The Evolution of AI: Traditional AI vs. Generative AI

Summary

This script explores the evolution of AI, contrasting traditional predictive analytics with modern generative AI. Traditional AI relied on internal data repositories, analytics platforms, and applications with feedback loops for continuous learning. Generative AI, however, leverages vast external data, large language models, and a prompting and tuning layer to customize general models for specific business needs. The script highlights the shift to a new architecture due to the massive scale of data and models, which traditional systems cannot accommodate.

Takeaways

• 📚 Traditional AI relies on a repository of organized historical data specific to an organization.
• 🔍 An analytics platform is used to build predictive models based on the data in the repository.
• 🛠️ The application layer in traditional AI is where models are applied to perform tasks such as customer retention.
• 🔁 A feedback loop in traditional AI allows for continuous learning and improvement of models.
• 🚀 Generative AI shifts the paradigm by starting with vast amounts of data from diverse sources, not just organizational repositories.
• 🌐 Large language models (LLMs) in generative AI are powerful and can process massive quantities of information.
• 🔧 Prompting and tuning are used to tailor general LLMs to specific business use cases, like understanding customer churn.
• 🔄 The feedback loop in generative AI typically feeds back into the prompting and tuning layer to refine the models.
• 🏢 The architecture of generative AI is fundamentally different, requiring new approaches to handle the scale of data and models.
• 🌟 Generative AI represents a significant evolution in AI capabilities, moving beyond traditional predictive analytics.

Q & A

What is the main difference between generative AI and traditional AI?

The main difference lies in the data source and architecture. Traditional AI uses data from within an organization, while generative AI leverages massive amounts of data from various sources, often outside the organization, and uses large language models for processing.

What are the three components of traditional AI systems as described in the script?

The three components are the repository, which stores all the information; the analytics platform, which is used to build models; and the application layer, where the AI is used to take action based on the models.

How does a feedback loop enhance traditional AI systems?

A feedback loop allows AI systems to learn from their predictions, improving the models by adjusting them based on whether they were right or wrong in the past, thus preventing the same mistakes from happening again.

What is the role of the prompting and tuning layer in generative AI?

The prompting and tuning layer is used to make the general knowledge from large language models specific to a particular use case or organization, fine-tuning the AI to better suit the unique requirements and nuances of the business.

Why is the architecture of generative AI different from traditional AI?

Generative AI requires a different architecture because it deals with much larger quantities of data and more complex models that are beyond the capacity of traditional repositories within organizations.

How does generative AI utilize large language models?

Generative AI uses large language models to process vast amounts of data from various sources. These models are then fine-tuned through prompting and tuning to be specific to the needs of the organization.

What is the purpose of the application layer in both traditional and generative AI?

The application layer is where AI is consumed and put to use to fulfill specific purposes, such as preventing customer churn in the example of a telco company.

How does the feedback loop in generative AI differ from that in traditional AI?

In generative AI, the feedback loop typically goes back to the prompting and tuning layer to further refine the models, as opposed to directly improving the models within an organization's repository.

Why might a large language model not have the specific details needed for a business?

Large language models, while powerful, are trained on general data and might lack the specific nuances and idiosyncrasies of a particular organization's customers or data.

What is the significance of the size and quantity of data in generative AI?

The size and quantity of data in generative AI are significant because they allow for the creation of more accurate and nuanced models, but they also necessitate a fundamentally different architecture to handle the data's scale.