Opinion: Why are we still bullish on Bittensor?

Original title: Why we are bullish on Bittensor?

Original author: 0xai

Original translation: TechFlow

What is Bittensor?

Bittensor itself is not an AI product, nor does it produce or provide any AI products or services. Bittensor is an economic system that acts as an optimizer for the AI product market by providing a highly competitive incentive system for AI product producers. In the Bittensor ecosystem, high-quality producers receive more incentives, while less competitive producers are gradually eliminated.

So, how does Bittensor specifically create this incentive mechanism to encourage effective competition and promote the organic production of high-quality AI products?

Bittensor Flywheel Model

Bittensor achieves this goal through a flywheel model. Validators evaluate the quality of AI products in the ecosystem and allocate incentives based on their quality, ensuring that high-quality producers receive more incentives. This inspires the continued increase in high-quality output, thereby enhancing the value of the Bittensor network and promoting the appreciation of TAO. The appreciation of TAO not only attracts more high-quality producers to join the Bittensor ecosystem, but also increases the attack cost of manipulators who manipulate quality evaluation results. This further strengthens the consensus of honest validators and enhances the objectivity and fairness of evaluation results, thereby achieving a more effective competition and incentive mechanism.

Ensuring the fairness and objectivity of evaluation results is a key step in starting the flywheel. This is also the core technology of Bittensor, the abstract verification system based on the Yuma consensus.

So, what is Yuma consensus, and how does it ensure that the quality evaluation results after consensus are fair and objective?

Yuma consensus is a consensus mechanism that aims to calculate the final evaluation results based on the diverse evaluations provided by many validators. Similar to the Byzantine Fault Tolerant consensus mechanism, as long as the majority of validators in the network are honest, the correct decision will be made in the end. Assuming that honest validators can provide objective evaluations, the evaluation results after consensus will also be fair and objective.

Take subnet quality assessment as an example, the root network validator evaluates and ranks the output quality of each subnet. The evaluation results from 64 validators are aggregated and the final evaluation results are obtained through the Yuma consensus algorithm. These final results are then used to allocate newly minted TAO to each subnet.

Currently, Yuma consensus does have room for improvement:

· Root network validators may not fully represent all TAO holders, and the evaluation results they provide may not necessarily reflect a wide range of views. In addition, the evaluations of some top validators may not always be objective. Even if bias is discovered, it may not be corrected immediately.

· The existence of root network validators limits the number of subnets that Bittensor can accommodate. To compete with centralized AI giants, only 32 subnets are not enough. However, even with 32 subnets, it may be difficult for root network validators to effectively monitor all subnets.

· Validators may not have a strong inclination to migrate to new subnets. In the short term, validators may lose some rewards when migrating from old subnets with higher issuance to new subnets with lower issuance. The uncertainty of whether the issuance of the new subnet will eventually catch up, coupled with the clear loss of rewards in the pursuit process, will reduce their willingness to migrate.

Bittensor also plans to upgrade the mechanism to address these shortcomings:

· Dynamic TAO will distribute the power to evaluate the quality of subnets to all TAO holders, rather than a few validators. TAO holders will be able to indirectly determine the allocation ratio of each subnet through staking.

· Without the limit of root network validators, the maximum number of active subnets will increase to 1024. This will greatly reduce the threshold for new teams to join the Bittensor ecosystem, resulting in more intense competition between subnets.

· Validators who migrate to new subnets earlier may receive higher rewards. Early migration to a new subnet means buying dynamic TAO of that subnet at a lower price, increasing the possibility of obtaining more TAO in the future.

Strong inclusiveness is also one of the main advantages of Yuma consensus. Yuma consensus is used not only to determine the issuance of each subnet, but also to determine the distribution ratio of each miner and validator within the same subnet. In addition, no matter what the miner's task is, its included contributions, including computing power, data, human contribution, and intelligence, are considered abstractly. Therefore, any stage of AI commodity production can access the Bittensor ecosystem and enjoy incentives while enhancing the value of the Bittensor network.

Next, let's explore some of the leading subnets and see how Bittensor incentivizes the output of these subnets.

Excellent Subnets

Subnet 3: Myshell TTS

You can contribute to the development of the myshell ai/MyShell TTS subnet by creating an account on GitHub.

Issuance:3.46% (April 9, 2024)

Background:Myshell is the team behind Myshell TTS (text-to-speech), composed of core members from well-known institutions such as MIT, Oxford University, and Princeton University. Myshell aims to create a no-code platform that enables college students without programming background to easily create the robots they want. Focusing on the field of TTS, audiobooks, and virtual assistants, Myshell launched its first voice chatbot, Samantha, in March 2023. With the continuous expansion of the product matrix, it has accumulated more than one million registered users to date. The platform hosts various types of robots, including language learning, education, and utility robots.

Positioning:Myshell launched this subnet to pool the wisdom of the entire open source community to create the best open source TTS model. In other words, Myshell TTS does not directly run the model or process end-user requests; instead, it is a network for training TTS models.

Myshell TSS Architecture

The process of Myshell TTS operation is shown in the figure above. Miners are responsible for training models and uploading trained models to the model pool (the model's metadata is also stored in the Bittensor blockchain network); validators evaluate models by generating test cases, evaluating model performance, and scoring based on the results; the Bittensor blockchain is responsible for aggregating weights using Yuma consensus to determine the final weight and distribution ratio of each miner.

In short, miners must continue to submit higher quality models to maintain their rewards.

Currently, Myshell has also launched a demo on its platform for users to try out models in Myshell TTS.

Open Kaito Architecture

In the future, as the models trained by Myshell TTS become more reliable, more use cases will come online. In addition, as open source models, they are not limited to Myshell, but can be extended to other platforms. Isn’t it our goal in decentralized AI to train and incentivize open source models through this decentralized approach?

Subnet 5: Open Kaito

You can contribute to the development of Open Kaito by creating an account on GitHub.

Issuance:4.39% (April 9, 2024)

Background:The team behind Kaito.ai is the Open Kaito team, whose core members have extensive experience in the field of artificial intelligence and have previously worked at first-class companies such as AWS, META, and Citadel. Before entering the Bittensor subnet, they launched their flagship product Kaito.ai, a Web3 off-chain data search engine, which was launched in the fourth quarter of 2023. Using artificial intelligence algorithms, Kaito.ai optimizes the core components of search engines, including data collection, ranking algorithms, and retrieval algorithms. It has been recognized as a first-class information collection tool in the crypto community.

Positioning:Open Kaito aims to build a decentralized indexing layer to support intelligent search and analysis. A search engine is not just a database or ranking algorithm, but a complex system. In addition, an effective search engine also requires low latency, which poses additional challenges to building a decentralized version. Fortunately, through Bittensor's incentive system, these challenges are expected to be solved.

The operation process of Open Kaito is shown in the figure above. Open Kaito not only decentralizes each component of the search engine, but also defines the indexing problem as a miner-validator problem. That is, miners are responsible for responding to users' indexing requests, while validators distribute requirements and score miners' responses.

Open Kaito does not restrict how miners complete indexing tasks, but focuses on the final results of miners' output to encourage innovative solutions. This helps to cultivate a healthy competitive environment among miners. Faced with users' indexing needs, miners work hard to improve execution plans to obtain higher quality response results using fewer resources.

Subnet 6: Nous Finetuning

You can contribute to the development of the Nous Research/finetuning subnet by creating an account on GitHub.

Issuance:6.26% (April 9, 2024)

Background:The team behind Nous Finetuning comes from Nous Research, a research team focused on large-scale language model (LLM) architectures, data synthesis, and on-device reasoning. Its co-founder was previously the chief engineer of Eden Network.

Positioning:Nous Finetuning is a subnet dedicated to fine-tuning large language models. In addition, the data used for fine-tuning also comes from the Bittensor ecosystem, specifically subnet 18.

The operation process of Nous Finetuning is similar to Myshell TSS. Miners train models based on data from subnet 18 and periodically publish these models for hosting on Hugging Face; validators evaluate the models and provide scores; again, the Bittensor blockchain is responsible for aggregating weights using Yuma consensus, determining the final weight and issuance of each miner.

Subnet 18: Cortex.t

You can contribute to the development of corcel-api/cortex.t by creating an account on GitHub.

Issuance:7.74% (April 9, 2024)

Background:The team behind Cortex.t is Corcel.io, which has received support from Mog, the second largest validator on the Bittensor network. Corcel.io is an end-user application that provides a similar experience to ChatGPT by leveraging the Bittensor ecosystem's AI products.

Positioning:Cortex.t is positioned as the last layer before delivering results to the end user. It is responsible for instrumenting and optimizing the outputs of the various subnetworks to ensure that the results are accurate and reliable, especially when a single prompt calls multiple models. Cortex.t is designed to prevent blank or inconsistent outputs, ensuring a seamless user experience.

Miners in Cortex.t utilize other subnets in the Bittensor ecosystem to process requests from end users. They also use GPT 3.5 turbo or GPT 4 to verify the output results to ensure reliability to end users. Validators evaluate the miners' output by comparing it to the results generated by OpenAI.

Subnet 19: Vision

Contribute to the development of namoray/vision by creating an account on GitHub.

Issuance: 9.47% (April 9, 2024)

Background: The development team behind Vision also came from Corcel.io.

Positioning:Vision aims to maximize the output capacity of the Bittensor network by leveraging an optimized subnet building framework called DSIS (Distributed Scaled Inference Subnet). The framework accelerates the response of miners to validators. Currently, Vision focuses on the scenario of image generation.

Validators receive requirements from the Corcel.io frontend and distribute them to miners. Miners are free to choose their favorite technology stack (not limited to models) to process requirements and generate responses. Then, validators evaluate the performance of miners. Due to the existence of DSIS, Vision is able to respond to these requirements faster and more efficiently than other subnets.

Summary

It is obvious from the above examples that Bittensor is highly inclusive. Both the generation of miners and the verification of validators occur off-chain, and the Bittensor network is only used to allocate rewards to each miner based on the evaluation of the validator. Any aspect of AI product generation that conforms to the miner-validator architecture can be transformed into a subnet.

In theory, competition between subnets should be fierce. For any subnet to continue to receive rewards, it must continue to produce high-quality outputs. Otherwise, if the root network validator deems the output of a subnet to be of low value, its allocation may be reduced and it may eventually be replaced by a new subnet.

However, in reality, we do observe some problems:

· Redundancy and duplication of resources due to similar positioning of subnets. Among the 32 existing subnets, there are multiple subnets that focus on popular directions such as text to image, text prompts, and price prediction.

· There are subnets with no real use cases. Although the price prediction subnet may provide theoretical value as an oracle, the current performance of prediction data is far from the level that can be used by end users.

· There is a situation of "bad money driving out good money". Some top validators may not be inclined to migrate to new subnets, even if some new subnets show significantly higher quality. However, due to lack of financial support, they may not be able to obtain sufficient issuance in the short term. Since new subnets only have a 7-day protection period, if they cannot quickly accumulate sufficient issuance, they may face the risk of being eliminated and offline.

These problems reflect insufficient competition between subnets, and some validators have not played a role in encouraging effective competition.

Open Tensor Foundation Validators (OTF) have implemented some temporary measures to alleviate this situation. As the largest validator holding 23% of the staking rights (including delegation), OTF provides a channel for subnets to compete for more staked TAO: subnet owners can submit requests to OTF every week to adjust their staked TAO ratio in the subnet. These requests must cover 10 aspects, including "subnet goals and contributions to the Bittensor ecosystem", "subnet reward mechanism", "communication protocol design", "data source and security", "computing requirements" and "roadmap", etc., to facilitate the final decision of OTF.

However, to fundamentally solve this problem, on the one hand, we urgently need to launch dTAO (Dynamic TAO), which aims to fundamentally change the above unreasonable problems. Alternatively, we can call on large validators holding a large amount of Stake TAO to consider the long-term development of the Bittensor ecosystem more from the perspective of "ecosystem development" rather than just from the perspective of "financial returns".

To sum up, with its strong inclusiveness, fierce competition environment and effective incentive mechanism, we believe that the Bittensor ecosystem can organically produce high-quality AI products. Although not all outputs of existing subnets are comparable to those of centralized products, we should not forget that the current Bittensor architecture has just been established for one year (Subnet 1 was registered on April 13, 2023). For a platform that has the potential to compete with centralized AI giants, perhaps we should focus on proposing practical improvement plans instead of rushing to criticize its shortcomings. After all, none of us want to see AI constantly controlled by a few giants.

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