Understanding Generative AI vs Traditional AI
The Rise of Generative AI
Generative AI is currently dominating discussions in the tech space, but how does it differ from the AI that has been in use for the past few decades? To clarify this distinction, let’s explore the conventional model of AI before the advent of generative technologies.
Traditional AI: The Components
Traditional AI systems typically functioned through three main components:
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Repository
A repository is a structured storage system where information is organized into data tables, rows, columns, and can include various formats such as images and documents. This serves as a historical repository for all relevant data.
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Analytics Platform
For example, in the IBM ecosystem, platforms such as SPSS Modeler or Watson Studio serve as analytics tools. This is where data from the repository is analyzed, and predictive models are built.
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Application Layer
The application layer interacts with users and implements the findings from the analytics. For instance, a telecom company might use this data to identify customers at risk of churning and create strategies to retain them.
However, this predictive approach alone does not constitute true AI; it’s primarily predictive analytics or modeling.
The Role of Feedback Loops
A feedback loop is essential to transform predictive analytics into genuine AI. This mechanism allows the system to learn from its outcomes—both successes and failures—by adjusting models based on customer behavior and responses. The saying goes, “Fool me once, shame on you; fool me twice, shame on me.” This concept encapsulates the goal of a robust AI model: to continuously improve its predictions.
Generative AI: A New Paradigm
Now that we’ve established how traditional AI functions, let’s look at the transformative impact of generative AI. The architecture of generative AI differs significantly:
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Data Sourcing
Unlike traditional AI that starts with proprietary organizational data, generative AI utilizes vast datasets from across the globe. This includes an enormous repository of information that is not confined to any one company.
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Large Language Models (LLMs)
These models are designed to digest and analyze extensive information, offering insights into general trends. However, they may lack the specificity required by individual businesses, such as the unique reasons behind customer churn.
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Prompting and Tuning
This layer is crucial for customizing large language models to suit the unique needs of an organization. By refining the model through prompting and tuning, businesses can better align the general insights with their specific operational contexts.
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Application Layer and Feedback Loop
As with traditional AI, there’s an application layer to facilitate user interaction. However, the feedback loop in generative AI primarily feeds back to the prompting and tuning layer, allowing for ongoing customization and learning.
Conclusion: The Future of AI
The key takeaway is that the shift to generative AI represents a fundamental change in architecture, marked by immense datasets and robust language models. This evolution in AI technology enables organizations to access knowledge and insights that far exceed the limits of traditional data repositories. Understanding these distinctions is crucial for businesses looking to leverage AI effectively.
Thanks for taking the time to explore this topic with me. I hope this article has been informative!