Artificial Intelligence

found on twitta, sry guis.😬

https://nitter.net/KirkegaardEmil/status/1927868673743573270#m

fuck yeah, here it goes off! lets goo! :D

https://legacy.lmarena.ai/

nice! it actually follows me now!

• Executive Summary.
• Interactive Summary

Dramatic advances in artificial intelligence over the past decade (for narrow-purpose AI) and the last several years (for general-purpose AI) have transformed AI from a niche academic field to the core business strategy of many of the world’s largest companies, with hundreds of billions of dollars in annual investment in the techniques and technologies for advancing AI’s capabilities.

We now come to a critical juncture. As the capabilities of new AI systems begin to match and exceed those of humans across many cognitive domains, humanity must decide: how far do we go, and in what direction?

AI, like every technology, started with the goal of improving things for its creator. But our current trajectory, and implicit choice, is an unchecked race toward ever-more powerful systems, driven by economic incentives of a few huge technology companies seeking to automate large swathes of current economic activity and human labor. If this race continues much longer, there is an inevitable winner: AI itself – a faster, smarter, cheaper alternative to people in our economy, our thinking, our decisions, and eventually in control of our civilization.

But we can make another choice: via our governments, we can take control of the AI development process to impose clear limits, lines we won’t cross, and things we simply won’t do – as we have for nuclear technologies, weapons of mass destruction, space weapons, environmentally destructive processes, the bioengineering of humans, and eugenics. Most importantly, we can ensure that AI remains a tool to empower humans, rather than a new species that replaces and eventually supplants us.

This essay argues that we should keep the future human by closing the “gates” to smarter-than-human, autonomous, general-purpose AI – sometimes called “AGI” – and especially to the highly-superhuman version sometimes called “superintelligence.” Instead, we should focus on powerful, trustworthy AI tools that can empower individuals and transformatively improve human societies’ abilities to do what they do best. The structure of this argument follows in brief.

Traditional autoregressive language models generate text sequentially, one token at a time, leading to slower outputs with limited coherence and quality.

Diffusion models are an alternative approach. Instead of direct prediction, they iteratively refine noise, enabling faster generation, dynamic error correction, and greater control. This makes them particularly effective for editing tasks, including in math and code.

https://github.com/ML-GSAI/LLaDA

MiMo-7B, a series of reasoning-focused language models trained from scratch, demonstrating that small models can achieve exceptional mathematical and code reasoning capabilities, even outperforming larger 32B models. Key innovations include:

• Pre-training optimizations: Enhanced data pipelines, multi-dimensional filtering, and a three-stage data mixture (25T tokens) with Multiple-Token Prediction for improved reasoning.
• Post-training techniques: Curated 130K math/code problems with rule-based rewards, a difficulty-driven code reward for sparse tasks, and data re-sampling to stabilize RL training.
• RL infrastructure: A Seamless Rollout Engine accelerates training/validation by 2.29×/1.96×, paired with robust inference support. MiMo-7B-RL matches OpenAI’s o1-mini on reasoning tasks, with all models (base, SFT, RL) open-sourced to advance the community’s development of powerful reasoning LLMs.

an in-depth discussion of mimo-7b >https://www.youtube.com/watch?v=y6mSdLgJYQY