Introduction

As confirmed by official sources, the AI initiative QnA3.AI has entered into a strategic alliance with Solana. By the time of reporting, they have successfully launched a blockchain contract on the Solana network. Together, they are set to create an app custom-designed for Solana Saga. This partnership aims to harness the power of QnA3.AI’s combined AI and Decentralized Personal Identification (DePIN) technologies to bolster Solana’s ecosystem, promising to drive significant and valuable user engagement.

Evolution of QnA3.AI

Launched in January 2023, the QnA3.AI team has swiftly propelled its product from inception to a significant milestone in just a year’s journey:

• By June 2023: QnA3.AI rolled out its Q&A feature, quickly amassing over 10,000 users.
• By September 2023: The platform introduced an intent-driven Telegram bot, catapulting its user base to over 300,000.
• By December 2023: The introduction of a data mining feature saw QnA3.AI’s daily active users dominate the BNB Chain as number one for an extended period, pushing the user count past the 2 million mark. As of now, QnA3.AI’s user base has impressively surged past ten million.

What is QnA3.AI?

Source: qna3.ai

QnA3.AI stands as the premier AI-powered Web3 knowledge platform, serving as a formidable force in building and shaping the AI + DePIN ecosystem. It offers comprehensive services spanning the entire lifecycle, including information, asset, and rights management. By leveraging Retrieval-Augmented Generation (RAG) technology alongside the comprehension, pre-training, scalability, and inferencing capabilities of Large Language Models (LLMs), QnA3.AI significantly enhances the efficiency and accuracy of information retrieval and generation. Furthermore, the team is pioneering the integration of AI with trading and decentralized personal identification (DePIN) in the realms of asset and rights management, pushing forward the envelope in decentralized machine learning practices.

Position of QnA3.AI

Per the official description, QnA3.AI is positioned as an indispensable AI Agent for those navigating the Web3 world, offering comprehensive lifecycle and scenario management for Web3 integration. The team adeptly merges state-of-the-art data engineering with the sophisticated analytical prowess of Large Language Models (LLMs), sifting through and reassembling vast datasets. This process ensures not only the delivery of precise information but also provides deep analytical insights.

Information Management AI + Research

As a frontrunner in the Web3 sphere, QnA3.AI leverages its AI-enhanced search capabilities, expert data, and proprietary knowledge to furnish users with instant and trustworthy answers. Unlike traditional search engines, QnA3.AI delivers accurate, streamlined responses, adept at dissecting queries and offering pertinent feedback. With a solid grasp of LLMs and extensive technical expertise, QnA3.AI seamlessly merges LLMs with search functionalities. This synergy, coupled with its exceptional product innovation, engineering prowess, and swift adaptation, cements its leadership status. At its core, QnA3.AI maximizes Retrieval-Augmented Generation (RAG) technology to cater to the Web3 knowledge’s demand for expertise, immediacy, and question-answer relevance. Adopting a Meta learning approach, QnA3.AI positions RAG at the heart of its AI + Research, blending external knowledge bases with inherent model knowledge to stay at the forefront in retrieval efficiency, speed of response, and data quality.

Retrieval-Augmented Generation (RAG) encompasses both the retrieval and generation stages.

Retriever

The retrieval component is structured around a Query Encoder and a Document Index. It employs two distinct BERT models to encode queries (q) and documents (z) into q(x) and d(z) formats, respectively. Following this, it utilizes a Maximum Inner Product Search (MIPS) algorithm to identify the document with the highest inner product. This document, along with the original query, is then fed into the generation segment for further processing.

Generator

At this stage, the generator constructs the final answer, drawing on the retriever’s summarized output. A large-scale model is employed to estimate the likelihood of the appearance of the next word based on the provided input, selecting the word with the highest probability for generation. There are two strategies for calculating this generation probability: 1) RAG-Sequence, which involves making predictions based on a single document, initially identifying the document before estimating the likelihood of potential words; 2) RAG-Token, which makes predictions across multiple documents, with the probability of each candidate word being the aggregate of the conditional probabilities across all documents.

AI-Driven Asset Management and Trading

In the Web3 universe, trading is a critical activity that no participant can avoid. Drawing on the “intent-centric” approach suggested by Paradiam and examining the dynamics between users and AI, QnA3.AI has unearthed several key insights:

1. The development of a user’s intent is a progressive process. However, the initial intent often starts out as vague or even incorrect.
2. The precision of this intent plays a crucial role in shaping the overall user experience, with intent recognition being a significant factor in this equation.
3. Ideally, aligning with the user’s practical execution path should be as streamlined, rapid, and secure as possible.

Source: qna3.ai

The scientific community differentiates human-AI collaboration into basic and advanced cooperation. Within advanced cooperation, there are three distinct models: Embedding, Copilot, and Agents. In the Copilot model, humans take the lead in tasks, with AI playing a supportive role in accomplishing certain aspects. On the other hand, the Agents model sees AI taking full advantage of its intelligence to tackle more complex tasks. Here, humans are required only to define objectives, provide resources, and oversee outcomes, allowing AI agents to autonomously carry out the work with a focus on intent.

In autonomous agent systems powered by large-scale models, LLM serves as the AI Agent’s brain, while essential components such as Planning, Memory, and Tool Use equip the LLM to handle increasingly sophisticated tasks. This approach is in harmony with QnA3.AI’s foundational principles.

QnA3.AI, through its AI Agent technology, seamlessly integrates information management with trading capabilities. This enables users to quickly seize market opportunities and execute transactions using the most effective strategies determined by algorithms. The QnA3 AI Agent is action-oriented, assisting users in accomplishing various tasks from information to asset management. Users simply need to set their objectives and await outcomes. While both ChatGPT and QnA3.AI excel in reasoning capabilities, QnA3.AI distinguishes itself in specialized fields through its RAG technology and action capabilities, fulfilling the objectives of AI-enhanced trading.

Rights Management Enhanced by AI and DePIN

Elevating Web3 to new heights necessitates two fundamental agreements within the current market landscape:

1. There’s a pressing need to attract and transition more Web2 users.
2. Establishing connections with the tangible economy is essential.

In 2023, the evolution of DePIN significantly impacted the Web3 ecosystem. With its expanding scope, DePIN has demonstrated its potential as a consumer-facing application layer, akin to DeFi, gaming, and social media, capable of stimulating consumer demand within the foundational blockchain or ecosystem.

As DePIN initiatives progress, transitioning governance to Decentralized Autonomous Organizations (DAOs) is poised to become a prevailing trend. DAOs will be tasked with orchestrating the acquisition, utilization, and upkeep of physical devices. DePIN is set to emerge as a trend that broadens DAOs’ governance from digital to physical assets.

QnA3.AI, serving as a conduit between the Web2 and Web3 realms, is unveiled in a Q&A format, persistently seeking novel methods to forge connections between these two distinct worlds.

Riding the wave of enthusiasm for DePIN, QnA3.AI has unveiled a groundbreaking data mining feature. This innovative function leverages the unused computational power of decentralized physical devices to train AI models, offering users a way to earn passive income. It seamlessly blends the decentralized hardware layer with a community-owned new data economy.

At its core, DePIN is built on two foundational processes: the generation of off-chain data and the verification of on-chain data. It employs several approaches, including the use of customized hardware and a bespoke incentive layer, to either adapt conventional hardware for use in the Web3 network or incorporate it directly. On its official site, QnA3.AI provides an opportunity for users to mine using their computational power, inviting them to explore the possibilities of QnA3.AI data mining. This initiative transforms available computing power into tangible value by engaging in data processing tasks through a straightforward Chrome extension, utilizing spare computational resources as users navigate the web. Once installed, this extension facilitates automatic data gathering, cleansing, and model pre-training in the background, all while being secure, non-intrusive, and respectful of users’ privacy by not collecting personal information.

Source: MWSSARI

The Three Pillars of AI+DePIN:

1. Enhancing Scalability: The hardware requirements set by DePIN directly impact the pool and growth rate of computing power contributors.
2. Simplifying Adoption: Minimizing barriers is crucial for engaging a broader base of computing power contributors.
3. Optimizing Token Economics: Tailoring and fine-tuning the token economy to align with stakeholder interests.

QnA3.AI employs the AI+DePIN framework to harness users’ computational resources for tasks like data retrieval and cleaning. Moreover, QnA3.AI underscores the importance of recognizing the intrinsic value of data in the context of hardware ownership rights. It advocates for new digital rights for individuals as data subjects, highlighting the fundamental value proposition of DePIN beyond mere data tokenization and incentives. In sync with real-world applications, QnA3.AI has decisively pivoted towards Decentralized Machine Learning. AI projects inherently grapple with computational constraints and collaborative hurdles. Merging AI with DePIN, QnA3.AI is on a path to methodically address these challenges.

The ventures into application scenarios presently undertaken are merely the initial steps by MachinenA3. Looking ahead, QnA3.AI is exploring the introduction of additional cornerstone products, including hardware wallets and desktop robots. This initiative aims to establish a comprehensive service matrix anchored in AI, spanning research, trading, and DePIN. It seeks to create an AI Agent-centric product architecture that encompasses information, asset, and rights management, catering to the wide-ranging needs of users throughout their lifecycle and across various scenarios.

Three Key Strengths of QnA3.AI

Research-Driven Approach

QnA3.AI stands out as a research-led endeavor within the Crypto sphere, bolstered by profound AI research strength. It boasts partnerships with prestigious institutions like Stanford University, UC Berkeley, and Northwestern University, with imminent joint publications in elite journals. Additionally, it benefits from a technical advisory board comprising luminaries from OpenAI, Google DeepMind, Meta, Apple, and Nvidia. The founding team, rooted in the US and drawing talent from Tencent, Baidu, and leading global investment banks, brings a wealth of expertise in data, AI, and cryptocurrency.

User Intent Resolution

QnA3.AI has revolutionized the cryptocurrency sector by integrating cutting-edge AI Q&A bots, top-tier technical analysis bots, and comprehensive asset trading functionalities. It distills user intent into a three-step process: “gathering information,” “analyzing information,” and “executing trades.” Through functionalities like “Q&A,” “technical analysis,” and “on-the-spot trading,” QnA3 bots adeptly fulfill user intents.

With a deep understanding of users and keen market insights, QnA3.AI has swiftly evolved, rolling out key updates and new features at an impressive pace—minor updates weekly and significant enhancements bi-monthly. Its information management capabilities cater to diverse needs across scenarios, including conceptualization, immediacy, reasoning, analysis, trading, and pricing. Asset management tools discern and facilitate user tasks, while rights management is set to advance digital rights management for users, epitomizing a truly user-centric product ethos.

Innovative Functionality

From inception to launch in under three months, QnA3.AI has attracted 9 million users, spanning 166 countries and regions, with daily active users nearing 200,000, consistently leading the BNB chain rankings.

Token Economics

Here’s how the team has structured the distribution and unlocking of tokens:

Source: qna3.ai

GPT Token Utilities

1. Advertising and Sponsorships: Users have the opportunity to use GPT tokens to secure advertising spaces within the trending search list, like sponsoring specific pieces of content to boost their exposure.
2. Governance Enhancement: Staking GPT tokens grants users “vetoken,” enabling them to engage in the governance of the platform. This approach empowers users with a say in platform decisions, offering a tailored interactive experience.
3. Query Quota Expansion: To cater to your specific needs, we’ve made available additional query quotas that can be acquired with GPT tokens.
4. Gaming and Point Earning: We’ve introduced a fun gamification element where users can employ GPT tokens to predict and bet on the hottest questions of the week. This feature not only allows for point accumulation but also enhances the visibility and appeal of the questions.
5. Transaction Incentives: Our team is keenly integrating trading features to distribute a portion of transaction fee dividends to GPT token holders. Plans are also in place for regular GPT repurchases to distribute extra rewards back to the community.
6. Exclusive Content: We’re in the process of developing a feature that allows for the trading of research reports as NFTs using GPT tokens, aiming to offer a distinct content experience to users.

Vision Behind QnA3.AI

In the rapidly evolving realm of technology, the rise of Artificial Intelligence (AI) heralds a new era that is redefining our interactions with machines and offering solutions to complex challenges like never before. Its deep-seated impact solidifies AI’s role as a crucial component of our global infrastructure. Furthermore, blending AI with cryptocurrency is set to revolutionize digital asset management, trading, and security, enhancing market prediction accuracy, fortifying against security risks, and refining automated trading strategies. This synergy reshapes the operational landscape of cryptocurrencies, unlocking vast potential. By integrating AI with the decentralized ethos of Web3, we envision creating a digital future that is more democratic, transparent, and inclusive, ensuring the widespread benefits of AI for all, while encouraging technological adoption and community growth.

The emergence of DePIN (Decentralized Artificial Intelligence Network) marks not just a stride in technological innovation but a groundbreaking shift towards a reimagined digital society of the future. DePIN’s philosophy is to decentralize AI’s application and governance into a distributed network. This union promises to democratize the advantages of AI, advancing technology dissemination and community growth, and paving the way for a digital future that is more democratic, transparent, and open.

Conclusion

QnA3.AI represents a pioneering leap in the AI+ search domain, blending intelligent content generation with search functionality. It boasts not just formidable Q&A capabilities but also an adeptness at discerning user needs and monitoring market movements. With its commitment to relentless innovation and the ongoing refinement of its features, QnA3.AI is on a steadfast journey of evolution. Its goal is to stand as the go-to platform for users navigating the Web3 landscape, offering information services that are both smarter and more accessible.