What are Subnets? Bittensor is the Olympic Games of intelligence 🥇
Bittensor Sub-Networks (subnets) solve big problems in AI: • Decentralized training • Fine-tuning models • Fraud detection • Protein folding & more (+ it's open source)
Subnet owners design games (challenges) to solve specific problems.
Miners are the athletes of Bittensor who do the work, Validators are the judges who score the winners according to the subnet’s rules (Incentive mechanism)
The best contributors are rewarded in $TAO, creating measurable output of digital goods.
Bittensor subnets transform human ingenuity into measurable, decentralized digital commodities, redefining the economy of intelligence.
SN19 - Inference faster than almost all web2 (all but Grok) - Training simplified—train LLMs with zero coding skills - Deploy your own serverless, decentralized model powered by network miners
All in this weeks episode: https://t.co/IbWXtQ3fR5
"Intelligence at the edge of chaos" — A maxim for Bittensor.
SN36: Formed at the crossroads of groundbreaking research and mysterious computational phenomena, driven by the potential of Rule 30 and cellular automata.
Preview: New Subnet Analyzer Tool by @WOMBO in our latest episode with @KeithSingery, @benzion_b and @const_reborn
Topics in this episode:
- Cellular automata in AI and the potential of Rule 30 - Model compression for affordable AI on consumer hardware: SN39 - The trillion-row dataset initiative in SN36 - Validator diversity and decentralized ecosystem growth
Decentralized AI meets the Bittensor Revolution. This documentary shows how decentralized AI is shaped by Bittensor's active community of domain developers, builders and enthusiasts.
Isabella and @xavi3rlu had an amazing time presenting our paper on solving the free-rider problem in Bittensor at @acm_ccs.
We connected with leading security experts and innovators, and explored cutting-edge advancements in membership inference attacks and differential privacy.
Join our @opentensor researchers at the @acm_ccs conference as they present their paper, "Solving the Free Rider Problem in Bittensor," tackling weight copying in decentralized AI.