The Baltimore Orioles should be good, but they are not good. At 15-24, they are one of the worst teams in all of Major League Baseball this season, an outcome thus far that fans, experts, and the team itself will tell you are either statistically improbable or nearing statistically impossible based on thousands upon thousands of simulations run before the season started.
Trying to figure out why this is happening is tearing the fanbase apart and has turned a large portion of them against management, which has put a huge amount of its faith, on-field strategy, and player acquisition decision making into predictive AI systems, advanced statistics, probabilistic simulations, expected value positive moves, and new-age baseball thinking in which statistical models and AI systems try to reduce human baseball players into robotic, predictable chess pieces. Teams have more or less tried to “solve” baseball like researchers try to solve games with AI. Technology has changed not just how teams play the game, but how fans like me experience it, too. — Read More
Daily Archives: May 16, 2025
Company Regrets Replacing All Those Pesky Human Workers With AI, Just Wants Its Humans Back
Two years after partnering with OpenAI to automate marketing and customer service jobs, financial tech startup Klarna says it’s longing for human connection again.
Once gunning to be OpenAI CEO Sam Altman’s “favorite guinea pig,” Klarna is now plotting a big recruitment drive after its AI customer service agents couldn’t quite hack it.
The buy-now-pay-later company had previously shredded its marketing contracts in 2023, followed by its customer service team in 2024, which it proudly began replacing with AI agents. Now, the company says it imagines an “Uber-type of setup” to fill their ranks, with gig workers logging in remotely to argue with customers from the comfort of their own homes. — Read More
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning
We introduce INTELLECT-2, the first globally distributed reinforcement learning (RL) training run of a 32 billion parameter language model. Unlike traditional centralized training efforts, INTELLECT-2 trains a reasoning model using fully asynchronous RL across a dynamic, heterogeneous swarm of permissionless compute contributors.
To enable a training run with this unique infrastructure, we built various components from scratch: we introduce PRIME-RL, our training framework purpose-built for distributed asynchronous reinforcement learning, based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers, and SHARDCAST, which efficiently broadcasts policy weights from training nodes to inference workers.
Beyond infrastructure components, we propose modifications to the standard GRPO training recipe and data filtering techniques that were crucial to achieve training stability and ensure that our model successfully learned its training objective, thus improving upon QwQ-32B, the state of the art reasoning model in the 32B parameter range.
We open-source INTELLECT-2 along with all of our code and data, hoping to encourage and enable more open research in the field of decentralized training. — Read More
Rick's Cafe AI 