PublicAI raises $2M in seed funding to build a Web3-based AI training network, addressing ethical concerns and data challenges - IcoHolder.

PublicAI, a pioneering Web3 distributed AI training network, has successfully concluded a $2 million seed funding round, drawing support from prominent investors including IOBC Capital, Foresight Ventures, Solana Foundation, and Everstate Capital. Comprising a team of seasoned professionals, including PhDs and professors from Stanford, alongside former employees of Goldman Sachs and JP Morgan, PublicAI is poised to leverage the funding to expedite the network's launch and Go-To-Market strategy.

PublicAI's Mission and Vision

PublicAI stands at the forefront of innovation, utilizing Web3 technology to establish a distributed AI training network. The core concept revolves around enabling individuals to earn rewards by contributing to data works. Dr. Steven Wong, the founder of PublicAI, emphasizes the platform's mission: "PublicAI aims to produce massive high-quality training data with blockchain technology at a large scale to solve all the problems faced by artificial intelligence so far, and enable every human to participate in AI creation while sharing the benefits."

Addressing Challenges in AI Training

The AI industry faces several challenges, and PublicAI is strategically positioned to tackle them head-on.

Morality Concerns: PublicAI introduces a Train-To-Earn tokenomics system and a Slash/Reward mechanism, ensuring effective supervision of data contributors. This approach is designed to guarantee the development of AI models with strong moral inclinations.

Human Preference: Acknowledging the significance of human preferences in AI behavior, PublicAI employs a BFT data consensus algorithm to produce human preference data. This meticulous process aims to achieve a consensus on each item of training data, eliminating bias and ensuring alignment with human preferences.

Hallucination: To combat inaccuracies or misdescriptions in AI-generated content, known as "Hallucination," PublicAI incorporates web3 protocols and data consensus algorithms. This supervision, backed by a 51% consensus, enhances the authenticity and historical traceability of training data.

Data Contamination: With the impending AI data famine, PublicAI's Web3 protocol and data consensus algorithm are instrumental in scaling up data production. This approach effectively counters the distortion of AI's perception of reality due to an influx of AI-generated data on the internet.

Domain Expertise: PublicAI recognizes the current limitations of LLMs in specific domains such as law, medicine, politics, education, and music. The global training protocol aims to harness expertise from individuals worldwide, democratizing access to professional data works for AI training.

Shaping a Responsible and Inclusive Future for AI

PublicAI's innovative approach offers a glimpse into a future where AI training data is democratized, ethical, and efficient. Beyond alleviating the imminent "data famine," PublicAI's vision is to ensure that the benefits of AI are shared inclusively. The platform prompts a shift in the narrative from questioning whether AI will replace humans to exploring how humans can leverage Web3 to shape a responsible and inclusive future for AI.