The New Dynamics Amid OpenAI‘s Dominance
Generative AI advancements have brought the internet to a pivotal moment of change. In the near future, virtual assistants powered by large language models (LLMs) could serve as the universal gateway to the internet. As a result, Web3 projects will face crucial decisions regarding their approach to consumer engagement.
On one end of the spectrum, companies can delegate control of their consumer interface to an LLM-powered virtual assistant or other conversational AI, utilizing APIs like plug-ins. For instance, OpenAI's ChatGPT plug-in empowers consumers to purchase and order through third-party platforms such as OpenTable and Instacart. It is likely that other providers of LLMs will follow a similar path.
Conversely, companies can opt to retain control of their interface by employing a customized generative AI model on their own websites and applications. The implementation may vary, as businesses can choose to either build their own model or fine-tune an existing one. Bloomberg, for instance, has developed its own model that it intends to integrate into its services and features. Expedia has incorporated OpenAI's model into its own application, allowing users to plan trips while remaining on the company's website and utilizing the capabilities of ChatGPT.
“OpenAI's GPT-3.5: Making API Requests is a Breeze, but Can it Tackle Web3-related Queries?
RSS3’s Devotion in In-house AI Models
Since November 2021, RSS3 has been actively exploring AI-assisted solutions. Capitalizing on the ongoing AI trend, specifically, the LLM trend, we want to seize the opportunity to further expand our expertise in this domain. In doing so, we have made significant progress in developing a range of in-house hybrid AI models specifically tailored to address the unique requirements of the Web3 context. This hybrid AI solution is proven to be highly efficient, we will share the details in a separate article.
Leveraging RSS3's extensive data indexing capabilities, our focus has been on two key developments: the AI-training Open Platform and Model 1 - the inaugural Web3 AI assistant. These advancements adhere to rigorous standards encompassing general tasks such as trend prediction, on-chain data analysis, and cross-chain data retrieval.
“An in-house AI model refers to an artificial intelligence model that is developed and deployed within an organization or company, as opposed to using pre-trained models or cloud-based AI services. It involves building a customized AI solution tailored to the specific needs and requirements of the organization.
We Do AI the RSS3 Way - From Data Indexing to Data Training
Our expertise in on-chain data indexing serves as the bedrock for applying cutting-edge AI techniques; RSS3's in-house AI model offers a robust analytical framework, accurate prediction capabilities, and efficient retrieval of both on-chain and off-chain data.
RSS3's in-house AI model, Model 1, is a hybrid solution that combines multiple AI models and algorithms, each meticulously developed to leverage our expertise in on-chain data indexing and enhance the overall user experience. Model 1 serves as a Web3 AI assistant to support users navigating the complexities of Web3 environments while providing effective AI solutions for data analysis. By feeding indexed data into different models, Model 1 is able to perform:
- Analytical Task: We have built an analytical component that enables comprehensive data analysis. This empowers users to extract valuable insights and make informed decisions based on the vast amount of data available on the RSS3 data network. By employing data analysis, we provide robust solutions to cater to diverse user requirements, such as providing data-driven insights.
- Prediction Task: We have integrated a prediction task into our in-house model. By training our model on the extensive data available on the RSS3 Network, we can generate accurate predictions and forecasts related to the crypto market (they, of course, do not constitute financial advice). This enables users to anticipate trends, identify patterns, and assist in decision-making.
- Information Retrieval: RSS3 has been working on information dissemination since its inception, enabling seamless access to Open Web data. Our model is designed to 1) efficiently retrieve relevant information from the blockchain and external sources and 2) provide concise and meaningful summaries when required. This comprehensive approach ensures users can effortlessly access the information they need, providing a user-friendly and streamlined experience.
The Motivation Behind Developing In-house Models is Multifaceted and Strategic-driven
Cost-effectiveness and Superior Performance in Specialized Domains
Utilizing pre-trained models or cloud-based AI services often entails considerable costs and technical complexities. The expenditure involved in running and maintaining such services can be prohibitive, straining the financial resources of Web3 projects. In contrast, in-house models offer a more cost-effective solution when it comes to running services. Moreover, these custom models have the potential to yield superior results by addressing the intricacies and nuances of specialized domains. We encourage Web3 projects to reduce their dependence on third-party services and instead focus on developing models aligned with their precise requirements. This promotes greater self-sufficiency and drives innovation within the industry.
Reducing Reliance on Centralized Models
Recent discussions have raised concerns about the reliance on centralized AI models, such as OpenAI's GPT3/GPT4. The potential risk of these models becoming unavailable or unaffordable highlights the need for self-sufficiency and innovation. By prioritizing the development of RSS3's in-house AI models and algorithms, we’d like to minimize reliance on large tech companies for AI capabilities. This ensures our long-term competitiveness and empowers us to innovate independently, safeguarding against the uncertainties associated with relying solely on external providers.
Advancing Team Expertise
One of RSS3's core objectives is to excel in exploring innovative ways to utilize the data we have indexed. We aim to leverage this emerging technology by venturing into multiple AI models to enhance our team's proficiency in delivering innovative and superior Web3 data solutions. This strategic move allows us to capitalize on the current momentum and expand our expertise within the blockchain data domain.
