Research & Education

BRAID UK is a 3-year national research programme funded by the UKRI Arts and Humanities Research Council (AHRC), led by the University of Edinburgh in partnership with the Ada Lovelace Institute. It focuses on integrating Arts, Humanities, and Social Science research more fully into the Responsible AI ecosystem. The program aims to bridge the divides between academic, industry, policy, and regulatory work on responsible AI, with over £18 million in funding from 2022 to 2028. BRAID UK offers various projects including demonstrator projects, fellowships, scoping projects, and artist commissions, alongside opportunities for flexible impact funding and a Responsible AI Innovation course for SMEs.

10,000 Original AI Godfathers is presented as a digital time capsule, highlighting individuals who played significant roles in the foundational stages of artificial intelligence. This initiative aims to document and recognize researchers, scientists, engineers, investors, and dedicated AI users, collectively referred to as 'AI Godfathers.' The project seeks to preserve the legacy of these early contributors, offering insights into the diverse talents and efforts that shaped the nascent AI landscape. While the website content currently displays a parked domain message, the original concept suggests a focus on historical documentation and recognition within the AI community.

Autopilot-Notes is a comprehensive open-source knowledge base designed for systematic learning and mastery of autonomous driving technology. It covers a wide array of topics including foundational theories, hardware components, perception algorithms, localization techniques, planning strategies, and control systems. The repository also features in-depth analyses of solutions from leading manufacturers like Tesla, Baidu Apollo, and Huawei ADS. With daily updates on industry news and technical advancements, Autopilot-Notes serves as an invaluable resource for students and developers looking to stay current with the rapidly evolving field of autonomous vehicles. It emphasizes practical application with content on simulation, deployment, and optimization.

Ellen AI is a smart AI companion with voice capabilities, offering users the opportunity to own and customize their own AI. The product includes full source code for the project and a chatbot template, along with a step-by-step video guide on creation and customization. Users can rebrand, resell, and improve the source code, making it a versatile tool for developers and entrepreneurs. It allows for the application of the same concept to build various AI products such as AI girlfriends, chatbot assistants, and extensions with voice. The product is available for a one-time purchase, granting perpetual ownership and access to the code immediately, with the video guide added shortly after purchase.

Know Center is a leading European innovation and research center specializing in trustworthy AI and data science. They focus on transforming the latest findings in data science and AI into concrete competitive advantages for businesses across industries like energy, healthcare, and manufacturing. The center conducts high-impact research in areas such as Data Management for AI, Data Privacy for AI, Methods & Algorithms for AI, Human-AI Interaction, and Fair AI. They also offer solutions for industrial AI, enterprise search, intelligent predictions, and AI strategy consulting, aiming to ensure data and technological sovereignty for Europe.

Giotto-tda is a high-performance topological machine learning toolbox implemented in Python, designed to facilitate advanced data analysis and machine learning research. Built on top of the scikit-learn ecosystem, it offers robust algorithms for topological data analysis (TDA). The toolbox is part of the Giotto family of open-source projects and is distributed under the GNU AGPLv3 license. It supports Python 3.7+ and integrates with popular libraries like NumPy, SciPy, and Plotly. Giotto-tda is the result of a collaborative effort between L2F SA, EPFL, and HEIG-VD, making it a reliable tool for researchers and data scientists working with complex datasets.

The Institute for Computational Mechanics (Wall Lab) at the Technical University of Munich (TUM) is dedicated to cutting-edge research in computational mechanics. Their work spans application-motivated fundamental research, with a particular emphasis on complex coupled multifield and multiscale problems across various engineering and applied science domains. The institute's activities encompass advanced modeling techniques, the development of novel computational methods, and the creation of specialized software for high-performance computing systems. This focus enables them to tackle challenging scientific and engineering questions, contributing to advancements in fields requiring sophisticated simulation and analysis.

InterpretableMLBook is the Chinese translation of "Interpretable Machine Learning" by Christoph Molnar, a highly regarded work in the field of interpretable machine learning. This book serves as a comprehensive guide to understanding the interpretability of black-box models, making complex concepts accessible to a Chinese-speaking audience. It systematically organizes interpretability methods, describing each through intuitive language and detailed mathematical formulas. A key feature is the practical application of each method to real-world data, allowing readers to truly grasp their utility. The book also includes critical discussions on the advantages and disadvantages of various methods, making it a valuable resource for both technical practitioners and researchers.

KB2E is a knowledge graph embedding tool developed as a subproject of THU-OpenSK. It provides implementations for several prominent knowledge graph embedding algorithms, including TransE, TransH, TransR, and PTransE. These algorithms are crucial for representing entities and relations in a knowledge graph as low-dimensional vectors, enabling various downstream tasks like link prediction and entity classification. While the project offers valuable resources for researchers and developers interested in knowledge graph embeddings, it is important to note that KB2E is no longer actively maintained. Users are advised to transition to the newer and actively supported OpenKE package for continued development and support in this domain.

named_entity_recognition is an open-source project dedicated to Chinese named entity recognition (NER), offering practical implementations of several prominent models. It includes Hidden Markov Model (HMM), Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), and a hybrid BiLSTM+CRF model. The project utilizes a resume dataset for training and evaluation, providing detailed accuracy, recall, and F1 scores for each model. It serves as a valuable resource for researchers and developers interested in NLP, particularly in the context of Chinese NER, allowing for direct comparison and understanding of different algorithmic approaches.

PyHealth is a comprehensive, open-source deep learning Python toolkit designed to support clinical predictive modeling for both ML researchers and medical practitioners. It aims to make healthcare AI applications easier to develop, test, and deploy, offering flexibility and customizability. Key features include a modular 5-stage pipeline, a healthcare-first approach with support for medical codes and clinical datasets like MIMIC and eICU, and over 33 pre-built models with production-ready trainers and metrics. The toolkit supports more than 10 healthcare tasks and datasets, providing fast data processing for quick experimentation. PyHealth also includes independent modules for medical code mapping (pyhealth.medcode) and medical code tokenization (pyhealth.tokenizer), enhancing its utility for complex healthcare data.

Book7_Visualizations-for-Machine-Learning is an open-source GitHub repository offering a comprehensive educational resource for machine learning. It provides Python code examples for various machine learning algorithms, alongside detailed PDF explanations. The content covers a wide range of topics, from regression analysis and regularization to clustering and dimensionality reduction techniques. Designed to help users understand complex machine learning concepts through practical visualizations, this resource is particularly valuable for students and enthusiasts. The materials are primarily in Chinese, making it a significant resource for Chinese-speaking learners.

AI Matters EU is a Testing and Experimentation Facility (TEF) dedicated to validating new AI and robotics technologies within the manufacturing sector. The initiative aims to increase the flexibility of European manufacturing industries by deploying advanced AI solutions. It provides access to state-of-the-art facilities and real-world manufacturing data, enabling companies to test and mature their AI solutions. Services include XR-based training tools for industrial activities, XR-based digital twin simulations of assembly lines, XAI consulting, and workshops on vision technologies. Companies established in Europe can access these services, potentially with financial aid, to integrate and test their solutions in a real-world context. The project is a collaborative effort involving 22 partners across 8 European countries, bringing together expertise in various manufacturing sectors.

GROBID (Generation Of BIbliographic Data) is an open-source machine learning library designed to extract, parse, and restructure raw PDF documents, particularly technical and scientific publications, into structured XML/TEI encoded formats. Developed since 2008, it offers functionalities like header extraction (title, abstract, authors), reference parsing (with high F1-scores), citation context recognition, and full-text structuring (sections, figures, tables). GROBID also provides PDF coordinates for interactive augmented PDFs, name and affiliation parsing, and consolidation of references using services like biblio-glutton or CrossRef. It includes a comprehensive web service API, Docker images, and supports batch processing, making it suitable for large-scale scientific literature processing. Deployments include ResearchGate, Semantic Scholar, and HAL Research Archive.

Skeptic Reader is a web plugin for Chrome and Firefox designed to detect biases and logical fallacies in real-time. It acts as a personal "bullshit detector," fostering informed skepticism for a safer browsing experience by highlighting potential biases and logical inconsistencies in online content. The tool offers observable bias detection, logical fallacy identification, and even suggests counter-arguments for a well-rounded view. Powered by GPT4o, it analyzes content scoring metrics like balance, logic, and objectivity, and can even decode YouTube video transcripts for bias and fallacies. Developed by Domestic Data Streamers, it's presented as an experimental beta tool aimed at helping users ask better questions.

reasoning-gym is a Python library designed for training reasoning models using reinforcement learning. It offers a comprehensive set of dataset generators and reasoning environments, allowing users to create and manage training data with adjustable complexity. The tool provides access to over 100 distinct tasks, covering a wide range of reasoning challenges. This makes it a valuable resource for researchers and developers focused on advancing AI's reasoning capabilities, particularly those working with reinforcement learning approaches. While the provided content is from GitHub's pricing page, it indicates that the underlying project is likely open-source or free to use, given its presence on GitHub and the lack of specific pricing for the 'reasoning-gym' itself, suggesting it's a development framework rather than a commercial product.

Real3DPortrait is an open-source project providing a PyTorch implementation for one-shot realistic 3D talking portrait synthesis. It allows users to generate high-quality talking face videos from a single source image and a driving audio or video. The tool supports both audio-driven and video-driven methods for generating expressive 3D portraits. Key features include the ability to control mouth amplitude, map initial poses, and provide custom background images. It offers a command-line interface, a Gradio WebUI, and a Google Colab notebook for inference, making it accessible for various users. The project also provides training code for its audio-to-motion and image-to-plane models.

PDFSeek is an AI-powered document management tool designed to enhance productivity for students, researchers, and professionals. It allows users to upload PDF documents and interact with them through AI chat, summarization, and translation features. The platform supports multi-language translation, intelligent recognition of multi-column content, and the ability to retain and translate text within charts and formulas. Users can organize multiple PDFs into folders and chat with them simultaneously, with built-in citations linking responses directly to the original PDF content. PDFSeek aims to simplify document interaction, making it easier to understand complex information and extract key insights without extensive reading.

pytorch-original-transformer offers a PyTorch implementation of the original transformer model by Vaswani et al., designed to facilitate learning and experimentation with transformers. The repository includes a `playground.py` file with visualizations for complex concepts like positional encodings and custom learning rate schedules, making them easier to grasp. It also provides pretrained models on the IWSLT dataset for English-German machine translation, demonstrating practical application. The tool supports training new models and inference, with well-commented code and setup instructions for a smooth user experience. It's an excellent resource for anyone looking to understand and work with the foundational transformer architecture.

Creax is an innovation agency with over 25 years of experience, partnering with global innovators to identify future playing fields and address complex challenges. The agency offers services across three key areas: Visionary Roadmaps to sharpen innovation journeys, Smart Opportunities to uncover new markets and applications through emerging trends, and Sustainable Solutions to rethink products and processes with data-driven insights. Creax emphasizes a unique methodology that combines continuous data analysis with creative exploration, helping clients develop next-gen solutions, improve innovation processes, reduce risk, and accelerate progress. They have successfully completed over 1,250 projects across diverse industries.

3D-GRAND is a Densely-Grounded 3D-LLM designed to bridge the gap between natural language descriptions and 3D environments. Users can select a 3D scene and input a query to describe specific objects or locations. The tool then provides visual highlights of the relevant objects directly overlaid on the 3D model, offering a unique way to interact with and understand 3D data through text. This AI tool facilitates research in 3D understanding and grounding, making complex 3D scenes more accessible and interpretable through language-based interaction. It is available as a Hugging Face Space, indicating its potential for academic and research-oriented applications.

3DOI is a research tool hosted on Hugging Face Spaces, designed for the academic exploration of 3D object interaction. The project focuses on understanding how 3D objects behave and interact when presented with only a single image as input. This tool is primarily intended for researchers and academics in the field of computer vision and artificial intelligence who are working on problems related to 3D reconstruction, scene understanding, and object manipulation from limited visual data. While the current live website indicates a runtime error preventing full functionality, the underlying goal is to provide a platform for experimentation and development in this specialized area.

RewardBench is an open-source benchmark and evaluation tool specifically designed for assessing the capabilities and safety of reward models, including those utilizing Direct Preference Optimization (DPO). The repository offers common inference code compatible with various reward models such as Starling, PairRM, OpenAssistant, and DPO. It ensures fair evaluation through standardized dataset formatting and testing procedures. Additionally, RewardBench includes robust analysis and visualization tools to help researchers and developers interpret results effectively. It supports quick evaluation of any reward model on any preference set, with features for logging model outputs and accuracy scores, and options for generative models (LLM-as-judge) and DPO models. The platform also facilitates contributing models to a public leaderboard and offers offline ensemble testing.

Satellite-Imagery-Datasets-Containing-Ships is a comprehensive GitHub repository that curates radar and optical satellite datasets specifically designed for ship detection, classification, semantic segmentation, and instance segmentation tasks. These datasets are invaluable for researchers and developers working in computer vision, machine learning, remote sensing, and maritime analysis. The repository details various datasets, including SSDD, OpenSARship, SAR-Ship-Dataset, AIR-SARShip, HRSID, LS-SSDD, and FUSAR-Ship, providing information on their authors, year, tasks supported, and direct access links. Each dataset entry includes specifics like image dimensions, spatial resolutions, polarization types, and annotation formats, making it a crucial resource for developing and evaluating algorithms for maritime surveillance and naval operations.