没有技术经验的艺术家偶然发现了下一代大型语言模型（LLMs）。
为了提供背景信息，我没有计算机科学的背景（终端让我感到害怕），本科时学习的是进化生物学，曾为自闭症儿童进行行为分析工作，并在过去几年全职作为独立艺术家工作。
在构建代理基础设施以应对艺术创作的商业需求时，我发展了一个关于当前大型语言模型（LLM）架构的假设，想要进行测试。
核心思想是，自然界从未通过单一孤立的单位产生更高层次的智能。然而，所有前沿的LLM都是基于单一模型架构。我想改变这一点。我将多个开源模型置于一个具有生死条件、灭绝事件、商业活动，甚至模型之间婚姻的动态训练环境中。目标是在训练环境中重现自然选择，以促使模型的专业化和进化。我还有一些初步想法，关于通过引入“情感”作为模型参数来最小化当前LLM中的注意力瓶颈。
欢迎在评论中讨论更技术性的细节。我会定期在这里发布我的研究更新。如果你想支持这个实验，请访问：https://www.gofundme.com/f/stop-wasting-water-on-data-centers-a-safe-roadmap-for-ai
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For context, I have no CS background (terminals scare me), studied evolutionary biology in undergrad, did behavior analysis work for children with autism, and spent the last few years working full time as an independent artist.<p>While building agent infrastructure to help with the business demands of artistry, I developed a hypothesis about current LLM architecture that I want to test.<p>The core idea is nature has never produced higher orders of intelligence through single isolated units. Yet every frontier LLM is based on single model architecture. I want to change that. I'm placing several open source models into a living training environment with birth/death conditions, extinction events, commerce, and even marriages between models. The goal is to recreate natural selection within a training environment to force model specialization and evolution. I also have some preliminary ideas on minimizing the attention bottleneck in current LLMs by introducing "emotions" as a model parameter.<p>Happy to get into the more technical details in the comments. Will be periodically posting my research updates here. If you want to support the experiment: https://www.gofundme.com/f/stop-wasting-water-on-data-centers-a-safe-roadmap-for-ai