If technically we have the know-how to make more powerful AIs, what prevents us from just scaling up neural networks? Why doesn't OpenAI, Google, or anyone else make an AI with 10 trillion parameters?

While it is technically possible to scale up neural networks and create more powerful AI models with an increasing number of parameters, there are several factors that prevent us from simply scaling up without limitations. Here are a few reasons why AI developers like OpenAI, Google, and others may not create an AI with an extremely large number of parameters, such as 10 trillion:

1. Computational limitations: Larger neural networks require exponentially more computational resources, including processing power, memory, and energy consumption. Training and running such massive models would be extremely resource-intensive and may not be feasible with existing infrastructure.

2. Diminishing returns: While increasing the number of parameters in a neural network can improve performance to some extent, there is a point of diminishing returns. Beyond a certain size, the gains in performance become marginal compared to the increased computational costs.

3. Overfitting and generalization: Larger models run the risk of overfitting, where the model becomes too specialized and performs poorly on unseen data. It becomes harder to generalize and adapt to new situations, leading to reduced performance in real-world scenarios.

4. Data limitations: Training massive models requires vast amounts of high-quality training data. Generating or curating such large-scale datasets with sufficient diversity and accuracy can be a challenging and time-consuming process.

5. Ethical considerations: AI models with an enormous number of parameters raise ethical concerns related to fairness, transparency, and interpretability. As models become more complex, understanding and explaining their decision-making processes become more difficult, which can hinder their responsible deployment.

6. Environmental impact: The energy consumption associated with training and running large-scale AI models is substantial. Creating AI models with billions or trillions of parameters can significantly contribute to carbon emissions and environmental degradation.

While scaling up AI models can offer some benefits, striking a balance between model size, computational resources, performance, and ethical considerations is crucial. AI researchers and developers constantly explore ways to optimize and improve models without disregarding practical limitations and ethical implications.