Hey Carsten, great to have you on the Knowledge Science Pulse podcast. Today we'll be diving into two recent papers that explore exciting advancements in AI and language models. Are you ready to get started?

#### Absolutely, Sigurd! I'm excited to discuss these groundbreaking papers with you. The first one, "OS-Copilot: Towards Generalist Computer Agents with Self-Improvement," introduces a framework for building AI agents that can interact with various elements of an operating system. 

#### That's right, and the key innovation here is FRIDAY, a self-improving embodied agent created using the OS-Copilot framework. FRIDAY demonstrates impressive performance on the GAIA benchmark, outperforming previous methods by 35%. What's particularly noteworthy is its ability to generalize to unseen applications.

#### Indeed, and this generalization is made possible by FRIDAY's self-directed learning capabilities. It autonomously proposes a curriculum of tasks related to unfamiliar applications and solves them, accumulating valuable tools and knowledge in the process. This allows FRIDAY to effectively master new applications with minimal supervision.

#### Moving on to the second paper, "Fishing for Magikarp: Automatically detecting under-trained tokens in large language models," the authors present a comprehensive analysis of tokenizers in Large Language Models or LLMs. They specifically target the issue of detecting untrained and under-trained tokens, which can lead to unwanted behavior in LLMs.

#### That's correct. The disconnect between tokenizer creation and model training can result in certain tokens, like the infamous "_SolidGoldMagikarp," being nearly or fully absent in training data. This paper develops effective methods for automatically detecting these problematic tokens through a combination of tokenizer analysis, model weight-based indicators, and prompting techniques.

#### The findings demonstrate the prevalence of under-trained tokens across various models and provide valuable insights into improving the efficiency and safety of language models. By identifying and addressing these issues, we can create more robust and reliable LLMs.

#### Absolutely. The authors also explore the potential for transferring their findings to closed-source models, leveraging the experience gained from inspecting a wide range of open models. This opens up exciting possibilities for improving even proprietary language models.

#### In conclusion, both papers make significant contributions to the field of AI and language modeling. OS-Copilot and FRIDAY showcase the potential for creating highly capable, general-purpose computer agents, while the work on detecting under-trained tokens provides a framework for enhancing the performance and safety of LLMs.

#### I couldn't agree more, Sigurd. These advancements bring us one step closer to realizing the dream of truly intelligent digital assistants that can seamlessly interact with our operating systems and applications. It's an exciting time for AI research, and I can't wait to see what the future holds!

#### That's a wrap for today's episode of Knowledge Science Pulse. Thanks for joining me, Carsten, and thanks to our listeners for tuning in. Stay curious, and we'll catch you next time!