Best AI tools for< hci researcher >

Victor Dibia is an expert in Applied Machine Learning and HCI. He is currently a Principal Research Software Engineer at the Human-AI eXperiences (HAX) team, Microsoft Research where he focuses on Generative AI. His research interests are at the intersection of human-computer interaction (HCI), computational social science, and applied machine learning. His research has been published at conferences such as ACL, EMNLP, AAAI, and CHI and has received multiple best paper awards. His work has also been featured in outlets such as the Wall Street Journal and VentureBeat. He is an IEEE Senior member, a Google Certified Professional (Data Engineer, Cloud Architect), and currently a Google Developer Expert in Machine Learning. He holds a PhD in Information Systems from City University of Hong Kong (recipient of the HKPFS scholar award by the Hong Kong Research Grants Council). His dissertation studied developer contribution behavior in software crowdsourcing contests - factors influencing participation, the impact of incentives on participation behavior, and the problem-solving process within crowdsourcing contests. Prior to City University, he studied at the Information Networking Institute at Carnegie Mellon University where he earned a Master's degree in Information Networking. He previously worked as a Principal Research Engineer at Cloudera Fast Forward Labs, Research Staff Member at IBM Research, Technical Lead for MIT Global Startup Labs, Researcher at the Innovation Management Lab, Athens Information Technology Athens Greece, and founder/lead developer for a small startup focused on West African markets.

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

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

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.

Instructor is a tool designed to structure outputs from OpenAI and other OSS LLMs by coaxing them to return JSON that maps to a provided Ecto schema. It allows for defining validation logic to guide LLMs in making corrections, and supports automatic retries. Instructor is primarily used with the OpenAI API but can be extended to work with other platforms. The tool simplifies usage by creating an ecto schema, defining a validation function, and making calls to chat_completion with instructions for the LLM. It also offers features like max_retries to fix validation errors iteratively.

Countdown timers to keep track of a bunch of CV/NLP/ML/RO conference deadlines.

Aiwnios is a HolyC Compiler/Runtime designed for 64-bit ARM, RISCV, and x86 machines, including Apple M1 Macs, with plans for supporting other architectures in the future. The project is currently a work in progress, with regular updates and improvements planned. Aiwnios includes a sockets API (currently tested on FreeBSD) and a HolyC assembler accessible through AARCH64. The heart of Aiwnios lies in `arm_backend.c`, where the compiler is located, and a powerful AARCH64 assembler in `arm64_asm.c`. The compiler uses reverse Polish notation and statements are reversed. The developer manual is intended for developers working on the C side, providing detailed explanations of the source code.