Artificial intelligence (AI) has been rapidly developing in recent years, with the help of large language models like transformers. However, these models are not without their limitations. In this article, we will explore the challenges faced by AI and how embedded intelligence can help overcome them. We will also discuss the benefits of using embedded intelligence and its potential to revolutionize various industries.
Motivation: The Limitations of Existing AI Models
Currently, most AI models are trained on large datasets, but these datasets are limited in quality and quantity. As a result, AI models are not able to learn as much as they could, leading to suboptimal performance. Moreover, these models are not flexible enough to handle new tasks or data, which hinders their ability to adapt to changing circumstances.
To overcome these limitations, researchers have proposed using embedded intelligence (EI). EI is a type of AI that uses small, specialized models called foundation models (FMs) to augment existing large language models. These FMs are trained on high-quality data and can be easily adapted to new tasks or domains.
Benefits of Embedded Intelligence: Improved Performance and Flexibility
By using EI, AI systems can improve their performance and flexibility in several ways:
• Faster adaptation: FMs can quickly adapt to new tasks or data, allowing AI systems to learn more efficiently.
• Better generalization: EI can help AI models generalize better to new situations by incorporating domain-specific knowledge into the model.
• Reduced overfitting: By using smaller FMs, AI models are less likely to overfit the training data, leading to improved performance on unseen data.
Challenges in Transferring Knowledge between GAIs and EIs
Although EI has the potential to greatly improve AI systems, there are challenges to transferring knowledge between GAIs (generative artificial intelligence) and EIs. As shown in Table II, the differences in parameter size, application domain, network architecture, data size, and resource provision make it difficult for GAIs and EIs to share knowledge effectively.
To overcome these challenges, researchers are exploring ways to fine-tune GAIs using EI models. By doing so, EI can provide pre-trained foundation knowledge that can be used as a robust baseline for AI systems, accelerating learning convergence and improving inference performance.
Conclusion: Embedded Intelligence – The Key to Unlocking Artificial Intelligence’s Full Potential
In conclusion, embedded intelligence has the potential to revolutionize the field of artificial intelligence by providing a more efficient and flexible way to train AI models. By using EI, AI systems can improve their performance and adaptability, leading to better overall results. Although there are challenges to transferring knowledge between GAIs and EIs, researchers are actively working on addressing these issues. As the field of AI continues to evolve, we can expect to see even more innovative applications of embedded intelligence in the years ahead.
