OpenLedger

What is Openledger? What do mean by Payable AI? A Deep Dive to Openledger

Created Date

12/04/2024 9:26 PM

Post Updated

12/14/2024 3:06 PM

Topic

12/14/2024 3:06 PM

Overview of Post

Openledger is a data blockchain for AI that provides decentralised trust infrastructure for creating specialized language models. By leveraging datanets to collect and curate data, Openledger enables the development of these models, which are then consumed by AI agents, chatbots, copilots, and various other applications.

In recent years, Large Language Models (LLMs) have transformed how we interact with technology. From chatbots to code assistants, they have presumably made technology simpler and easier to use. Foundational LLMs excel at general tasks, offering broad applicability across various domains. However, when it comes to specialized, industry-specific applications, their performance often falls short. This has lead to the discovery of a small and specialized alternative which excels deeply in a particular niche, These models are called as

Specialized Model qwen 2.5 outperforms models like Gemini, gpt-4o and o1()

What is

Specialised Language Models?

Specialized Language Models are compact, efficient, and trained to excel in one or more specific areas. They frequently outperform their larger counterparts in speed, performance, and accuracy within their trained domains.

To understand why specialized language models should be preferred over LLMs, Here’s a detailed comparison across various aspects:

Think of LLMs as the operating systems (OS) of AI—a foundational layer offering broad functionality and compatibility across various domains. Just as an OS serves as the baseline for running applications, LLMs provide a versatile foundation for building AI-driven solutions.

In contrast, SLMs function like apps running on this foundation. These are purpose-built systems tailored to meet specific needs.

For example:

LLMs as the OS include models like ChatGPT, GPT-4, and Claude—general-purpose models capable of handling a wide range of tasks.
SLMs as the apps could include:

- A climate analysis SLM for environmental monitoring
- A contract review SLM for legal services
- A personalized learning assistant SLM for education

Just as an OS and its apps work together, LLMs and SLMs will coexist in the AI ecosystem. LLMs will act as orchestrators, managing high-level interactions and integrations, while SLMs will delve deep into specific domains to solve problems requiring specialized expertise.

So Now openledger wants to create specialized SLMs and what is stopping it?

Challenges with Creating SLMs

1. Specialized Data:

The first challenge in creating specialized language models is obtaining specialized data. Currently, this data exists primarily within businesses and specialist organizations, but there is no dedicated platform for businesses to contribute such data. While businesses today are internet-ready, they are not yet AI-ready. This gap will become increasingly important as AI evolves into a general purpose technology.

2. Training Techniques:

Specialized Language Models are often trained using three core techniques:

Distillation: Learning from an already trained model
Pruning: Removing unnecessary parts of the data
Quantization: Reducing the precision of the model

Most commonly, these SLMs are created by fine-tuning and quantizing LoRA models. However, this approach comes with several challenges, including overfitting, catastrophic forgetting, and introducing bias. Consequently, creating effective SLMs requires skilled AI engineers and specialized, proven training and model-building techniques—a rare combination in the industry.

3. Application and Monetization:

The internet has provided businesses with various monetization opportunities through platforms like AdSense and YouTube, enabling them to contribute content and earn compensation. However, in the AI space, it has been a one-way street. The field neither creates opportunities for sharing nor contributing, as companies typically focus solely on their own profits. Currently, no platform exists that enables broader AI monetization.

At Openledger, we are solving these challenges with our payable AI infrastructure. Here's how it works:

So First, What Is Openledger?

Openledger is a data blockchain for AI that provides decentralized trust infrastructure to help users contribute data, collaborate, and build payable models.

What is payable AI?

Openledger creates a new economic model for AI by combining it with blockchain technology. By leveraging AI's unlimited potential and decentralized trust, Openledger paves the way for the world's first AI models that can actually pay you. This revolutionary approach, known as Payable AI, ensures fair compensation for data contributors and model creators alike.

Openledger’s Payable AI Infrastructure has three main layers:

1. Payable AI Models:

Openledger is working on making Specialized Language Models (SLMs) more compact and efficient, while adding the ability to explain data influence from various data points across different datanets. This means you can build an SLM by combining two or more datanets, allowing inferences to be drawn from multiple data sources with proper attribution.

Openledger’s Payable AI Infrastructure has three main layers:

How do we do it?

We call this process Proof of Attribution. It's a method that identifies data influence and provides transparent attribution to each dataset, enabling rewards, price discovery, and explainability.

so, What is a Datanet?

2. Datanet Layers:

Openledger sources its specialized data through Datanets. There will be numerous datanets, each specialized for a particular use case. The data can be in the form of text, images, audio, video, or documents.

Datanets have three main roles:

Datanet owners
Data Contributors
Validators

Some key datanets that have already started building include Data Intelligence Datanet, IP Datanet, Depin Datanet, Creator Datanet, Web3 Dev Datanet, and Web3 Alpha Datanet.

3. Blockchain Layer:

Openledger utilizes an EVM-compatible Layer 2 blockchain, where smart contracts for data contribution and attribution are available as precompiled contracts. This blockchain infrastructure opens up endless possibilities for new applications. On-chain inference, AI-specific game engines, and smart DeFi solutions can be created, showcasing the limitless potential of this technology.

Application/ Agents Laye

Openledger's vision extends beyond its Payable AI Infrastructure to encompass a robust application layer. While most AI applications today are merely wrappers for OpenAI and Meta, relying on centralized or open-source entities poses risks. services can be terminated or open-source projects can become closed-source without warning. Openledger, however, puts ownership in the hands of the people. With its decentralized trust model, users can rely on it completely.

Openledger is committed to simplifying the development process. We envision a diverse range of applications including chat assistants, copilots, trading engines, game engines, and image/video generators with IP-enabled data. This layer enables applications to track data attribution, providing better transparency throughout the inference process while establishing a decentralized trust mechanism to reward data contributors.

Final Thoughts

Openledger's noble cause is building AI with fair models that reward the actual people who contributed, instead of funneling everything to a centralized organization. This new opportunity is similar to what YouTube created for content creators. In its early version, YouTube had 10% quality videos and 90% junk, but when they introduced a reward mechanism, these numbers changed drastically. Today's AI landscape is similar—while it's focused on serving information seekers, Openledger will pave the way for businesses to enter AI without hesitation. With its Payable AI Infrastructure, Openledger will create new opportunities in the AI landscape.

Join our community today to learn more about Openledger and how it benefits you.