Research & Education

ContentBlocks is a platform designed for coaches, marketers, and agencies to transform their expertise into personalized, outcome-driven funnels. Users can build smart quizzes that capture lead needs, goals, and pain points. The platform then leverages AI to generate comprehensive, personalized reports based on the lead's answers, incorporating the user's methodology and recommendations. These reports include action plans, research-backed insights, and interactive elements, creating trust and converting leads into clients. ContentBlocks also extracts brand elements from any URL to apply consistent branding to reports, and integrates with existing sales stacks for seamless lead management and follow-up. It aims to deliver actual outcomes rather than just content.

Rememl revolutionizes sales interactions by creating AI-powered clones of individuals. Users can generate interactive digital characters with just a few seconds of audio and a picture, allowing for personalized and comprehensive sales meetings. The platform enables customization of the replica's voice and style, offering a unique blend of Cameo and MidJourney functionalities. It also provides access to new forms of analytics derived from actual conversations, helping businesses scale conversations and learn from interactions. Rememl aims to enhance understanding between professionals and clients, leading to more meaningful, productive, and shorter engagements.

VideoKen is an AI-powered video platform designed to enhance engagement and learning from video content. It transforms traditional videos into interactive and immersive experiences, aiming to increase learner engagement by up to 3x. Key features include AI-generated video chapters for easy navigation, in-video quizzes to assess understanding and identify skill gaps, and comprehensive analytics to gain insights into learner behavior. VideoKen also offers VideoLake for organizing event and conference recordings into discoverable collections, and an AI-powered video search engine to help users find relevant information within videos in real-time. It is ideal for learning and development, e-learning, and making conference and event recordings more accessible and engaging.

Smarty positions itself as a "Chief of Everything," offering a unique blend of human strategic thinking and AI-powered execution to manage various business functions. It handles marketing, sales, operations, and customer success, aiming to free up CEOs and founders from day-to-day operational burdens. The service is designed for lean businesses, service providers, venture funds, and founders seeking strategic leverage without additional hires. Smarty's approach involves a senior operator who understands the business end-to-end, backed by AI infrastructure and a specialist team for tasks like content creation, design, and research. This system learns and compounds over time, building reusable workflows and custom tools to make operations smarter and more efficient each month.

The Icelandic Institute for Intelligent Machines (IIIM) is a newly established center dedicated to research in artificial intelligence, robotics, and advanced simulation. IIIM aims to catalyze innovation and high-technology research within Iceland, fostering advancements in these critical fields. The institute actively publishes scientific and technical papers, shares news on AI policy, and engages in featured projects. It also maintains a focus on AI ethics and gender equality, reflecting a commitment to responsible technological development. Through its work, IIIM contributes to the global understanding and application of intelligent machines.

ValgrAI is a non-profit foundation based in Valencia, Spain, dedicated to advancing Artificial Intelligence through education, research, and collaboration. It offers a range of specialized courses and master's programs in AI, catering to both individuals and organizations. The foundation aims to meet the technological needs of the business sector, foster new AI talent, and provide specialized training to professionals. ValgrAI is supported by the Valencian government, companies, and five public universities, coordinating efforts to drive innovation and contribute to the region's productive model. They also engage in various research projects and offer custom training solutions.

GPTWorld is an educational environment created to help users understand and master prompting techniques for language models. It provides a unique grid-world interface where users can issue instructions to an AI, which then generates code to control a game. This interactive approach allows for practical experimentation and observation of how language models interpret and execute commands in a grounded, simulated environment. The tool is particularly useful for testing the capabilities and limitations of AI in following complex instructions, making it an excellent resource for researchers, educators, and anyone interested in the practical application of large language models.

VIDIZMO EnterpriseTube is a Gartner-recognized, AI-powered enterprise video platform designed for secure video content management. It enables organizations to host and stream live and on-demand video content with advanced features like AI-powered video workflows, white-label branding, and adaptive bitrate streaming. The platform offers granular video analytics, robust security, encryption, and access controls, including multifactor authentication and single sign-on. Users can leverage AI for enhanced search capabilities through spoken words, tags, objects, and text within videos, as well as automatic AI-generated content tags, transcription, translation, summarization, and chaptering. It supports flexible deployment options to suit various enterprise and government needs.

Letrus is a pioneering educational technology program in Brazil that integrates artificial intelligence with pedagogical methods to significantly enhance reading and writing abilities for students in the final years of elementary school and throughout high school. The platform offers immediate feedback to students via AI, personalized learning paths based on data, and comprehensive assessment criteria aligned with textual genres and exam requirements like ENEM. For teachers, Letrus provides real-time performance data, engagement levels, and autonomy to complement AI-generated feedback, reducing correction time and allowing more focus on classroom instruction. School administrators benefit from performance reports, pedagogical support, and monitoring dashboards to track learning evolution across their networks. Recognized by UNESCO as a leading educational technology, Letrus has demonstrated proven efficacy, contributing to improved national exam rankings in states where it has been implemented.

Preschools Near Me is an AI-powered platform designed to simplify the process of finding, comparing, and applying to preschools. It leverages advanced AI technology to provide personalized recommendations based on location, program type, budget, and specific needs like teaching philosophies or special requirements. The platform offers comprehensive search capabilities with real-time availability updates and detailed program information, including curriculum, tuition fees, virtual tours, and parent reviews. Users can communicate directly with preschools through an in-app email system, manage multiple applications, track statuses, and receive automated follow-up reminders, streamlining the entire admissions journey for busy parents.

Molecule.one leverages frontier AI chemistry to accelerate drug discovery and development. The platform, Maria™, is a high-throughput robotic synthesis system that has mapped chemical reactivity through over 300,000 microliter experiments, enabling superhuman AI to achieve high synthesis success rates with diverse building blocks. Users can order unique molecules and hits on-demand, with the fastest tier shipped in 7 working days, or license Maria's AI for in-house chemistry to design better synthesis plans and conditions. The tool also offers end-to-end discovery services for highly novel hits within 4 weeks and custom AI model development for specific chemistry challenges.

papers is a GitHub repository that curates and summarizes research papers on deep learning. It offers a valuable resource for researchers and practitioners to quickly understand the core concepts of various academic papers without having to read the full text immediately. Each entry in the repository includes a link to the original paper and a review, making it easy to access both the source material and a concise overview. The repository is open-source, encouraging community contributions to expand its collection of summaries. It covers papers from various years, including significant works from 2012 to 2018, across topics like computer vision, neural networks, and natural language processing.

TexTeller is an end-to-end formula recognition model designed to convert images into corresponding LaTeX formulas with high accuracy and strong generalization abilities. Trained on 80 million image-formula pairs, it significantly surpasses previous models in data volume and diversity, enabling it to cover most usage scenarios. Key features include support for scanned images, handwritten formulas, and English/Chinese mixed formulas, along with OCR capabilities for both languages in printed images. TexTeller also offers paragraph recognition and a formula detection model trained on extensive datasets. It provides a web demo, a Python API, and a server for integration, making it a versatile solution for various formula recognition needs.

The Gemma Cookbook is a comprehensive collection of guides and examples designed to help developers get started with and understand Google's Gemma family of open models. It covers a wide range of Gemma variants, including the core Gemma models for text generation, CodeGemma for coding tasks, FunctionGemma for function calling, MedGemma for medical text and image comprehension, and PaliGemma for vision language tasks. The cookbook also features models like ShieldGemma for safety evaluation and TxGemma for therapeutic development. While the repository is deprecated, it serves as a valuable resource for exploring the capabilities and applications of these generative AI models, built from the same research as Gemini.

The ITU Artificial Intelligence and Data Science Application and Research Center is dedicated to advancing the fields of artificial intelligence and data science through robust research and development initiatives. Its core mission is to enhance Turkey's competitive standing in science and technology by fostering innovation and developing domestic products. The center achieves this through strong university-industry collaborations, ensuring that research translates into practical applications. Furthermore, it plays a crucial role in training a highly competent workforce in AI and data science, preparing future leaders and experts. The center also actively establishes national and international partnerships to broaden its impact and facilitate knowledge exchange, contributing to a global network of AI and data science advancements.

The tensorflow_cookbook is a comprehensive GitHub repository that serves as a practical guide for implementing machine learning algorithms with TensorFlow. It accompanies the Tensorflow Machine Learning Cookbook by Nick McClure, offering code examples across a wide range of topics. Users can explore chapters dedicated to linear regression, support vector machines, nearest neighbor methods, neural networks, natural language processing, and convolutional neural networks. The repository details how to set up TensorFlow, work with tensors, variables, and operations, implement activation functions, and handle various data sources. It also covers advanced topics like computational graphs, loss functions, backpropagation, and taking TensorFlow models to production, making it an invaluable resource for both learning and applying TensorFlow in real-world scenarios.

trashnet offers a comprehensive dataset of trash images, categorized into six classes: glass, paper, cardboard, plastic, metal, and general trash. This dataset, comprising 2527 images, was collected using various iPhone models under natural lighting conditions and is available for download via Google Drive. Alongside the dataset, trashnet provides the code for a Torch-based convolutional neural network (CNN) designed for garbage image classification. The CNN, developed as a final project for Stanford's CS 229, has achieved approximately 75% test accuracy. The repository includes installation instructions for Lua and Python dependencies, as well as guidance for setting up CUDA for GPU acceleration, making it a valuable resource for students and researchers in machine learning and environmental studies.

TTRL (Test-Time Reinforcement Learning) is an open-source research project focused on advancing Reinforcement Learning (RL) techniques, particularly for scenarios where ground-truth labels are unavailable during inference. The project investigates how common practices in Test-Time Scaling (TTS), such as majority voting, can generate effective rewards to drive RL training. TTRL has demonstrated significant performance improvements, such as boosting the pass@1 performance of Qwen-2.5-Math-7B by approximately 211% on AIME 2024 using only unlabeled test data. The project provides code and experimental logs, with an implementation based on the 'verl' framework, making it accessible for researchers and developers to reproduce results and further explore test-time reinforcement learning.

torch-Video-Tutorials is a comprehensive collection of introductory video tutorials designed to guide users through the Torch ecosystem, a fast and flexible framework for Machine and Deep Learning. The resource aims to demystify the learning curve often associated with Torch by providing accessible video content. Each tutorial comes with accompanying slides, transcripts, and quizzes, which are available in the 'res' folder, along with notes on video creation. The tutorials cover fundamental concepts such as Lua and Torch's Tensor and image packages, delve into Artificial Neural Networks (feed-forward, backpropagation, and Torch's nn package), explore Convolutional Neural Networks (basics, internals, architectures, training, and loss functions), and introduce Recurrent Neural Networks (vectors, sequences, nngraph package, and training). Future content will include LSTM and training with the rnn package.

TinyZero offers a minimal reproduction of DeepSeek R1-Zero, focusing on reinforcement learning tasks. Built upon the veRL library, this tool allows 3B base Large Language Models (LLMs) to independently develop self-verification and search capabilities. The project provides scripts and instructions for data preparation and training, including configurations for single GPU and multi-GPU setups, and supports instruct ablation experiments. While the repository is no longer actively maintained, it serves as a valuable resource for understanding and replicating the core concepts of DeepSeek R1-Zero, particularly for researchers and developers exploring advanced RL techniques for LLMs.

tiny-llm provides a comprehensive course for system engineers focused on learning LLM inference serving, specifically tailored for Apple Silicon. The curriculum guides users through building a tiny vLLM using MLX and Qwen, with a codebase primarily utilizing MLX array/matrix APIs. This approach allows participants to construct model serving infrastructure from scratch, gaining deep insights into optimizations. The course covers essential components like attention, RoPE, KV cache, and continuous batching, with a roadmap extending to advanced topics such as Paged Attention and Speculative Decoding. It's designed for those who want to understand the underlying techniques for efficiently serving large language models.

Video-MME is the first-ever comprehensive evaluation benchmark designed to assess the capabilities of Multi-modal Large Language Models (MLLMs) in video analysis. It covers a wide range of visual domains, temporal durations, and data modalities, including short, medium, and long-term videos (from 11 seconds to 1 hour). The benchmark comprises 900 videos totaling 254 hours and 2,700 human-annotated question-answer pairs. It integrates multi-modal inputs beyond video frames, such as subtitles and audios, to provide a full-spectrum evaluation. Video-MME is suitable for both image MLLMs and video MLLMs, offering a robust framework for evaluating model performance in understanding and processing sequential visual data.

The Zero to Mastery Machine Learning repository offers a complete set of course materials for the Zero to Mastery Machine Learning and Data Science course. Hosted on GitHub, it provides code, Jupyter notebooks, images, and other resources designed to guide learners through various machine learning concepts and projects. The course covers fundamental libraries like NumPy, pandas, and Matplotlib, introduces Scikit-Learn, and delves into deep learning with TensorFlow/Keras. It features milestone projects such as heart disease classification and bulldozer price prediction, allowing students to apply their knowledge in practical, end-to-end scenarios. The materials are structured to support a 6-step machine learning modeling framework, making it an invaluable resource for students and aspiring data scientists.

Grand-Challenge.org offers a comprehensive platform for the development, evaluation, and deployment of machine learning solutions specifically tailored for biomedical imaging. It enables users to manage and upload medical imaging data securely, control access, and view data using browser-based workstations. The platform facilitates the training of expert annotators by allowing the creation of question sets for datasets and providing immediate feedback. Users can also gather annotations, customize hanging protocols, and benchmark algorithms for fair assessment. Furthermore, it supports the deployment of algorithms by allowing users to upload container images and manage access for researchers, making it a robust environment for collaborative AI development in the medical field.