Effective Use Cases for Machine Learning and Large Language Models

Traditional Machine Learning vs. Large Language Models: Finding the Perfect Fit

Author: Ammar Malik

22 May, 2024

As the field of artificial intelligence continues to evolve, the debate around the appropriate use cases for traditional machine learning versus the more advanced large language models (LLMs) has become increasingly relevant. While LLMs have demonstrated remarkable capabilities in natural language processing and generation, there are still many applications where traditional machine learning approaches remain the better choice.

In this blog post, we will explore several examples of when traditional machine learning should be preferred over LLMs, highlighting the unique strengths and limitations of each approach. By understanding the nuances of these AI techniques, we can ensure that the right tools are applied to the right problems, leading to more effective and efficient solutions.

Key Differences in Use Cases

Traditional Machine Learning can and most probably will still be used for a long time. This is due to a variety of reasons, one is that machine learning algorithms are trained to accomplish a single task, this includes regression, image classification, or simply structured data analysis. LLMs are usually trained on unstructured data relating to language so it is more tailored for more linguistic patterns and context.

However, LLM is more adaptable and can excel at zero-shot learning. This means they can adapt and accomplish new tasks without being trained on them. LLMs can also remember their outputs, this contextual understanding offers more flexibility. Traditional machine learning algorithms also have a hardware advantage due to requiring less computational resources to run inference.

Use Cases

Basic Calculations

For numeric data analysis and calculations, both ML and LLM can accomplish this task. However, as mentioned earlier, LLMs require much more computational complexity as such ML algorithms would be the better pick for this kind of technology.

Fraud Detection

Financial institutions can leverage Machine Learning to detect instances of fraud. Depending on the scale they are running inference, they are able to leverage machine learning to make quick predictions on whether or not a transaction is fraudulent or not.

Personalized Recommendations

Since LLM can understand and store context they are better are providing additional recommendations to improve customer experiences and help drive sales further. Leveraging shared user-item data and overlapping contexts across different recommendation tasks, making the process more transparent and allowing users to better understand and engage with the recommendations, and continuously evolving and improving by incorporating user feedback to deliver increasingly personalized and delightful experiences over time, ultimately leading to enhanced customer engagement and sales.

Customer Support

Large language models can significantly enhance customer service automation by enabling instantaneous, consistent, and accurate responses to customer inquiries. LLMs can analyze customer questions in real time and generate relevant, human-like answers immediately, reducing wait times and improving the overall customer experience. These automated systems can operate 24/7, providing assistance to customers in different time zones without the need for shifts or breaks.

Conclusion

In conclusion, while large language models have undoubtedly made impressive strides in natural language processing and generation, traditional machine learning approaches still have a vital role to play in the world of artificial intelligence. For applications requiring rapid inference on structured data, such as fraud detection or basic calculations, ML algorithms remain the more efficient and practical choice. Additionally, LLMs in healthcare are being explored for tasks like medical record summarization and patient interaction, traditional ML models are crucial for tasks like diagnostic imaging and predicting patient outcomes, where speed and accuracy are paramount.

However, LLMs shine when it comes to tasks that demand deeper language understanding and generation, like personalized recommendations and customer support. As the field of AI continues to evolve, it will be crucial for developers and researchers to carefully consider the unique strengths of each approach and apply them to the problems they are best suited to solve. By leveraging the complementary capabilities of ML and LLMs, we can create AI systems that are more powerful, versatile, and effective than ever before. The future of AI lies in striking the right balance between these two powerful techniques, and the possibilities are truly exciting.