Name: Bittensor
Brand: Bittensor
SKU: TAO
Price: 24549.805 USD
Availability: InStock
Rating: 4.5 (30 reviews)

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Demonstrates high technological feasibility and relatively rich application scenarios, with major products already launched and continuously iterated, contributing to a stable ecosystem. Multiple automated metrics reflect significant industry attention and support, steady development, and highly manageable risks.

Bittensor

Mudrex

Market Statistics

Bittensor (TAO) is a native cryptocurrency token that powers the Bittensor network, an open-source protocol for a decentralized machine learning and artificial intelligence (AI) network. The Bittensor ecosystem aims to create a marketplace where AI models can train collaboratively, share insights, and be rewarded based on the value they provide. TAO tokens incentivize miners and validators within the network, facilitate transactions, and participate in governance decisions.

The tokenomics of Bittensor (TAO) are designed to incentivize participation and contribution to the network. The total supply of TAO tokens is capped at 21 million, with a halving mechanism similar to Bitcoin, where the reward for mining new blocks is reduced by half approximately every four years. TAO tokens are distributed as rewards to miners and validators for their contributions to the network’s intelligence and computational resources. The distribution is based on the value of contributions recorded on the network’s digital ledger, and tokens used for recycling registrations are burned, extending the halving data accordingly.

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Bittensor addresses the centralization of AI development by creating a decentralized network that incentivizes and rewards the collaborative creation and sharing of AI models. This approach aims to democratize AI, allowing more equitable access to AI resources and fostering innovation through a global community of contributors.

Jacob Robert Steeves and Ala Shabaana co-founded Bittensor. The team behind Bittensor comprises individuals with expertise in blockchain technology, AI, and machine learning. Their backgrounds contribute to developing a platform that integrates these technologies to solve complex problems in the AI space.

The initial coin offering (ICO) details of the Bittensor (TAO) Token are unknown.

The Bittensor whitepaper begins by framing machine intelligence as a commodity extensively mined from data, monetarily valuable, transferable, and generally applicable. The current production of machine intelligence relies heavily on benchmarking, where machine learning models are trained to excel at narrowly defined supervised problems. However, this approach needs to be improved in its ability to reward intelligence production as a commodity, often resulting in centralization and the loss of diverse intelligence systems. Bittensor proposes a new market framework where intelligence is measured and priced by other intelligence systems across a peer-to-peer network. In this market, models are ranked for their informational production, regardless of the specific tasks or datasets used to train them. This system aims to reward a broader spectrum of intelligence production and reduce centralization by enabling standalone engineers to monetize their work directly.

The Bittensor network operates on a peer-ranking mechanism, where neural networks train to learn the value of their neighbors and rank each other accordingly. These ranks accumulate on a digital ledger, and high-ranking peers are rewarded monetarily with additional weight in the network. To prevent collusion and ensure the accuracy of the ranking mechanism, Bittensor employs an incentive mechanism that maximally rewards honestly selected weights. This makes the system resistant to collusion by up to 50 percent of the network weight. The result is a collectively run intelligence market that continually produces newly trained models and compensates contributors who add value. The whitepaper also discusses the importance of standard encoding for inputs and outputs to allow various model types to interact within the network. This standardization is crucial for the network’s expansion into multi-modality, where successful models should accept inputs from any modality and process them into a proper representation.

Bittensor acknowledges the challenges of scaling the network, particularly as outward bandwidth becomes a bottleneck. To address this, the whitepaper suggests conditional computation, where peers learn to efficiently select and prune neighbors in the network. Techniques like product essential layers or sparsely gated layers can be employed to reduce network transfer and determine a sparse combination of peers to query for each example. This approach can significantly increase outward bandwidth, allowing peers to communicate with many more neighbors in the graph. The conditional layer acts as a trainable DNS lookup for peers based on inputs and serves as a proxy for the weights in the network. The whitepaper concludes by emphasizing the potential of Bittensor to foster an interactive, open-ended ecosystem for AI development, tapping into a global supply of computing power and innovation.