GENERATIVE AI 4 LAYERS

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Content level: Intermediate
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Generative Artificial Intelligence (Gen AI)

Enter image description here The hype surrounding Generative Artificial Intelligence (Gen AI) is currently at its peak, accompanied by a fair share of confusion. Questions about what to study, how to approach it, and where the most job opportunities lie are at the forefront of our minds. Personally, I’m a proponent of first-principles thinking, which involves deconstructing a problem into its smallest logical components. I apply this approach to understanding Gen AI.

Gen AI can be broken down into four distinct layers:

The foundational layer comprises the hardware necessary for training models, such as silicon chips produced by companies like AMD and NVIDIA. Next, we have the Language Learning Model (LLM) models that are trained and executed on these chips. Examples of such models include those developed by OpenAI and Anthropic. Following that is the infrastructure layer, which encompasses providers that offer convenient solutions for consuming, hosting, training, and deploying models. AWS is a prime example of such a provider, offering managed services like Amazon Bedrock for hosting pre-trained models and Amazon EC2 for provisioning VMs for model training. Finally, we reach the application layer, where these trained models are utilized. Examples of applications include Adobe Firefly, LLM-powered chatbots, and LLM-driven travel agents. Now, here’s the crucial aspect: as we ascend from the bottom to the top of these layers, the learning curve becomes less steep, and the barrier to entry for new market players decreases. Developing new hardware, such as chips, demands substantial investments, making it challenging for newcomers to enter the market. Consequently, the most promising opportunities exist within the top two layers.

For those already familiar with cloud computing, integrating Gen AI with existing knowledge can significantly enhance their value. For instance, individuals working in DevOps can delve into MLOps, while those proficient in Kubernetes or Serverless architectures can explore integration with Gen AI technologies. Similarly, application developers can leverage managed LLM services to augment functionality. Personally, I’m dedicating most of my efforts to mastering this uppermost layer.

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EXPERT
published 2 months ago1113 views
4 Comments

Great breakdown of Gen AI layers, it’s a helpful guide for anyone navigating their career in the evolving AI landscape!

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EXPERT
replied 2 months ago

Taking into consideration these 4 layers, which layer facilitates writing prompt using Chat-GPT (from OpenAI) to arrive at wonderful clues/answers on solution to real life problems ? Is OpenAI providing application service in collaboration with any other company for writing answers to queries by the users?

Subrata
replied a month ago

The layer that facilitates writing prompts using ChatGPT (from OpenAI) to arrive at wonderful clues/answers on solutions to real-life problems would primarily fall under the "Application Layer."

The Application Layer is where the interaction between the user and the AI model occurs. In this case, users interact with ChatGPT by inputting prompts or queries, and ChatGPT generates responses based on its training data and underlying algorithms. This layer is responsible for understanding user inputs, generating contextually relevant responses, and providing valuable insights or solutions to real-life problems.

Regarding your second question, OpenAI provides APIs and tools for developers to integrate GPT models like ChatGPT into their applications or services. OpenAI does not directly provide application services for writing answers to user queries, but rather offers the technology and infrastructure for developers to build such applications themselves.

However, there might be companies or organizations that have integrated OpenAI's technology into their products or services to provide AI-powered responses to user queries. These collaborations could involve using OpenAI's APIs or licensing agreements to leverage ChatGPT for specific applications, such as customer support chatbots, virtual assistants, or content generation tools.

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EXPERT
replied a month ago

Nice and clear explanation of some very common questions that we may have while enjoying the fruit of such great technological advancement.

Subrata
replied a month ago