Google DeepMind today pulled the curtain back on AlphaEvolve, an artificial-intelligence agent that can invent brand-new computer algorithms — then put them straight to work inside the company’s vast computing empire.
AlphaEvolve pairs Google’s Gemini large language models with an evolutionary approach that tests, refines, and improves algorithms automatically. The system has already been deployed across Google’s data centers, chip designs, and AI training systems — boosting efficiency and solving mathematical problems that have stumped researchers for decades.
AlphaEvolve is a Gemini-powered AI coding agent that is able to make new discoveries in computing and mathematics. — Read More
Daily Archives: May 15, 2025
Memory Layers at Scale
Memory layers use a trainable key-value lookup mechanism to add extra parameters to a model without increasing FLOPs. Conceptually, sparsely activated memory layers complement compute-heavy dense feed-forward layers, providing dedicated capacity to store and retrieve information cheaply. This work takes memory layers beyond proof-of-concept, proving their utility at contemporary scale. On downstream tasks, language models augmented with our improved memory layer outperform dense models with more than twice the computation budget, as well as mixture-of-expert models when matched for both compute and parameters. We find gains are especially pronounced for factual tasks. We provide a fully parallelizable memory layer implementation, demonstrating scaling laws with up to 128B memory parameters, pretrained to 1 trillion tokens, comparing to base models with up to 8B parameters. — Read More
DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level
Through a joint collaboration between the Agentica team and Together AI, we release DeepCoder-14B-Preview, a code reasoning model finetuned from Deepseek-R1-Distilled-Qwen-14B via distributed RL. It achieves an impressive 60.6% Pass@1 accuracy on LiveCodeBench (+8% improvement), matching the performance of o3-mini-2025-01-031 (Low) and o1-2024-12-17 with just 14B parameters. We’ve open-sourced our dataset, code, training logs, and systems optimizations for everyone to progress on scaling and accelerating intelligence with RL. — Read More
ChatGPT is used more for science in countries where it is prohibited
Regulating AI is a key societal challenge, but effective methods remain unclear. This study evaluates geographic restrictions on AI services, focusing on ChatGPT, which OpenAI blocks in several countries, including China and Russia. If restrictions were effective, ChatGPT usage in these countries should be minimal. We measured usage with a classifier trained to detect distinctive word choices (e.g., “delve”) typical of early ChatGPT outputs. The classifier, trained on pre- and post-ChatGPT “polished” abstracts, outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying our classifier to preprints from Arxiv, BioRxiv, and MedRxiv revealed ChatGPT use in approximately 12.6% of preprints by August 2023, with usage 7.7% higher in restricted countries. This gap emerged before China’s first major domestic LLM became widely available. To address whether high demand could have driven even greater use without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found higher usage in countries with restrictions. ChatGPT use correlated with increased views and downloads but not with citations or journal placement. Overall, geographic restrictions on ChatGPT appear ineffective in science and potentially other domains, likely due to widespread workarounds. — Read More
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