts of water and energy. This not only drives up costs but also poses environmental risks. Furthermore, data availability becomes problematic; larger datasets often introduce more biases, leading to less reliable results.

The risk of overfitting also increases with larger models, as they become more generalized with vast datasets, potentially losing specificity. As the model’s complexity grows, it becomes harder to maintain accountability, transparency, and trust, which are crucial for ethical AI development.

Moreover, the widespread use of larger AI models can result in job displacement and raise security and privacy concerns. Companies may find larger models appealing, but they come with significant sustainability challenges and potential legal risks.

Source

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Written by

Emily M. Bender

Timnit Gebru

Angelina McMillan-Major

Shmargaret Shmitchell