0xai 团队：为什么我们看好 Bittensor？

What exactly is Shanouba Bitcoin Trading Network? It is not an artificial intelligence product, nor does it produce or provide any artificial intelligence products or services. It is an economic system. By providing highly competitive incentive system for product producers, it acts as an optimizer of product market. In the ecology, high-quality producers get more incentives, while less competitive producers are gradually eliminated. How to create such incentives and effective competition in team and group discussions? The incentive mechanism to promote the organic production of high-quality products? The flywheel model has achieved this goal through the flywheel model. Verifiers evaluate the quality of artificial intelligence products in the ecosystem and allocate incentives according to their quality to ensure that high-quality producers get more incentives, which stimulates the continuous increase of high-quality output, thus enhancing the value of the network and increasing the appreciation, which not only attracts more high-quality producers to join the ecology, but also increases the attack cost of manipulating the quality evaluation results. It further strengthens the consensus of honest verifiers, enhances the objectivity and fairness of evaluation results, and thus realizes a more effective competition and incentive mechanism. Ensuring the fairness and objectivity of evaluation results is a key step in turning the flywheel, which is also the core technology, that is, the abstract verification system based on consensus. So what is Yuma Consensus and how to ensure the fairness and objectivity of quality evaluation results after consensus? Consensus is a consensus mechanism aimed at calculating the final evaluation from the diversified evaluations provided by many verifiers. The results are similar to the Byzantine fault-tolerant consensus mechanism. As long as most verifiers in the network are honest, they can finally reach the correct decision. Assuming that honest verifiers can provide objective evaluation, the evaluation results after consensus will also be fair and objective. Take the subnet quality evaluation as an example. The root network verifier evaluates and ranks the output quality of each subnet, summarizes the evaluation results, obtains the final evaluation results through the consensus algorithm, and then uses the final results to allocate the newly created ones to each one. At present, there is indeed room for improvement in the Yuma consensus on subnets. The root network verifier may not fully represent all the holders, and the evaluation results they provide may not necessarily reflect a wide range of views. In addition, the evaluation of some top verifiers may not always be objective, and even if prejudice is found, the existence of root network verifier may not be corrected immediately, which limits the number of subnets that can be accommodated. It is not enough to compete with centralized artificial intelligence giants to have only one subnet. It may be difficult to effectively monitor all subnets. Verifiers may not have a strong intention to move to a new subnet. In the short term, verifiers may lose some rewards when they move from an old subnet with high emissions to a new subnet with low emissions. The uncertainty of whether the emissions of the new subnet can catch up in the end and the clear loss of rewards in the pursuit process weaken their willingness to move. It is also planned to upgrade the mechanism to solve these shortcomings. Dynamically, the power to evaluate subnet quality will be distributed to all holders rather than a few verifiers. Holders will be able to indirectly determine the distribution ratio of each subnet through pledge. Without the restriction of root network verifier, the maximum number of active subnets will increase to 10, which will greatly lower the threshold for new teams to join the ecosystem, leading to more intense competition among subnets. Verifiers who move to a new subnet earlier may get higher rewards, which means buying the subnet at a lower price, thus increasing the possibility of receiving more in the future. Strong inclusiveness is also Yuma. One of the advantages of consensus is that consensus is not only used to determine the emissions of each subnet, but also used to determine the distribution ratio of each miner and verifier in the same subnet. Moreover, no matter what the task of miners is, its contributions, including computing power, data, human contribution and intelligence, are abstractly considered, so any stage of commodity production can be connected to the ecosystem to enjoy incentives and at the same time enhance the value of the network. Next, let's explore some leading subnets and observe how to encourage the loss of these subnets. Subnet Subnet can contribute to the development circulation of the subnet by creating an account on the Internet. The background is text-to-speech and the core members of the team come from MIT, Oxford University, Princeton University and other well-known universities. The aim is to create a code-free platform for college students without programming background to easily create the robots they want, focusing on the field, audio books and virtual assistants. With the continuous expansion of the product matrix, the first voice chat robot was launched in June, and currently registered users. The platform has hosted more than one million robots of various types, including language learning, educational and practical robots. This is to gather the wisdom of the whole open source community to create the best open source model. In other words, it does not directly run the model or handle the requests of end users. On the contrary, it is a network architecture for training the model. As shown in the above figure, miners are responsible for training the model, uploading the trained model to the model pool and storing the metadata of the model in the blockchain network. Verifiers evaluate the model performance by generating test cases and scoring according to the results. Blockchain is responsible for using consensus aggregation weights to determine the final weight and distribution ratio of each miner. In short, miners must constantly submit higher-quality models to maintain their rewards. Currently, models under trial have been launched on their platforms for users. In the future, as the trained models become more reliable, more use cases will come online, and as an open source model, they will not only be limited to but also be extended to. Isn't it our goal in decentralized artificial intelligence that other platforms train and motivate open source models through this decentralized method? Subnet can contribute to the development circulation by creating an account on the Internet. The team behind the background is the team, and its core members have rich experience in the field of artificial intelligence. Before entering the subnet, they launched a flagship product, an offline data search engine, which used artificial intelligence. The algorithm optimizes the core components of search engine, including data collection, ranking algorithm and retrieval algorithm. It has been recognized as a first-class information collection tool in the encryption community. Positioning aims to establish a decentralized index layer to support intelligent search and analysis. Search engine is not just a database or ranking algorithm, but a complex system. In addition, an effective search engine needs low delay, which poses additional challenges to the construction of decentralized versions. Fortunately, these challenges are expected to be solved through the incentive system. As shown in the above figure, the operation flow of artificial intelligence is not simply decentralized to each component of search engine. 比特币今日价格行情网_okx交易所app_永续合约_比特币怎么买卖交易_虚拟币交易所平台

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