Sakana AI, a Tokyo-based research company, raised $30 million in seed funding led by Lux Capital and Khosla Ventures to develop nature-inspired foundation models based on evolution and collective intelligence. The round included major Japanese firms like NTT Group, Sony, and KDDI, making it among the first Japanese AI startups to secure top-tier Silicon Valley backing at seed stage. The funding enables the company to build a world-class AI lab in Japan and develop alternative foundation models outside the dominant transformer architecture paradigm.
Sakana AI received a supercomputing grant from Japan's government as one of seven institutions selected through the Generative AI Accelerator Challenge program. The grant provides access to a GPU cluster equipped with latest hardware for several months in 2024 to support foundation model development. The company plans to use the additional compute capacity to scale nature-inspired AI models and advance Japan's generative AI capabilities.
Sakana AI used LLMs to automatically discover new preference optimization algorithms for training other LLMs, a process they call LLM². They discovered Discovered Preference Optimization (DiscoPOP), which outperforms existing methods like DPO across multiple benchmarks. This approach reduces reliance on human researchers to manually design training algorithms and creates a self-referential feedback loop where AI improvements can accelerate future AI development.
Sakana AI released two image generation models trained on Japanese ukiyo-e artwork: Evo-Ukiyoe generates ukiyo-e-style images from Japanese text prompts, while Evo-Nishikie colorizes monochrome classical woodblock prints into multi-color versions. The models were trained on 24,038 high-quality digitized ukiyo-e images from Ritsumeikan University's Art Research Center, using LoRA fine-tuning and ControlNet techniques. Both models are now publicly available on HuggingFace for research and education, enabling new applications in cultural education, content creation, and digital preservation of classical Japanese literature.
Sakana AI released The AI Scientist, a system that uses large language models to autonomously conduct scientific research, generating full papers from idea conception through peer review without human intervention. Each generated paper costs approximately $15 to produce, and the system has created papers in areas like diffusion modeling and language modeling that score at 'Weak Accept' level on top machine learning conference standards. The system raises safety and ethical concerns around paper quality, reviewer workload, potential misuse, and the need for transparency when AI substantially generates research submissions.
Sakana AI, a Tokyo-based AI research company, announced a Series A funding round raising approximately $200M led by New Enterprise Associates, Khosla Ventures, and Lux Capital, with participation from NVIDIA and major Japanese financial and industrial firms. The company will leverage NVIDIA GPU access and collaborate on research, infrastructure, and community building to develop nature-inspired foundation models. Sakana AI aims to establish a world-class AI lab in Japan to help the country address demographic decline and geopolitical challenges while building competitive advantage in AI development.
Sakana AI proposes CycleQD, a method that evolves a population of specialized 8-billion-parameter language models using model merging and quality diversity techniques, rather than training a single large model. The framework was tested on three computer science tasks (coding, database operations, and OS operations) where it outperformed traditional fine-tuning and model merging baselines. This population-based approach creates diverse agents with complementary skills that can specialize in different domains while maintaining general capabilities, offering a more computationally sustainable path to developing capable AI agents.
Sakana AI released Fugu-Cyber, a specialized AI orchestration model designed for cybersecurity tasks, achieving 86.9% success on the CyberGym benchmark and 72.1% on CTI-REALM. The company emphasizes that strong performance on benchmarks alone does not solve real enterprise security challenges without human expertise, specialized workflows, and verification processes to validate AI-generated findings before deployment. Access to Fugu-Cyber requires manual approval and is available through their API with an updated acceptable-use policy limiting offensive applications.
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