🙋The current AI industry is showing an unprecedented trend of centralization. Tech giants like OpenAI and Google control the most advanced AI models, forming a new "digital oligopoly."

These companies have hundreds of billions of dollars in capital and computing resources, but they also bring some well-known issues:

- The black box of decision-making flow: Users cannot know how AI makes decisions and whether this decision truly considers the user's perspective.

- Objective output caused by compliance: It's actually quite simple. The AI companies involved must undergo corresponding compliance reviews based on the country they are in, and naturally, this will result in the content output reflecting relevant political "emotions".

- Everyone's value is being exploited: B-end companies need to pay to use the product, C-end users need to pay to use the product, but their data makes AI better without any return.

- The slow innovation brought about by decision-making power being in the hands of a small group: It is evident that the companies behind AI products, and even a small number of people (the board), determine the development direction of the products, which will limit the "hundred flowers blooming" of AI.

The emergence of Sentient represents a fundamental paradigm shift—from "AI for a few" to "AI by the community, for the community."

Essentially, I think what Sentient is doing is a kind of AI egalitarianism.

Whether it's usage rights, ownership, decision-making rights, cost options, and other rights.

➡️Everyone understands the above 4 questions, but how to solve them, how to resolve them.

Sentient gave its own answer.

1. Community-driven model development: through the OML (Open, Monetizable, Loyalty) framework.

Trainers, deployers, and validators of AI models can receive corresponding rewards, forming a sustainable incentive mechanism.

2. Promote decentralized governance: Utilize the decentralized characteristics of blockchain to ensure that the direction of AGI development is determined by the community.

The community decides AI, not a small minority.

3. Achieve fair distribution of value: Break the traditional AI company's "winner takes all" model, allowing every contributor to benefit from the growth of the AI economy.

Where does the core technological innovation manifest?

I believe that GRID and OML are representations of its core product innovation achievements.

1. GRID is the world's largest open-source intelligent network and the technological core of SentientAGI, integrating over 100 AI components.

I have summarized it briefly, and everyone can take a look.

2. OML Framework: Fingerprint recognition technology of AI models

This technology can directly embed ownership and values into the model weights.

Ensure that the model uses traceable and verifiable methods through cryptographic primitives and a trusted execution environment ( TEE ), preventing unauthorized extraction.

Its specific commercial value lies in solving the profitability problem of open-source AI.

Developers can not only share their models with peace of mind but also ensure their ownership and rights to profits from their models.

📖 The team's multiple papers have been recognized by NeurIPS.

First, let's talk about what NeurIPS is.

For example: the Oscars in the field of AI, or, as everyone is more familiar with, the Golden Rooster, Golden Horse, and Golden Image in the film and television industry.

The value of this is self-evident.

The four accepted papers cover scalable LLM fingerprinting (OML), anti-pollution code evaluation benchmarks, multi-agent social games, and model safety control primitives.

1. Taking OML as an example: The OML 1.0 system developed by SentientAGI can embed 24,576 persistent and undetectable fingerprints in large language models, achieving a scale improvement 100 times greater than existing solutions.

Yes, it's 100 times, you didn't misread.

2. Anti-pollution code evaluation benchmark: This breaks the misleading promotion of "AI surpassing human programmers" and establishes a new standard for the credible assessment of code generation AI.

It is equivalent to promoting the development of more reliable code generation tools in the trillion-dollar software engineering market.

3. Multi-Agent Social Games: The paper submitted by Sentient achieves a shift from static benchmarks to adaptive multi-agent environments, addressing the issue of traditional evaluations neglecting social dynamics.

AI should not only serve static scenarios but should also be capable of independently responding to changing situations at any time.

4. Model safety control primitives: This paper has significant implications for AI business discourse, especially for AI applications in regulated industries such as healthcare and finance.

➡️Rooted in technology, centered on products, and based on decentralization, Sentient has proven itself for all AI projects.

AI belongs to all humanity

Technology is the best proof of project quality.

AI + Web3 = Future

AI For You
AI For Me
AI For Everyone