Over the past few years, zero-knowledge proofs on blockchain have served two important purposes:
(1) Scale computationally limited networks by processing off-chain transactions and validating results on mainnet.
(2) It protects user privacy by enabling shielded transactions that can only be seen by those who have the decryption key.
In the context of blockchain, it is clear why these properties are desirable. A decentralized network like Ethereum cannot increase throughput or block size without placing demands on validator processing power, bandwidth, and latency. can be used to validate computational progress in a concise and efficient manner.
So far, many of us have experienced the potential of interacting with very powerful machine learning products. Anyone who has played GPT-4 may have had a similar experience of gaining superpowers.
Clearly, this is an exciting and somewhat difficult prospect to consider. A natural impulse for anyone working in the cryptocurrency industry (after marveling at what machine learning can do) is to distribute potential centralized vectors and networks that are transparently audited and owned by people. to consider how. Current models are built by absorbing vast amounts of publicly available text and data, yet very few people currently manage and own these models.
Recently, there have been calls to pause or slow down the progress of large artificial intelligence projects like Chat-GPT. However, stopping progress is not the solution. Instead, a better approach is to promote open-source models and use on-chain, fully auditable, privacy-preserving zero-knowledge proofs to protect models when model providers want to keep weights and data private. That’s it. The latter use case for private model weights and data is currently not viable on-chain, but advances in zero-knowledge proof systems will allow it in the future.
Today, the primary use case for zero-knowledge proofs in on-chain machine learning environments is verifying correct computations. Zero-knowledge proofs usually represent programs as arithmetic circuits. Using these circuits, provers generate proofs from public and private inputs, and verifiers mathematically compute whether a statement’s output is correct, but do not obtain information about private inputs. The implication of machine learning models here is that once you have designed a proof system, the most important things developers have to consider are proof time and memory, i.e. expressing the model in a way that can be proven relatively quickly. . It cannot be denied that it is a catch-up battle at this stage. As zero-knowledge proofs become more optimized, so does the complexity of machine learning models.
GPT-AI machine learning
GPT-AI is a decentralized web3 project independently developed and created using CHATGPT artificial intelligence. GPT-AI’s goal is to allow anyone to use and train their own AI robots, eventually forming a massive scale AI application, transaction and rental platform.
For example, if you are an image processor, designer, nutritionist, fitness coach, or chef, you want to teach your AI robot the best skills and knowledge, continuously train it, accumulate data, optimize the data structure, and can be made more professional. Such AI will be the most popular of all industries on Web3, where you can rent or sell AI robots to serve other users and earn commission. This is a huge demand value unlocked by the combination of Web3 community and AI, and the value generated after solving the demand will be returned to users who continue to train GPT-AI robots.
Web3’s decentralized and decentralized features provide better support for GPT-AI. In the Web3 ecosystem, all data and applications are stored on a decentralized blockchain network. This network is public, transparent, and immutable. The distributed data architecture makes GPT-AI easy to access and share data while ensuring data security. In addition, Web3’s smart contract feature provides a more flexible and efficient transaction and training mechanism for GPT-AI, making the application and sale of GPT-AI more convenient.
GPT-AI’s IDO will officially launch on April 11th, with both $GPT and GAI Genesis NFTs for sale. $GPT is the governance token of the GPT-AI platform protocol, used for participation in governance, voting, and profit sharing. Its maximum supply is $1 billion GPT and the target for this IDO is 20%. The total amount of IDOs is set at 4 million USDT and the deadline is April 30th. The first 500 participants of the $GPT IDO will qualify for the GAI Genesis NFT whitelist. GAI Genesis NFTs are limited to 5,000 with a minimum purchase of $500 USDT. Holding GAI Genesis NFTs will allow mining quotas worth up to 4,000 USDT.
Details: https://gpt-ai.io/
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Website: https://gpt-ai.io/
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