Closed 2Jus2 closed 8 months ago
Unable to find a solution for the AI model training, requires to find an alternative way to research on
In the process of developing a local AI model for AI-Enhanced applications, we encountered a critical challenge that has proven difficult to surmount. This challenge primarily concerns the feasibility and appropriateness of constructing a local AI model in alignment with our project's goals. The issues we faced can be categorized into several key areas:
Data Availability and Quality:
Our research indicated that the AI model in question requires vast amounts of data to train effectively. Unfortunately, our current resources do not allow access to such a magnitude of high-quality data, which is crucial for the model to learn and make accurate predictions or decisions. Ethical and Safety Considerations:
The use of large-scale data introduces significant ethical and safety considerations, particularly concerning privacy and data protection. Ensuring compliance with data protection laws and ethical standards is paramount, yet the sheer volume of data needed poses substantial challenges in this area. Scale and Repetitiveness of Task:
The model’s applicability often relies on tasks that are large-scale and repetitive, making it suitable for certain types of projects. However, our assessment revealed that the specific requirements of our project do not align with the characteristics that the model is optimized for, reducing its effectiveness in our context.
When determining the applicability of AI-enhanced AI models, it is critical to pay particular attention to the availability and quality of data, as high-quality data is fundamental to ensuring model accuracy and validity.
Description Determining the applicability of an AI model for AI-Enhanced involves assessing various factors, including the availability and quality of data, the ethical and safety considerations of using the data, the scale and repetitiveness of the task, and the potential for real-world application and outcomes.