Research & Education

awesome-llm-unlearning is a comprehensive repository dedicated to machine unlearning within large language models (LLMs). It serves as a centralized hub for the latest research and developments in this critical area. The repository curates and organizes a variety of resources, including academic papers, insightful surveys, practical frameworks, and informative blog posts. It is specifically designed to support researchers and developers who are actively engaged in or interested in the field of LLM unlearning, providing them with a valuable collection of materials to advance their understanding and work.

Awesome-LLMs-meet-Multimodal-Generation is a comprehensive, curated list of academic papers dedicated to the intersection of Large Language Models (LLMs) and multimodal generation. This repository serves as a valuable resource for researchers and developers interested in the latest advancements in generating various media types, including images, videos, 3D models, and audio, using LLMs. It aims to facilitate exploration and understanding of this rapidly evolving field by centralizing relevant research.

GenX is an intensive 10-day program specifically created for aspiring entrepreneurs in the generative AI space. The program is structured to guide participants through the process of building and launching their own generative AI startups. It features a series of interactive workshops and insightful talk shows, complemented by a dedicated builder's community for collaboration and networking. Participants are encouraged to develop and launch their ideas, with the added incentive of competing for prizes and potential investment opportunities.

awesome-local-llm is a comprehensive, curated list designed for individuals interested in running Large Language Models (LLMs) on their local machines. This resource compiles various platforms, tools, and essential resources, making it easier to navigate the landscape of local LLM deployment. It specifically includes categories such as inference platforms, engines, user interfaces, and a selection of specific models. The primary goal is to assist developers and researchers in identifying and utilizing the necessary components for effective local LLM deployment and experimental work.

BioSentVec is a specialized tool designed to provide pre-trained embeddings for biomedical text. It encompasses two main components: BioWordVec, which offers biomedical word embeddings utilizing the fastText model, and BioSentVec, which provides biomedical sentence embeddings based on the sent2vec model. This tool is primarily intended for researchers and developers working on biomedical text analysis tasks, enabling them to leverage advanced natural language processing techniques for understanding complex biological and medical literature.

Reality AI Lab is dedicated to the responsible application of artificial intelligence to address societal challenges. The lab's core mission revolves around democratizing AI technology, making it accessible and beneficial for a wider audience. It aims to position AI as a powerful catalyst for advancements in education, enhancing social mobility, and fostering equitable progress within diverse global communities. A key emphasis is placed on the ethical and responsible implementation of AI solutions.

Awesome-Video-Diffusion-Models is a comprehensive, curated list specifically focused on video diffusion models. It functions as a valuable resource for individuals involved in AI video generation, including researchers and practitioners. The repository offers direct links to a variety of models and their associated research papers, making it easier for users to explore and understand different approaches. Its primary purpose is to assist users in keeping abreast of the most recent developments and innovations within video diffusion technology.

The Mumbai AI Summit 2020 was a significant thought leadership event designed to explore and highlight the practical implementation of Artificial Intelligence across various industries. The summit's core objective was to demonstrate how AI can deliver tangible value to enterprises and different business functions. It served as a platform for facilitating in-depth discussions on the latest AI trends, emerging strategies, and best practices, bringing together experts and industry leaders to share insights and foster collaboration in the AI space.

Torus Engineering., JSC is an IT consulting and outsourcing firm dedicated to providing advanced AI solutions. Their core expertise lies in the medical, health, and dermatology sectors, where they leverage artificial intelligence to address specific industry challenges. As part of the broader Torus ecosystem, the company benefits from and contributes to AI research and development centers, ensuring cutting-edge solutions for their clients. They aim to deliver specialized AI-driven services and support to organizations within their target fields.

Transmute AI Lab operates as a research network with a primary focus on artificial intelligence. The lab's core mission is to develop and innovate cutting-edge AI technologies. It aims to achieve significant breakthroughs in the field of AI, with a particular emphasis on experiencing and understanding new realities that can be enabled or enhanced by artificial intelligence. This initiative brings together researchers and innovators to push the boundaries of current AI capabilities.

C is a comprehensive collection of algorithms, all implemented in the C programming language. This repository is specifically designed for educational use, covering a wide range of topics including mathematics, machine learning, computer science, and physics. It serves as an invaluable resource for both students and developers who are looking to deepen their understanding of fundamental algorithmic concepts and practice their implementation skills. The project aims to facilitate learning and practical application of complex algorithms.

SOMA is an AI-powered research platform specifically designed to identify and analyze relationships between various factors and medical conditions. It streamlines and automates complex research processes, significantly assisting scientists and medical researchers in their quest for new discoveries. The platform's core objective is to accelerate the pace of medical research by providing AI-driven insights, enabling more efficient and effective exploration of health-related data.

Chat-UniVi is a tool designed to empower large language models (LLMs) by integrating image and video understanding capabilities. It achieves this through the use of a unified visual representation, allowing LLMs to process and interpret visual data more effectively. This tool is primarily aimed at AI research and development, providing a foundation for building advanced multimodal AI applications. Its availability on GitHub suggests a focus on open-source contributions and community-driven development.

BrushNet is an implementation of the ECCV 2024 paper, "BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion." This tool is specifically designed for advanced image inpainting tasks, leveraging the power of diffusion models to fill in missing or corrupted parts of images seamlessly. It provides a robust framework for researchers and developers working in image generation and processing, enabling them to experiment with and integrate state-of-the-art inpainting capabilities into their projects. The dual-branch diffusion approach allows for more refined and context-aware image reconstruction.

box-convolutions offers a PyTorch implementation of the box convolution layer, as detailed in the research paper "Deep Neural Networks with Box Convolutions." This tool is designed to facilitate experimentation and replication of the research findings related to box convolutions within convolutional neural networks. It serves as a valuable resource for AI researchers and deep learning practitioners who are interested in exploring advanced convolution techniques.

CipherChat is a framework designed to assess how well safety alignment in large language models (LLMs) generalizes. Specifically, it investigates the transferability of safety measures to non-natural languages, such as ciphers. This tool provides a systematic approach for researchers to identify and understand the limitations of existing safety mechanisms within LLMs, particularly when faced with inputs outside of typical natural language contexts. It aims to shed light on the robustness and effectiveness of safety alignments.

LinguaBot is an AI-driven application specifically designed to help users practice and improve their conversational fluency in Spanish and Portuguese. The platform focuses on interactive learning experiences, providing a dynamic environment where users can engage in conversations to enhance their language abilities. It aims to facilitate practical language acquisition through direct application and practice, making it easier for learners to build confidence and proficiency in these languages.

Sinkove is an AI-powered tool specifically designed to generate synthetic biomedical images. Its primary purpose is to accelerate clinical research by providing realistic image data. This capability is particularly useful for applications such as data augmentation, where additional diverse data is needed to train and validate AI models, and other medical imaging research. By offering a source of synthetic yet realistic images, Sinkove aims to overcome limitations often associated with acquiring and using real patient data, such as privacy concerns and data scarcity.

InstaLingo is an AI-powered mobile application that facilitates image text extraction, translation, and pronunciation. Users can easily capture text from images and have it translated into various languages. The app also offers pronunciation assistance, making it a valuable tool for understanding and speaking foreign languages. Its features are particularly beneficial for individuals who travel frequently or are actively engaged in language learning.

Conditional_Diffusion_MNIST is an implementation of a conditional diffusion model designed to generate MNIST digits. This model leverages a U-Net architecture and incorporates principles from 'Classifier-Free Diffusion Guidance' to produce digits based on specified class labels. It serves as a minimal script, making it suitable for individuals looking to learn about or experiment with conditional diffusion models in a straightforward manner.

Controllable-RAG-Agent is an advanced Retrieval-Augmented Generation (RAG) solution engineered for complex question answering. It leverages sophisticated graph-based algorithms to enhance retrieval capabilities, moving beyond simple semantic similarity. This tool is particularly useful for managing and executing tasks that require more nuanced and structured information retrieval. It is designed to cater to the needs of developers and researchers working on advanced AI applications.

CoOp is a research project dedicated to advancing prompt learning within vision-language models. It provides a comprehensive codebase designed to facilitate the adaptation of existing models, such as CLIP, to various downstream datasets. The project specifically supports and implements techniques like conditional prompt learning and learning to prompt. CoOp is primarily intended for researchers and academics who are actively working on the adaptation and fine-tuning of vision-language models for specific applications or datasets.

ddpo-pytorch provides a PyTorch implementation of the Denoising Diffusion Policy Optimization (DDPO) technique. This tool is specifically designed for finetuning diffusion models, offering support for low-rank adaptation (LoRA) to enhance efficiency. A key feature is its ability to perform GPU-based finetuning of Stable Diffusion models while significantly reducing memory requirements. It is built for developers and researchers working with diffusion models and requires Python 3.10 or newer to operate.

RAI Alliance is a non-profit organization focused on promoting Responsible Artificial Intelligence (RAI) practices, initially in Kenya and with broader ambitions. Its core mission is to equip individuals, organizations, and communities with the necessary knowledge and resources to effectively navigate the complex ethical landscape of AI. The alliance actively advocates for and promotes key principles such as transparency, fairness, and accountability in the development and deployment of AI technologies.