2. AI Development Challenges " 1. AI Algorithm Characteristics"

The large model struggles to achieve a balance among specialization, generalization, and cost-effectiveness.

Specialization:

The accuracy and efficiency of large models in addressing specific domain problems or tasks.

As the demand for specialization in large models increases, they require training on domain-specific data, which may lead to overfitting and reduce generalization ability.

Additionally, the increased data collection and training will raise costs and lower cost-effectiveness.

Generalization:

The ability of large models to perform on new samples outside the training dataset.

As the demand for generalization in large models increases, a more diverse and extensive training dataset is needed, along with a greater number of model parameters.

This implies increased training and usage costs, reduced cost-effectiveness, and potentially diminished specialization in handling specific problems.

Cost-effectiveness:

The input-output ratio of training and applying large models. As the demand for cost-effectiveness in large models increases, there is a need to consume fewer computational resources and costs to meet performance requirements. However, reducing resource consumption typically requires smaller models or fewer parameters, which can diminish model performance. General-purpose large models primarily aim to develop general capabilities and emphasize generalization, making it difficult to fully meet the specific needs of particular industries or organizations in terms of specialization and cost-effectiveness, leading to issues such as "hallucinations" and high costs.