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AI Ecosystem Intelligence Explorer

3D
AI Detection
AI Fundamentals

21 of 128 articles

CloudCompare - Open Source project

CloudCompare website entry page

3D
Applied AI
 
1/18/2025

Foundations of Large Language Models

This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into four main chapters, each exploring a key area: pre-training, generative models, prompting techniques, and alignment methods. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.

LLM
AI Fundamentals
 
1/17/2025

GitHub - potree/potree: WebGL point cloud viewer for large datasets

WebGL point cloud viewer for large datasets. Contribute to potree/potree development by creating an account on GitHub.

3D
Applied AI
 
1/17/2025

MeshLab

A new version of MeshLab and PyMeshLab has been released: 2023.12! Moreover, MeshLab is now available for download on the Microsoft Store! Check it out here!

3D
Applied AI
 
1/17/2025

trimesh 4.5.3 documentation

3D
Applied AI
 
1/17/2025

AliceVision | Photogrammetric Computer Vision Framework

AliceVision is a Photogrammetric Computer Vision framework for 3D Reconstruction and Camera Tracking.

Computer Vision
3D
 
1/17/2025

AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking

The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.

Ethics, Governance and Policy
Education
AI Fundamentals
 
1/11/2025

Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.OpenAI has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge distillation to imitate o1’s reasoning style, but their effectiveness is limited by the capability ceiling of the teacher model. Therefore, this paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning. Policy initialization enables models to develop human-like reasoning behaviors, equipping them with the ability to effectively explore solution spaces for complex problems. Reward design provides dense and effective signals via reward shaping or reward modeling, which is the guidance for both search and learning. Search plays a crucial role in generating high-quality solutions during both training and testing phases, which can produce better solutions with more computation. Learning utilizes the data generated by search for improving policy, which can achieve the better performance with more parameters and more searched data. Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1’s advancement, making meaningful contributions to the development of LLM.

LLM
AI Fundamentals
 
1/7/2025
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