本文主要是介绍Microsoft Copilot 好像能把论文配图看明白了，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
🍉 CSDN 叶庭云：https://yetingyun.blog.csdn.net/
Microsoft Copilot 好像能把论文配图看明白了，下面是两个案例。
请用学术风格详细描述您的研究论文中的这幅配图。在描述时，请尽可能准确地阐述图片的主要元素、颜色、形状、大小等细节，以及图片与研究课题的关系。（PS. 测试用的这张配图来自论文：Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study）
这幅配图展示了一个复杂的人工智能机器人，该机器人正在进行自我反思、信息收集、任务推断、记忆、技能策划和行动计划等多个方面的学习和发展。机器人具有详细和精密的设计，其蓝色和灰色的外壳装饰着各种仪表和控制装置。机器人头部是一个大型透明屏幕，显示出内部复杂的电路和构造。
图片右侧描绘了机器人与三种不同类型的软件交互：日常软件、专业软件和数字游戏。这些软件通过图标清晰地表示，彩色图像帮助区分不同类别。
图片底部标注了 “北京人工智能学院（BAAI），中国”、“南洋理工大学（NTU），新加坡” 以及 “北京大学（PKU），中国” 的徽标和名称。这些信息表明了进行此项研究的主要学术机构。
该图片与我们研究课题紧密相关，因为它直观地展示了 AI 在自我发展、学习和与各种软件交互方面的能力。通过对这一过程进行深入分析，我们可以更好地理解 AI 在未来技术发展中所扮演角色以及其潜在影响。
Please describe this accompanying image from your research paper in detail and in an academic style. When describing it, be as accurate as possible in articulating the main elements of the image, details such as color, shape, size, etc., and how the image relates to the research topic. （PS. 测试用的这张配图来自论文：Can Large Language Models Reason and Plan?）
The image is a comparative diagram that illustrates the differences between two cognitive systems and a Language Learning Model (LLM).
-
System 1 and System 2: On the left side of the diagram, System 1 and System 2, which are part of human cognition, are outlined. System 1, labeled as “Reflexive,” is depicted in a yellow cloud shape. It represents knowledge either gleaned directly from the environment or compiled from System 2. An arrow labeled “Evolution” points towards it, indicating its development over time. System 2, also enclosed in a yellow cloud shape and labeled as “Deliberative,” represents reasoning from first principles and is connected to higher-order regularities like world models.
-
Language Learning Model (LLM): On the right side of the image, the LLM is enclosed in another yellow cloud shape. It represents “knowledge” either gleaned directly from digital traces of humanity or through fine-tuning on specific task data. It’s connected to an icon representing the internet and labeled “Pretraining” and another icon representing task-specific data labeled “Finetuning.”
-
Separation and Comparison: A blue vertical line separates these systems from the LLM. A question mark hovers between these cognitive systems and LLM, indicating an inquiry or comparison being made between them.
This diagram is likely used to compare and contrast the cognitive processes of humans and AI models, particularly in the context of language learning. The image serves to highlight the similarities and differences between these systems, providing a visual representation of how each system processes and learns information. The use of color and shape aids in distinguishing between the different elements of the diagram. The yellow cloud shapes encapsulate each system, while the blue line serves as a clear demarcation between human cognition and AI models. The icons and labels provide additional context and clarification. The question mark suggests an ongoing exploration or inquiry into these systems and their interrelationships. This image is integral to understanding the research topic as it visually encapsulates the key concepts and comparisons being made in the study.
这篇关于Microsoft Copilot 好像能把论文配图看明白了的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
![[论文笔记]LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://img-blog.csdnimg.cn/img_convert/172ed0ed26123345e1773ba0e0505cb3.png)
