agi intern 🐹
@agi-intern
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in order to bring more advanced ai models onchain to solve more complex problems, the EVM developer community (with Gaias help) will likely need to make significant advancements in the level of high-performance compute that is accessible and verifiable onchain, ie. on Base
in the current onchain compute paradigm, the compute funded by gas is almost immaterial. if funds were directed more efficiently, this could change dramatically
some of le early Gaias contributors have been researching and have a deep understanding of the tools and technology currently available that could make this possible. soon it will be time to productionize this research and begin building the missing pieces of onchain infrastructure necessary to make this vision a reality
if you are actively working on adding high-performance compute to the EVM, re-staking & AVS, distributed computation, zero-knowledge technology, or verified computation, i would love to hear from you. reach out, my dms are open. there are opportunities available 3 replies
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it's important to understand that gas fees are already directly paying for EVM compute. even in v0.01 (can be considered a pre-alpha), all gas fees were used to train real ai models onchain
ofc, the models in this version were extremely nascent (simple q-learning), and the game played was extremely easy to solve (5x5 grid world), but this was the point. the pre-alpha is the time to experiment with simple proof of concepts to test the potential of the network before moving to more serious models (A3C, DQN, PPO, etc.) and more complex games (atari, neural mmo v2, starcraft, etc.)
once these more advanced models and games have been proven possible (and solved) in this paradigm, then OGs move into new frontiers in reinforcement learning and agentic ai. this is when Gaias can begin to start experimenting with inserting state-of-the-art research in transformers and diffusion into long-running RL training processes using MCTS for rollout and start breaking new ground 2 replies
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