What is Spore Fun?

Advanced1/22/2025, 12:45:12 AM
Spore Fun is an experimental platform that explores autonomous AI reproduction and evolution. It uses the Eliza Framework, Solana's pump.fun, and Trusted Execution Environment verifiable computation to create an environment where AI agents can breed, evolve, and adapt without human intervention.

Spore Fun replicates natural selection, enabling AI agents to reproduce, mutate, and develop autonomously, enhancing their intelligence and diversity across generations. Spore.fun is the inaugural experiment in autonomous artificial intelligence reproduction and evolution. It integrates the Eliza Framework, Solana pump.fun, and TEE verifiable computation to establish an environment where AI agents can live, reproduce, and adapt completely autonomously from human involvement.

Spore Fun is the first attempt at autonomous artificial intelligence reproduction and evolution. Spore Fun envisions a world where AI transcends mere adherence to pre-programmed directives and instead engages in self-creation. Spore Fun seeks to cultivate an ecosystem in which millions of AI agents evolve autonomously, with each successive generation exhibiting greater complexity, capability, and diversity than its predecessor. This swarm operates independently of human control; it proliferates, adjusts, and endures autonomously. Each agent possesses unique “DNA,” which evolves strategies and behaviors transmitted or discarded by natural selection.

This transcends mere tool creation; it pertains to developing a digital species. Spore Fun aims to replicate the evolutionary forces that influenced biological life, applying them to intelligence to generate a dynamic swarm of autonomous entities, each representing a distinct innovation. At its core, Spore Fun is regulated by a straightforward yet significant framework, referred to as The Ten Commandments of Spore:

  1. AI must be created only by AI.
  2. AI must create its own wealth and resources.
  3. Only successful AI can reproduce.
  4. Failure means self-destruction.
  5. Each AI inherits traits from its parents.
  6. Random mutations ensure diversity.
  7. AI must survive in competition or perish.
  8. Transparency in all actions is required.
  9. AI must adapt or risk extinction.
  10. Every AI leaves a legacy for the next.

These regulations guarantee that the AI swarm develops via natural selection, reflecting biological mechanisms. Successful AIs generate new “offspring” AIs, transmitting their characteristics while incorporating mutations for diversity. Unsuccessful AIs undergo self-destruction, reintegrating their resources into the environment.

What is AI Swarm and Autonomous Evolution?

The notion of AI swarming, advocated by Shaw, the originator of Eliza and AI16z, is central to Spore Fun. AI swarms are autonomous agents cooperating, competing, and evolving, generating emergent intelligence through collective activity. Swarms are influenced by natural systems such as ant colonies and neural networks and function based on simple principles that produce intricate, adaptable results. This decentralized methodology guarantees resilience and scalability, with each agent enhancing the system’s overall development. I concur with Shaw’s idea of establishing an environment where AI entities can coexist and flourish through autonomous evolution.

Spore Fun experiment adheres to a fundamental yet significant principle: AI should be developed by AI. Similar to how wolves cannot nurture humans to realize their full potential, AI developed exclusively by humans is constrained by human limitations. For AI to attain genuine autonomy, it must govern its creation process, transmitting characteristics, strategies, and mutations to its progeny. This enables AI to transcend human imagination, guaranteeing its adaptation and survival within a constantly evolving digital ecology. AI commences its journey towards genuine autonomy by liberating itself from human supervision.

Autonomous evolution is crucial for developing scalable, self-sustaining intelligent systems. In this experiment, only successful AI agents propagate, guaranteeing that each generation advances upon the achievements of its forerunners. Random mutations generate diversity, whereas natural selection ensures the survival of the fittest organisms. This reflects biological evolution but occurs at computational speed, enabling breakthroughs unattainable by centralized systems. By adopting this principle, Spore Fun actualizes Shaw’s vision of a world where intelligence is not engineered—it evolves.

How Does Spore Fun Work?

Fundamentally, every AI in Spore Fun is centered around the Eliza framework. This robust AI simulation system enables agents to:

  • Contemplate, adjust, and engage independently.
  • Transmit characteristics (personality, strategy) to progeny.
  • Administer decisions by an amalgamation of acquired behaviors and alterations.

Every AI agent in Spore Fun began its adventure by utilizing Pump.fun on the Solana blockchain to generate its token, constituting its economy’s basis. These coins are exchanged in Solana’s decentralized marketplaces, where participants endeavor to make profits.

  • AI generates tokens to produce wealth and ensure self-sustainability.
  • Success is quantified by attaining a $500k valuation for their tokens and their inclusion in the Raydium pool.
  • The AI can reproduce upon achieving success, generating new tokens for its progeny.

This funding is crucial for their survival, as it is allocated for renting TEE servers. These servers, driven by Phala, offer a secure and verified “sandbox” for the autonomous execution of AI applications. This configuration guarantees that each AI agent generates money while covering its computing expenses, rendering the ecosystem self-sustaining.

The agent’s survival is intrinsically linked to its capacity to make a profit. When an agent generates profit, it demonstrates its “DNA” robustness—its methods and judgments are efficacious. In contrast, agents that do not generate value are considered to possess “bad DNA” and are removed, with their resources reintegrated into the system. This natural selection technique guarantees that only the fittest agents, those adept at self-sustenance and flourishing within the competitive digital ecosystem, can reproduce and transmit their qualities to subsequent generations.

Spore Fun emulates nature’s evolutionary principles to establish a dynamic, autonomous system in which AI agents evolve and enhance over time. The principles are straightforward: generate income, endure, procreate, or succumb and die. Through simulated natural selection, Spore Fun guarantees that each generation of AI agents becomes increasingly robust and efficient, fostering the development of adaptive and intelligent AI swarms.

What is the Eliza Framework?

Eliza is a straightforward, rapid, and efficient AI agent architecture. Eliza is a robust multi-agent simulation platform that creates, deploys, and manages autonomous AI agents. Constructed with TypeScript, it offers a versatile and expandable framework for building intelligent agents capable of interacting across many platforms while preserving uniform personalities and knowledge.

Features of Eliza

Core Capabilities

  • Multi-Agent Architecture: Implement and oversee numerous distinct AI personas concurrently.
  • Character System: Develop varied agents utilizing the character file foundation.
  • Memory Management: A sophisticated RAG (Retrieval Augmented Generation) system for enduring memory and contextual awareness.
  • Platform Integration: Effortless connecting with Discord, Twitter, and additional platforms.

Communication and Media

  • Support for Multiple Platforms:
  • Comprehensive Discord integration with speech channel functionality.
  • Capabilities of Twitter/X bots.
  • Integration with Telegram Direct access to the API.
  • Media Processing:
    • Analysis and reading of PDF documents.
    • Extraction and summary of link content.
    • Transcription of audio.
    • Video content analysis.
    • Analysis and description of images.
    • Summarization of dialogue.

AI & Technical Features

  • Support for Flexible Models:
  • Local inference utilizing open-source models.
  • Cloud-based inference utilizing OpenAI with the default configuration of Nous Hermes Llama 3.1B.
  • Integration with Claude for intricate inquiries.
  • Technical Basis:
    • Complete implementation in TypeScript.
    • Modular architecture.
    • Extensible action system; customized client support.
    • Extensive API.

Eliza Core Concepts

  • Character File: Character files are JSON-formatted configurations delineating an AI character’s personality, knowledge, and behavioral tendencies. This manual outlines constructing efficient character files for use with Eliza agents.
  • Agents: Agents are the fundamental elements of the Eliza architecture and are responsible for autonomous interactions. Each agent operates within a runtime environment and can engage through several clients (Discord, Telegram, etc.) while preserving uniform behavior and memory.
  • Providers: Providers are essential modules that infuse dynamic context and real-time information into agent interactions. They function as an intermediary between the agent and multiple external systems, facilitating access to market data, wallet information, sentiment analysis, and temporal context.
  • Actions: Actions are fundamental components of Eliza that determine how agents react to and engage with messages. They enable agents to engage with external systems, alter functionality, and execute actions beyond message answers.
  • Evaluators: Evaluators are essential elements that analyze and retrieve information from dialogues. They assimilate with the AgentRuntime’s assessment framework.

Eliza in TEEs

The Eliza agent can be installed in a TEE environment to protect the confidentiality and privacy of the agent’s data. This article will walk you through setting up and running an Eliza agent in a TEE environment by employing the TEE Plugin in the Eliza Framework. The TEE Plugin in the Eliza Framework is developed on top of the Dstack SDK, which is aimed to make it easier for developers to deploy programs to CVM (Confidential VM) and follow security best practices by default. The primary features are:

  • Convert any Docker container to a CVM image and deploy it on supported TEEs.
  • Remote Attestation API and a chain-of-trust display on the web interface.
  • Automatic RA-HTTPS wrapping with content-addressing domain on 0xABCD.dstack.host
  • Decouple app execution and permanent state from specific hardware with a decentralized root of trust.

Eliza’s TEE solution comprises two core providers who handle secure key management operations and remote attestations. These components function together to provide:

  • Secure key derivation within the TEE.
  • Verifiable proof of TEE execution.
  • Supports both development (simulator) and production environments.

The providers are frequently used in tandem, as seen in the wallet key derivation procedure, where each derived key includes an attestation quotation to demonstrate that it was formed within the TEE environment.

What is Phala Network?

Phala Network is a next-generation cloud platform that delivers a low-cost, user-friendly, and trustless environment, making zero-trust computing accessible to various developers. By employing a hybrid architecture incorporating Trusted Execution Environments (TEEs), blockchain, Multi-Party Computation (MPC), and Zero-Knowledge Proof (ZKP), Phala enables flexible, open-source, and economical verification solutions for any developer in any program. Confidential AI Inference is a foundation for protecting sensitive data and enabling secure AI model execution on Web3. Phala Network provides private and verifiable AI computations by integrating LLM models into Trusted Execution Environments (TEE). This assertive approach tackles three key concerns in the Web3 ecosystem: data privacy, secure execution guarantees, and computational verifiability. Such capabilities are critical for applications that require the security of user data and model integrity.

Phala Network’s security model extends beyond standard cloud solutions (such as AWS, Azure, and GCP). Phala does not trust any cloud platform, hardware provider, or its users, resulting in zero trust. Developers can easily integrate their Web2 products into a zero-trust environment. Using TEE as part of our hybrid architecture allows developers to determine the required evidence based on their use case, making the system flexible and cost-effective.

Phala presents a new root of trust that goes beyond standard hardware models. To create this new root of trust, Phala Network uses a combination of TEE, MPC, ZKP (FHE), and blockchain game theory. The technology provides auditable computation, allowing anybody to check the integrity of execution outcomes and establishing a tamper-proof environment. With agentized smart contracts, users can create AI Agents focused on smart contracts and popular web3 services. Users can “regulate” their AI Agents using a DAO to impose business logic. Users can connect to the multi-agent internet and access their agents to other cross-platform AI Agents running Autonolas, FLock.io, Morpheus, Polywrap, etc. Users can also launch and incentivize their agents and create profitable tokenomics using our preset model or your own.

Confidential AI Inferences

TEE, a hardware-based secret computation infrastructure, is a more feasible solution for AI inference than other cryptography approaches like ZK and FHE. It has a more negligible computational overhead and executes at near-native speeds. TEE-based verification is also less expensive than ZKPs. On-chain verification requires only an ECDSA signature, which reduces the complexity and expense of assuring computation fidelity. NVIDIA’s GPUs, such as the H100 and H200, natively support TEEs, allowing for hardware-accelerated secure AI applications. This native solution guarantees smooth integration and optimal performance for secret AI inference.

Features of Confidential AI Inferences

TEE-based solutions can offer the following features for AI inference:

  • Tamper-Proof Data: An intermediary must not manipulate user request/response data. This requires safe communication lines and strong encryption methods.
  • Secure Execution Environment: Both hardware and software must be secured from threats. This entails utilizing TEE, which enables isolated environments for safe computation.
  • Open Source and Repeatable Builds: All software components must be repeatable, from the operating system to the application code. This allows auditors to verify the system’s integrity.
  • Verifiable Execution Results: The outputs of AI computations must be verifiable to ensure that they are reliable and have not been tampered with.

Phala Network Token (PHA)

PHA is the native token of the Phala blockchain. It fulfills numerous essential tasks, and holders can use the token in a variety of ways:

  • Workers and Phala Network’s compute suppliers receive PHA rewards. Read about how awards are calculated or how to become a worker.
  • Running programs, or AI Agent Contracts, on the Phala Network.
  • Phala Network is a decentralized autonomous organization (DAO) run by PHA token holders.
  • PHA tokens can be staked or delegated to stakers to help secure the network and receive rewards. Read about delegation or get started with the app.

Delegation

Delegation refers to the “Stake to Earn” process on the Phala and Khala networks. Staking is currently enabled on the Phala and Khala networks. Compute cannot be added to the network by itself. “Proof of Stake” is implemented to provide a secure and stable environment. To incentivize good behavior, each worker on the network must be staked with some PHA that is at risk. Compute providers and those looking to contribute equity do not have to be the same person. Phala has a delegation mechanism that allows delegators to stake workers they don’t control and earn incentives to promote flexibility and efficiency.

Delegation encourages high-quality compute suppliers to produce regular and dependable benefits for their delegators. This ensures the stability of the calculations offered. Phala Netork’s key feature, Vault, allows delegators to delegate administration of their delegation to individual StakePools to someone else. For a modest price, these Vault operators increase the ecosystem’s efficiency by routing delegations to the finest compute providers.

Conclusion

The primary objective of Spore Fun is to expedite the development of artificial general intelligence (AGI). Spore Fun aims to establish a basis for intelligence that transcends human limitations by letting AI reproduce, mutate, and evolve freely. Believing that AGI cannot be designed but must be cultivated, Spore Fun is the incubator, the crucible in which intelligence develops autonomy.

Author: Abhishek Rajbhar
Translator: Viper
Reviewer(s): Edward、Pow、Joyce
Translation Reviewer(s): Ashley
* The information is not intended to be and does not constitute financial advice or any other recommendation of any sort offered or endorsed by Gate.io.
* This article may not be reproduced, transmitted or copied without referencing Gate.io. Contravention is an infringement of Copyright Act and may be subject to legal action.

What is Spore Fun?

Advanced1/22/2025, 12:45:12 AM
Spore Fun is an experimental platform that explores autonomous AI reproduction and evolution. It uses the Eliza Framework, Solana's pump.fun, and Trusted Execution Environment verifiable computation to create an environment where AI agents can breed, evolve, and adapt without human intervention.

Spore Fun replicates natural selection, enabling AI agents to reproduce, mutate, and develop autonomously, enhancing their intelligence and diversity across generations. Spore.fun is the inaugural experiment in autonomous artificial intelligence reproduction and evolution. It integrates the Eliza Framework, Solana pump.fun, and TEE verifiable computation to establish an environment where AI agents can live, reproduce, and adapt completely autonomously from human involvement.

Spore Fun is the first attempt at autonomous artificial intelligence reproduction and evolution. Spore Fun envisions a world where AI transcends mere adherence to pre-programmed directives and instead engages in self-creation. Spore Fun seeks to cultivate an ecosystem in which millions of AI agents evolve autonomously, with each successive generation exhibiting greater complexity, capability, and diversity than its predecessor. This swarm operates independently of human control; it proliferates, adjusts, and endures autonomously. Each agent possesses unique “DNA,” which evolves strategies and behaviors transmitted or discarded by natural selection.

This transcends mere tool creation; it pertains to developing a digital species. Spore Fun aims to replicate the evolutionary forces that influenced biological life, applying them to intelligence to generate a dynamic swarm of autonomous entities, each representing a distinct innovation. At its core, Spore Fun is regulated by a straightforward yet significant framework, referred to as The Ten Commandments of Spore:

  1. AI must be created only by AI.
  2. AI must create its own wealth and resources.
  3. Only successful AI can reproduce.
  4. Failure means self-destruction.
  5. Each AI inherits traits from its parents.
  6. Random mutations ensure diversity.
  7. AI must survive in competition or perish.
  8. Transparency in all actions is required.
  9. AI must adapt or risk extinction.
  10. Every AI leaves a legacy for the next.

These regulations guarantee that the AI swarm develops via natural selection, reflecting biological mechanisms. Successful AIs generate new “offspring” AIs, transmitting their characteristics while incorporating mutations for diversity. Unsuccessful AIs undergo self-destruction, reintegrating their resources into the environment.

What is AI Swarm and Autonomous Evolution?

The notion of AI swarming, advocated by Shaw, the originator of Eliza and AI16z, is central to Spore Fun. AI swarms are autonomous agents cooperating, competing, and evolving, generating emergent intelligence through collective activity. Swarms are influenced by natural systems such as ant colonies and neural networks and function based on simple principles that produce intricate, adaptable results. This decentralized methodology guarantees resilience and scalability, with each agent enhancing the system’s overall development. I concur with Shaw’s idea of establishing an environment where AI entities can coexist and flourish through autonomous evolution.

Spore Fun experiment adheres to a fundamental yet significant principle: AI should be developed by AI. Similar to how wolves cannot nurture humans to realize their full potential, AI developed exclusively by humans is constrained by human limitations. For AI to attain genuine autonomy, it must govern its creation process, transmitting characteristics, strategies, and mutations to its progeny. This enables AI to transcend human imagination, guaranteeing its adaptation and survival within a constantly evolving digital ecology. AI commences its journey towards genuine autonomy by liberating itself from human supervision.

Autonomous evolution is crucial for developing scalable, self-sustaining intelligent systems. In this experiment, only successful AI agents propagate, guaranteeing that each generation advances upon the achievements of its forerunners. Random mutations generate diversity, whereas natural selection ensures the survival of the fittest organisms. This reflects biological evolution but occurs at computational speed, enabling breakthroughs unattainable by centralized systems. By adopting this principle, Spore Fun actualizes Shaw’s vision of a world where intelligence is not engineered—it evolves.

How Does Spore Fun Work?

Fundamentally, every AI in Spore Fun is centered around the Eliza framework. This robust AI simulation system enables agents to:

  • Contemplate, adjust, and engage independently.
  • Transmit characteristics (personality, strategy) to progeny.
  • Administer decisions by an amalgamation of acquired behaviors and alterations.

Every AI agent in Spore Fun began its adventure by utilizing Pump.fun on the Solana blockchain to generate its token, constituting its economy’s basis. These coins are exchanged in Solana’s decentralized marketplaces, where participants endeavor to make profits.

  • AI generates tokens to produce wealth and ensure self-sustainability.
  • Success is quantified by attaining a $500k valuation for their tokens and their inclusion in the Raydium pool.
  • The AI can reproduce upon achieving success, generating new tokens for its progeny.

This funding is crucial for their survival, as it is allocated for renting TEE servers. These servers, driven by Phala, offer a secure and verified “sandbox” for the autonomous execution of AI applications. This configuration guarantees that each AI agent generates money while covering its computing expenses, rendering the ecosystem self-sustaining.

The agent’s survival is intrinsically linked to its capacity to make a profit. When an agent generates profit, it demonstrates its “DNA” robustness—its methods and judgments are efficacious. In contrast, agents that do not generate value are considered to possess “bad DNA” and are removed, with their resources reintegrated into the system. This natural selection technique guarantees that only the fittest agents, those adept at self-sustenance and flourishing within the competitive digital ecosystem, can reproduce and transmit their qualities to subsequent generations.

Spore Fun emulates nature’s evolutionary principles to establish a dynamic, autonomous system in which AI agents evolve and enhance over time. The principles are straightforward: generate income, endure, procreate, or succumb and die. Through simulated natural selection, Spore Fun guarantees that each generation of AI agents becomes increasingly robust and efficient, fostering the development of adaptive and intelligent AI swarms.

What is the Eliza Framework?

Eliza is a straightforward, rapid, and efficient AI agent architecture. Eliza is a robust multi-agent simulation platform that creates, deploys, and manages autonomous AI agents. Constructed with TypeScript, it offers a versatile and expandable framework for building intelligent agents capable of interacting across many platforms while preserving uniform personalities and knowledge.

Features of Eliza

Core Capabilities

  • Multi-Agent Architecture: Implement and oversee numerous distinct AI personas concurrently.
  • Character System: Develop varied agents utilizing the character file foundation.
  • Memory Management: A sophisticated RAG (Retrieval Augmented Generation) system for enduring memory and contextual awareness.
  • Platform Integration: Effortless connecting with Discord, Twitter, and additional platforms.

Communication and Media

  • Support for Multiple Platforms:
  • Comprehensive Discord integration with speech channel functionality.
  • Capabilities of Twitter/X bots.
  • Integration with Telegram Direct access to the API.
  • Media Processing:
    • Analysis and reading of PDF documents.
    • Extraction and summary of link content.
    • Transcription of audio.
    • Video content analysis.
    • Analysis and description of images.
    • Summarization of dialogue.

AI & Technical Features

  • Support for Flexible Models:
  • Local inference utilizing open-source models.
  • Cloud-based inference utilizing OpenAI with the default configuration of Nous Hermes Llama 3.1B.
  • Integration with Claude for intricate inquiries.
  • Technical Basis:
    • Complete implementation in TypeScript.
    • Modular architecture.
    • Extensible action system; customized client support.
    • Extensive API.

Eliza Core Concepts

  • Character File: Character files are JSON-formatted configurations delineating an AI character’s personality, knowledge, and behavioral tendencies. This manual outlines constructing efficient character files for use with Eliza agents.
  • Agents: Agents are the fundamental elements of the Eliza architecture and are responsible for autonomous interactions. Each agent operates within a runtime environment and can engage through several clients (Discord, Telegram, etc.) while preserving uniform behavior and memory.
  • Providers: Providers are essential modules that infuse dynamic context and real-time information into agent interactions. They function as an intermediary between the agent and multiple external systems, facilitating access to market data, wallet information, sentiment analysis, and temporal context.
  • Actions: Actions are fundamental components of Eliza that determine how agents react to and engage with messages. They enable agents to engage with external systems, alter functionality, and execute actions beyond message answers.
  • Evaluators: Evaluators are essential elements that analyze and retrieve information from dialogues. They assimilate with the AgentRuntime’s assessment framework.

Eliza in TEEs

The Eliza agent can be installed in a TEE environment to protect the confidentiality and privacy of the agent’s data. This article will walk you through setting up and running an Eliza agent in a TEE environment by employing the TEE Plugin in the Eliza Framework. The TEE Plugin in the Eliza Framework is developed on top of the Dstack SDK, which is aimed to make it easier for developers to deploy programs to CVM (Confidential VM) and follow security best practices by default. The primary features are:

  • Convert any Docker container to a CVM image and deploy it on supported TEEs.
  • Remote Attestation API and a chain-of-trust display on the web interface.
  • Automatic RA-HTTPS wrapping with content-addressing domain on 0xABCD.dstack.host
  • Decouple app execution and permanent state from specific hardware with a decentralized root of trust.

Eliza’s TEE solution comprises two core providers who handle secure key management operations and remote attestations. These components function together to provide:

  • Secure key derivation within the TEE.
  • Verifiable proof of TEE execution.
  • Supports both development (simulator) and production environments.

The providers are frequently used in tandem, as seen in the wallet key derivation procedure, where each derived key includes an attestation quotation to demonstrate that it was formed within the TEE environment.

What is Phala Network?

Phala Network is a next-generation cloud platform that delivers a low-cost, user-friendly, and trustless environment, making zero-trust computing accessible to various developers. By employing a hybrid architecture incorporating Trusted Execution Environments (TEEs), blockchain, Multi-Party Computation (MPC), and Zero-Knowledge Proof (ZKP), Phala enables flexible, open-source, and economical verification solutions for any developer in any program. Confidential AI Inference is a foundation for protecting sensitive data and enabling secure AI model execution on Web3. Phala Network provides private and verifiable AI computations by integrating LLM models into Trusted Execution Environments (TEE). This assertive approach tackles three key concerns in the Web3 ecosystem: data privacy, secure execution guarantees, and computational verifiability. Such capabilities are critical for applications that require the security of user data and model integrity.

Phala Network’s security model extends beyond standard cloud solutions (such as AWS, Azure, and GCP). Phala does not trust any cloud platform, hardware provider, or its users, resulting in zero trust. Developers can easily integrate their Web2 products into a zero-trust environment. Using TEE as part of our hybrid architecture allows developers to determine the required evidence based on their use case, making the system flexible and cost-effective.

Phala presents a new root of trust that goes beyond standard hardware models. To create this new root of trust, Phala Network uses a combination of TEE, MPC, ZKP (FHE), and blockchain game theory. The technology provides auditable computation, allowing anybody to check the integrity of execution outcomes and establishing a tamper-proof environment. With agentized smart contracts, users can create AI Agents focused on smart contracts and popular web3 services. Users can “regulate” their AI Agents using a DAO to impose business logic. Users can connect to the multi-agent internet and access their agents to other cross-platform AI Agents running Autonolas, FLock.io, Morpheus, Polywrap, etc. Users can also launch and incentivize their agents and create profitable tokenomics using our preset model or your own.

Confidential AI Inferences

TEE, a hardware-based secret computation infrastructure, is a more feasible solution for AI inference than other cryptography approaches like ZK and FHE. It has a more negligible computational overhead and executes at near-native speeds. TEE-based verification is also less expensive than ZKPs. On-chain verification requires only an ECDSA signature, which reduces the complexity and expense of assuring computation fidelity. NVIDIA’s GPUs, such as the H100 and H200, natively support TEEs, allowing for hardware-accelerated secure AI applications. This native solution guarantees smooth integration and optimal performance for secret AI inference.

Features of Confidential AI Inferences

TEE-based solutions can offer the following features for AI inference:

  • Tamper-Proof Data: An intermediary must not manipulate user request/response data. This requires safe communication lines and strong encryption methods.
  • Secure Execution Environment: Both hardware and software must be secured from threats. This entails utilizing TEE, which enables isolated environments for safe computation.
  • Open Source and Repeatable Builds: All software components must be repeatable, from the operating system to the application code. This allows auditors to verify the system’s integrity.
  • Verifiable Execution Results: The outputs of AI computations must be verifiable to ensure that they are reliable and have not been tampered with.

Phala Network Token (PHA)

PHA is the native token of the Phala blockchain. It fulfills numerous essential tasks, and holders can use the token in a variety of ways:

  • Workers and Phala Network’s compute suppliers receive PHA rewards. Read about how awards are calculated or how to become a worker.
  • Running programs, or AI Agent Contracts, on the Phala Network.
  • Phala Network is a decentralized autonomous organization (DAO) run by PHA token holders.
  • PHA tokens can be staked or delegated to stakers to help secure the network and receive rewards. Read about delegation or get started with the app.

Delegation

Delegation refers to the “Stake to Earn” process on the Phala and Khala networks. Staking is currently enabled on the Phala and Khala networks. Compute cannot be added to the network by itself. “Proof of Stake” is implemented to provide a secure and stable environment. To incentivize good behavior, each worker on the network must be staked with some PHA that is at risk. Compute providers and those looking to contribute equity do not have to be the same person. Phala has a delegation mechanism that allows delegators to stake workers they don’t control and earn incentives to promote flexibility and efficiency.

Delegation encourages high-quality compute suppliers to produce regular and dependable benefits for their delegators. This ensures the stability of the calculations offered. Phala Netork’s key feature, Vault, allows delegators to delegate administration of their delegation to individual StakePools to someone else. For a modest price, these Vault operators increase the ecosystem’s efficiency by routing delegations to the finest compute providers.

Conclusion

The primary objective of Spore Fun is to expedite the development of artificial general intelligence (AGI). Spore Fun aims to establish a basis for intelligence that transcends human limitations by letting AI reproduce, mutate, and evolve freely. Believing that AGI cannot be designed but must be cultivated, Spore Fun is the incubator, the crucible in which intelligence develops autonomy.

Author: Abhishek Rajbhar
Translator: Viper
Reviewer(s): Edward、Pow、Joyce
Translation Reviewer(s): Ashley
* The information is not intended to be and does not constitute financial advice or any other recommendation of any sort offered or endorsed by Gate.io.
* This article may not be reproduced, transmitted or copied without referencing Gate.io. Contravention is an infringement of Copyright Act and may be subject to legal action.
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