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.

Outlines

00:00

🤖 Evolution of AI: From Traditional to Generative

The paragraph discusses the evolution of AI, contrasting generative AI with traditional AI systems. Traditional AI relied on a repository of organized data, an analytics platform for model building, and an application layer for implementation. A feedback loop was crucial for AI to learn from its predictions and improve over time. Generative AI, however, uses vast amounts of data from various sources, not just a company's repository. It employs large language models (LLMs) that are then fine-tuned for specific use cases through prompting and tuning. This new approach requires a different architecture due to the massive scale of data and models involved.

05:05

🌐 The Architecture of Generative AI

This paragraph delves into the architecture of generative AI, emphasizing its difference from traditional AI. Generative AI starts with global data rather than internal company data, using large language models that are initially very general. These models are then tailored to an organization's specific needs through a prompting and tuning process. The application layer in generative AI is similar to traditional AI, where AI is consumed for specific purposes. The feedback loop in generative AI typically feeds back into the prompting and tuning layer, as the models are often external to the organization. The paragraph highlights the necessity for a new architecture due to the unprecedented scale of data and models in generative AI.

Keywords

💡Generative AI

Generative AI refers to a type of artificial intelligence that can create new content based on existing data. It is a significant advancement from traditional AI as it involves creating new outputs rather than just predicting or analyzing existing data. In the video, generative AI is contrasted with older AI models, highlighting its ability to leverage vast amounts of data to produce original content, such as text or images.

💡Repository

A repository in the context of the video is a storage location for data and information, which can include data tables, images, documents, and more. It serves as a foundational component in traditional AI setups where historical information is kept and used for analysis. The video explains that generative AI diverges from this by starting not with internal company data but with broader data sources.

💡Analytics Platform

An analytics platform is a software system used to create models and analyze data. In the video, examples like IBM's SPSS Modeler or Watson Studio are given. These platforms are integral to the traditional AI process where data from repositories is moved here to build predictive models, which are then used in applications to make informed decisions.

💡Application Layer

The application layer is the topmost level of a system where the AI models are implemented to perform specific tasks. In the video, it is mentioned in the context of a telco company using AI to predict customer churn and then taking action to prevent it. This layer is crucial as it is where AI interacts with the real world to provide value.

💡Predictive Analytics

Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The video discusses how traditional AI systems often operate within this framework, using models to predict events like customer cancellations, which is a step before becoming generative AI.

💡Feedback Loop

A feedback loop in AI is a process where the system's output is used to refine its future performance. The video explains that in traditional AI, a feedback loop allows the system to learn from its predictions, improving the accuracy of its models over time. This is a critical component in the evolution from predictive analytics to AI.

💡Large Language Models (LLMs)

Large Language Models are a type of generative AI that use vast amounts of text data to understand and generate human-like text. The video emphasizes that these models are trained on a global scale of information, which gives them broad knowledge but may lack specific business nuances. They form the basis of generative AI's capabilities.

💡Prompting and Tuning

Prompting and tuning is a process in generative AI where general large language models are adjusted to fit specific use cases. The video describes how these models, while knowledgeable, need to be fine-tuned with prompts to address the unique requirements of a business, such as understanding the particular reasons why a telco's customers might churn.

💡Data

Data in the video is portrayed as the fuel for AI systems. Traditional AI relied on internal company data, while generative AI leverages a much broader scope of data from various sources. The scale and diversity of data are highlighted as key factors that differentiate generative AI from its predecessors.

💡Architecture

Architecture, in the context of the video, refers to the structural framework of AI systems. The shift from traditional to generative AI necessitates a new architecture due to the vast amounts of data and the complexity of models involved. The video suggests that generative AI's architecture is fundamentally different, requiring a different approach to handling and processing information.

Highlights

Generative AI differs from traditional AI by utilizing large-scale data and advanced language models.

Traditional AI relies on a repository for historical data and an analytics platform for model building.

The application layer in traditional AI is where models are used to make predictions and take actions.

A feedback loop in traditional AI enables the system to learn from its predictions and improve over time.

Generative AI starts with global data, not just organizational data, providing a broader context.

Large language models in generative AI are powerful but may lack specific business nuances.

Prompting and tuning are used to make large language models specific to a business's use case.

Generative AI's architecture is fundamentally different, requiring a new approach to data and model handling.

The data and models in generative AI are too large for traditional repositories, necessitating a new architecture.

Generative AI's feedback loop primarily feeds back into the prompting and tuning layer.

The size and quantity of data and models in generative AI are significantly larger than in traditional AI.

Generative AI represents a paradigm shift in how AI is developed and utilized.

The fundamental architecture of AI has evolved to accommodate the vast amounts of data and complex models.

Generative AI's approach to learning from mistakes and successes is more dynamic and continuous.

The practical applications of generative AI are expanding, impacting various industries and business functions.

Generative AI's ability to learn from a global dataset offers insights that are more diverse and comprehensive.

The future of AI is likely to be dominated by generative models due to their adaptability and scalability.

Transcripts

So generative AI is all the rage,

but one question I get quite frequently

is how does generative AI differ from AI that we were doing

5, 10, 20, maybe even 30 years ago?

To understand that, let's take a look

at AI the way existed before generative AI.

so typically the way that it worked

is you start it off with a repository.

And a repository is exactly what it sounds like.

It's just where you keep all of your information

and they can be, you know, data and tables, rows and columns.

It can be images, it can be documents.

It can really be anything.

It's just kind of as an organization where you keep all of your

historical information or stuff.

The second part is what we call an analytic analytics platform.

And in the IBM world,

an example of a analytics platform is SPSS modeler

or Watson Studio.

And then the third component

is the application layer.

So let's say you're a telco.

You have all your information about the customers in the repository.

And let's say you want to know which customers are likely to churn or cancel their service.

So you would take that information in the repository,

move it into an analytics platform.

Inside the analytics platform you would build your models.

In this case, who is and isn't likely to churn or cancel their service?

And then once you have those models built,

you would put them in some kind of application.

And the application would just try, is where you try to

prevent those people from canceling.

So for example, if somebody is likely to cancel,

maybe you reach out to them and try to convince them not to

or give them some kind of benefit so that they stick around as a customer.

But this in itself, I wouldn't call this AI.

This is more of a predictive analytics or a predictive model.

To make this AI, you have to provide a feedback loop.

And a feedback loop allows you to automate the process.

So, for example, you know, you're a telco

and, you have your information on your customers,

you figure out who's going to cancel.

You take action through an application to try to keep them from canceling.

But your models here are sometimes they're right, or sometimes

sometimes they're right, sometimes they're wrong.

What the feedback loop allows you to do is to learn from that experience.

So if there are situations where you predicted somebody was going to cancel and they didn't,

maybe you can drill in and make your models better

so that you don't make that same mistake a second time.

So think of it like this:

Fool me once, shame on you.

Fool me twice, shame on me.

That's what you want your AI to do.

You want your AI to learn from its previous mistakes

and its previous successes, too.

And the feedback loop allows you to do that.

So this is the way that it always existed.

I've been in this business for over 30 years, and, this predates me.

But with generative AI, this whole paradigm has changed.

The whole fundamental architecture

and the way that we do things is different now.

With generative AI you start off with data,

not from your organization, not from a repository

inside the walls of your company.

But you start off with data from Earth.

Okay, so maybe not Earth, right?

But you start with this massive, massive, massive quantity of information.

Information about everything.

That information then is used by

large language models.

But these large language models are

they're very powerful, they're very big

and they're remarkable, to be honest.

But they - a lot of times they don't have the specifics that you need to guide you in your business.

So, for example, a large language model might know in general

why people cancel, a particular service if you're a telco.

but they wouldn't have the nuances and the idiosyncrasies

of why your specific customers cancel.

That's when you use what's called prompting and tuning.

So the prompting and tuning layer,

the prompting and tuning layer

is where you take the large language models,

which are very general models,

and make them specific to your use case.

So going back to our telco who's trying to deal with customer churn,

they would have this model that's built

not just on customer churn or your customers,

but built on massive quantities of information that have everything in it.

LLMs are derived from that massive quantity of information

then you use this prompting and tuning layer to try to fine tune

those models so that they're specific to your organization.

And then the final part is you have an application layer,

just like you do with traditional AI.

And the application again is is where you take the AI so that it's consumed

so that it's going to fulfill its specific purpose.

And also, just like with traditional AI, you also have a feedback loop,

but the feedback loop typically just goes back to the prompting and tuning part of it,

because these are typically outside of your organization.

So there you have it.

That's why large language models are generative.

AI is different because the fundamental architecture is different.

And primarily, it has to do with the size and the quantity,

both of the data coming in, and the models being built.

And these models and this data is way too big for any organization to hold in their repository.

That's why we need a fundamentally different architecture.

Thanks so much for your time. I hope this was helpful.