Best AI tools for< Faculty Development Specialist >

The Harvard Crimson is a renowned university newspaper established in 1873, providing daily news, opinion pieces, arts and culture coverage, blogs, multimedia content, and sports updates. The publication covers a wide range of topics related to Harvard University and its community, including faculty news, student activism, campus events, and academic developments. With a focus on diversity and inclusion, The Harvard Crimson aims to inform and engage its readers with insightful and thought-provoking content.

Faculty AI is a leading applied AI consultancy and technology provider, specializing in helping customers transform their businesses through bespoke AI consultancy and Frontier, the world's first AI operating system. They offer services such as AI consultancy, generative AI solutions, and AI services tailored to various industries. Faculty AI is known for its expertise in AI governance and safety, as well as its partnerships with top AI platforms like OpenAI, AWS, and Microsoft.

Petal is a document analysis platform powered by generative AI technology. It allows users to chat with their documents, providing fully sourced and reliable answers by linking to their own knowledge bases. Users can train AI on their documents to support their work, ensuring centralized knowledge management and document synchronization. Petal offers features such as automatic metadata extraction, file deduplication, and collaboration tools to enhance productivity and streamline workflows for researchers, faculty, and industry experts.

MIT Sloan Teaching & Learning Technologies connects MIT Sloan to research-driven best practices, resources, and training in instructional technology and design. They help the community make an impact in the classroom and beyond. They offer various services such as trainings, practice sessions, how-to guides, consultations, and a teaching studio. Their latest news and announcements include supporting learning with AI-generated images, providing students with access to Microsoft Copilot, and making Microsoft Copilot available for faculty and staff.

mapEDU is an AI-powered curriculum mapping and exam tagging software designed specifically for healthcare professions schools. It uses natural language processing and machine learning to automatically extract relevant MeSH tags from existing digital content, map events/courses/programs with outcomes, and auto-tag exam questions. This provides healthcare professions schools with objective, actionable data to improve curriculum design, validate revisions, and enhance student performance analytics.

Helper Systems has developed technology that restores the trust between students who want to use AI tools for research and faculty who need to ensure academic integrity. With kOS (pronounced chaos), students can easily provide proof of work using a platform that significantly simplifies and enhances the research process in ways never before possible. Add PDF files from your desktop, shared drives or the web. Annotate them if you desire. Use AI responsibly, knowing when information is generated from your research vs. the web. Instantly create a presentation of all your resources. Share and prove your work. Try other cool features that offer a unique way to find, organize, discover, archive, and present information.

The Stanford Artificial Intelligence Laboratory (SAIL) is a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963. SAIL faculty and students are committed to developing the theoretical foundations of AI, advancing the state-of-the-art in AI technologies, and applying AI to address real-world problems. SAIL is a vibrant and collaborative community of researchers, students, and staff who are passionate about AI and its potential to make the world a better place.

The Center for Human-Compatible Artificial Intelligence (CHAI) is dedicated to building exceptional AI systems for the benefit of humanity. Their mission is to steer AI research towards developing systems that are provably beneficial. CHAI collaborates with researchers, faculty, staff, and students to advance the field of AI alignment and care-like relationships in machine caregiving. They focus on topics such as political neutrality in AI, offline reinforcement learning, and coordination with experts.

TeacherServer is a platform offering a wide range of free AI tools specifically designed for educators, from PreK-12 teachers to college faculty. The platform provides tools for various subjects and purposes, such as lesson plan generators, worksheet generators, and curriculum quiz makers. TeacherServer prioritizes speed, privacy, and security by using a locally installed AI server, ensuring faster results without delays and processing data locally to maintain user privacy. The platform is free to use, adheres to education guidelines, and offers a supportive community through forums and research briefs.

Carnegie Mellon University's School of Computer Science (SCS) is a world-renowned institution dedicated to advancing the field of computer science and training the next generation of innovators. With a rich history of groundbreaking research and a commitment to excellence in education, SCS offers a comprehensive range of programs, from undergraduate to doctoral levels, covering various specializations within computer science. The school's faculty are leading experts in their respective fields, actively engaged in cutting-edge research and collaborating with industry partners to solve real-world problems. SCS graduates are highly sought after by top companies and organizations worldwide, recognized for their exceptional skills and ability to drive innovation.

The Berkeley Artificial Intelligence Research (BAIR) Lab is a renowned research lab at UC Berkeley focusing on computer vision, machine learning, natural language processing, planning, control, and robotics. With over 50 faculty members and 300 graduate students, BAIR conducts research on fundamental advances in AI and interdisciplinary themes like multi-modal deep learning and human-compatible AI.

LawDroid is an AI legal assistant application designed to help legal professionals work smarter and more efficiently. It serves as a force multiplier, enabling users to accomplish more tasks without additional overhead. LawDroid offers features such as case law research, drafting emails and letters, and document analysis. It also provides benefits like intake new clients, data-driven decisions, automate documents, human in the loop automation, and building scalable tools. The application is used by students, faculty, and legal professionals to streamline legal processes and improve client experiences.

Edmin is an AI-driven education administration software that streamlines and modernizes administrative tasks in educational institutions. It offers a comprehensive Student Information System (SIS), compliance features, data security, and customizable modules to meet the specific needs of colleges. Edmin enhances operational efficiency by automating routine tasks, improving data consistency, and enhancing the student and faculty experience through self-service portals and real-time notifications. The platform also facilitates efficient academic planning, communication through surveys and feedback forms, and offers scalability for colleges of all sizes.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

This repository contains a comprehensive list of various summer schools and winter schools in the field of artificial intelligence, machine learning, medical imaging, and healthcare. It provides detailed information about upcoming events, including the name, venue, date, deadline, organizers, fees, and scholarship details. The repository aims to share opportunities with the community and aspiring AI researchers/engineers, data scientists.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

LlamaV-o1 is a Large Multimodal Model designed for spontaneous reasoning tasks. It outperforms various existing models on multimodal reasoning benchmarks. The project includes a Step-by-Step Visual Reasoning Benchmark, a novel evaluation metric, and a combined Multi-Step Curriculum Learning and Beam Search Approach. The model achieves superior performance in complex multi-step visual reasoning tasks in terms of accuracy and efficiency.

The 'awesome-mobile-robotics' repository is a curated list of important content related to Mobile Robotics and AI. It includes resources such as courses, books, datasets, software and libraries, podcasts, conferences, journals, companies and jobs, laboratories and research groups, and miscellaneous resources. The repository covers a wide range of topics in the field of Mobile Robotics and AI, providing valuable information for enthusiasts, researchers, and professionals in the domain.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

A curated list of Generative AI projects, tools, artworks, and models

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

ChainForge is a visual programming environment for battle-testing prompts to LLMs. It is geared towards early-stage, quick-and-dirty exploration of prompts, chat responses, and response quality that goes beyond ad-hoc chatting with individual LLMs. With ChainForge, you can: * Query multiple LLMs at once to test prompt ideas and variations quickly and effectively. * Compare response quality across prompt permutations, across models, and across model settings to choose the best prompt and model for your use case. * Setup evaluation metrics (scoring function) and immediately visualize results across prompts, prompt parameters, models, and model settings. * Hold multiple conversations at once across template parameters and chat models. Template not just prompts, but follow-up chat messages, and inspect and evaluate outputs at each turn of a chat conversation. ChainForge comes with a number of example evaluation flows to give you a sense of what's possible, including 188 example flows generated from benchmarks in OpenAI evals. This is an open beta of Chainforge. We support model providers OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and Dalai-hosted models Alpaca and Llama. You can change the exact model and individual model settings. Visualization nodes support numeric and boolean evaluation metrics. ChainForge is built on ReactFlow and Flask.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

WritingAIPaper is a comprehensive guide for beginners on crafting AI conference papers. It covers topics like paper structure, core ideas, framework construction, result analysis, and introduction writing. The guide aims to help novices navigate the complexities of academic writing and contribute to the field with clarity and confidence. It also provides tips on readability improvement, logical strength, defensibility, confusion time reduction, and information density increase. The appendix includes sections on AI paper production, a checklist for final hours, common negative review comments, and advice on dealing with paper rejection.

This repository aims to promote global equality for individuals pursuing a PhD in NLP by providing resources and information on various aspects of the academic journey. It covers topics such as applying for a PhD, getting research opportunities, preparing for the job market, and succeeding in academia. The repository is actively updated and includes contributions from experts in the field.

Text2Text is a comprehensive language modeling toolkit that offers a wide range of functionalities for text processing and generation. It provides tools for tokenization, embedding, TF-IDF calculations, BM25 scoring, indexing, translation, data augmentation, distance measurement, training/finetuning models, language identification, and serving models via a web server. The toolkit is designed to be user-friendly and efficient, offering a variety of features for natural language processing tasks.

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