We interviewed Keith Singery, a Bittensor validator and advocate, to gain insights into the project’s potential and its place within the broader AI landscape. Come explore Bittensor, a blockchain project pioneering a unique incentive system for artificial intelligence development. You don’t want to miss this interview or our recent podcast!
Keith Singery — A Bittensor Guru
Keith’s journey into crypto began in 2017 during his time working in Germany. After a brief departure in 2018, he returned to the crypto space in 2019. Dissatisfied with other business ventures, he sought a more scalable income source. In 2020, he located a crypto mining community on Discord and soon became a validator for the Bittensor project.
Moreover, wanting to further contribute to the community, Keith leveraged his experience by launching the “Bittensor Guru” podcast. This platform allowed him to share his knowledge and empower new members. Today, Keith is entirely dedicated to Bittensor. Having witnessed the strong bonds within the community and the project’s potential, he sees his future intertwined with its success.
Bittensor Explained
Keith described Bittensor as an open-source AI development platform powered by the TAO token. It allows anyone to participate by creating and submitting AI models on “subnets,” independent networks within the Bittensor ecosystem. Furthermore, subnet owners design systems where miners compete by submitting AI outputs, like images or text, for evaluation by validators. These validators, who are subnet owners themselves, implement mechanisms to assign value to the AI creations.
Notably, this process leverages AI models and GPUs to assess factors like coherence or similarity to a desired outcome. In essence, Bittensor functions as a vast AI development engine that capitalizes on the current boom in AI research. By combining its incentive structure with open-source contributions, Bittensor empowers creators to monetize their AI models and contribute to a growing repository of “distilled intelligence.”
Operating as a foundational layer for blockchain technology, the protocol, situated on the Polkadot substrate, maintains consensus and verifies peer identity while motivating network participants. Positioned directly beneath the AI layer, inter-process communication enables seamless interaction between these layers. Within the Bittensor network, a staked, weighted trust mechanism ensures fair distribution of incentives among peers, with higher-ranking peers receiving added rewards.
Notably, collective rankings, rather than individual assessments, dictate the blockchain’s decision-making process, fostering equity. Peers are required to register wallets and unique cryptographic keys for identification and engagement within the network.
Addressing Concerns about AI
We expressed concerns regarding potential issues with AI models, particularly those wielded by large corporations. These concerns centered on data provenance and bias as well as potential control and manipulation. Essentially, we wanted to know how companies were ensuring the data used to train AI models is reliable and also unbiased.
Keith shared similar anxieties about the pervasive influence of AI in our lives. He worries that corporations are manipulating AI models to control information flow and potentially indoctrinate users. He highlighted the role of human bias in training these models and the profit-driven motives of large corporations as factors potentially compromising the objectivity of AI-delivered information. Moreover, he believes this control over information, especially in education, could have negative consequences for critical thinking and independent learning.
We then discussed the potential of decentralized AI, like Bittensor, to address these concerns. Keith acknowledged the ongoing challenge of energy consumption for large compute networks like Bitcoin. However, he expressed optimism about future solutions, including renewable energy advancements and the creation of hubs specifically designed for energy-intensive AI development.
In terms of data provenance, Keith emphasized the importance of blockchain technology for tracking the training data used in AI models. This transparency would be crucial for ensuring responsible AI development, especially as regulations around AI come into place.
Bittensor’s Strengths and Limitations
Per Keith, Bittensor offers several advantages:
- Open-Source Development: Bittensor incentivizes the creation and sharing of AI models, fostering collaboration and innovation within the AI community.
- Data Provenance: By allowing miners to submit models traceable back to their training data stored on the blockchain, Bittensor promotes transparency and responsible AI development.
- Democratization of AI: Bittensor paves the way for a future where users can train and utilize custom AI models on a decentralized network, potentially making this powerful technology more accessible.
However, limitations exist in the current Bittensor iteration:
- Limited Compute Power: Each subnet has a finite number of miners, potentially restricting the available compute power for AI model training.
- Token Value Uncertainty: The current high value of the TAO token incentivizes miners across all subnets. However, this economic model might not be sustainable in the long run, as some subnets rely on external compute power for training models submitted by miners.
Despite these limitations, Keith remains optimistic about Bittensor’s future. He points to the rising interest in TAO, fueled by applications like Corcel.io, which allows users to interact with AI models built on Bittensor. This creates a positive feedback loop that attracts more users and developers to the platform.
Inside the Bittensor Subnets
Keith explained the structure of Bittensor’s subnets, each operating independently and interacting with others through emissions allocation. These subnets can be categorized into different functions:
- Utility Subnets: These subnets provide services to the network, like renting computing power or decentralized storage.
- AI Model Development Subnets: Subnets like three, six, and nine focus on AI model development. They host competitions where miners submit open-source models, with validators assessing and rewarding the best performers. These winning models are then made available for anyone to download and use.
- Image Generation Subnets: These subnets leverage open-source image generation models and incentivize miners to generate large volumes of images. Whenever a new open-source image generation model is released, the subnet incorporates it, allowing miners to leverage its capabilities.
“And now, with the intelligence of the people participating in the network — they are going in and pruning out the subnets that didn’t really grow into an effective branch that is contributing to the overall project. Once that is done — what you’ll see is an explosion of subnets on Bittensor”
The protocol’s subnets promote open-source development across various AI applications. From training foundational models to generating images, these subnets offer valuable resources for anyone interested in exploring and utilizing the latest advancements in AI.
In Conclusion
Through an insightful interview with Keith Singery, a Bittensor validator and advocate, we’ve gained valuable insights into the project’s innovative incentive system and its potential impact on the broader AI ecosystem. By democratizing access to AI development, promoting transparency through blockchain technology, and fostering collaboration within the community, Bittensor stands as a beacon of hope for a more inclusive and responsible AI future.
As we navigate the complexities and challenges of AI deployment, platforms like Bittensor offer a glimpse into a future where technology serves humanity’s best interests, driven by principles of openness, fairness, and innovation.
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