are two notable projects in this space, with Giza focusing on ZK inference verification and RISC working on ZK training verification.
Smart Contract Layer
Once the middleware layer enables the connection between GPUs and on-chain smart contracts, the next layer focuses on the development and deployment of these smart contracts. This layer includes the development frameworks, programming languages, and tools necessary to create and execute smart contracts that utilize the power of LLMs.
Web AI Development Frameworks
These frameworks provide developers with the necessary tools and libraries to build AI-powered applications on the Web. They abstract away the complexities of interacting with LLMs and provide high-level APIs for developers to easily integrate AI capabilities into their applications. OpenAI’s GPT-3 API is a prime example of a Web AI development framework, allowing developers to access the power of LLMs through a simple API.
While existing programming languages can be used to develop Web AI applications, there is a growing need for languages specifically designed for AI development. These languages provide built-in support for AI-related tasks, such as natural language processing and machine learning, making it easier for developers to build AI-powered applications. Python is currently the most popular language for AI development, but new languages like Julia and Swift are emerging as contenders in this space.
Tools and Libraries
A wide range of tools and libraries are available to aid developers in building AI-powered applications. These include pre-trained models, data processing tools, visualization libraries, and debugging tools. These tools and libraries help streamline the development process and enable developers to leverage existing AI resources to accelerate their application development.
The final layer in the Web AI tech stack is the application layer, where AI-powered applications are built and deployed. These applications can range from chatbots and virtual assistants to recommendation systems and content generators. The possibilities are endless, and as LLMs continue to improve, we can expect to see more innovative and sophisticated AI applications being developed.
The Web AI ecosystem is still in its early stages, and the competitive landscape is rapidly evolving. While some projects have already gained traction and established themselves as leaders in their respective categories, there is still room for new entrants and innovation.
Key factors that will determine the success of projects in this ecosystem include distribution, network effects, performance, scalability, and developer adoption. Projects that can effectively address these factors and provide value to users and developers will likely emerge as leaders in the Web AI space.
The Web AI ecosystem is a complex and rapidly evolving landscape. From the infrastructure layer that powers LLMs to the middleware layer that connects them to smart contracts, and finally to the application layer where AI-powered applications are built and deployed, each layer plays a crucial role in enabling the development and adoption of AI on the Web.
As the demand for AI continues to grow, we can expect to see further advancements in the Web AI tech stack, with new projects and technologies emerging to address the evolving needs of developers and users. The potential of AI on the Web is immense, and the future of the Web AI ecosystem looks promising.