Best AI tools for< Ai Research Scientist >

Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

EleutherAI is an open-source AI research platform that focuses on discussing and disseminating cutting-edge research in the field of artificial intelligence. The platform provides updates on various research projects, including Mechanistic Anomaly Detection, Automated Interpretability for Sparse Autoencoder Features, Experiments in Generalization, Concept Erasure, Knowledge Elicitation, and more. EleutherAI aims to foster collaboration and innovation in the AI community by sharing insights and advancements in the field.

AlphaSignal is a leading technical newsletter in the field of Artificial Intelligence (AI), providing a daily 5-minute summary of the latest breakthrough news, models, research, and repositories. It aims to keep AI developers and researchers up to date with the most relevant topics discussed by top researchers in the industry. The newsletter covers state-of-the-art projects, notebooks, and GitHub repositories, offering valuable insights for practitioners in the AI domain.

The AI Security Institute (AISI) is a state-backed organization dedicated to advancing AI governance and safety. They conduct rigorous AI research to understand the impacts of advanced AI, develop risk mitigations, and collaborate with AI developers and governments to shape global policymaking. The institute aims to equip governments with a scientific understanding of the risks posed by advanced AI, monitor AI development, evaluate national security risks, and promote responsible AI development. With a team of top technical staff and partnerships with leading research organizations, AISI is at the forefront of AI governance.

Salesforce AI Blog is an AI tool that focuses on various AI research topics such as accountability, accuracy, AI agents, AI coding, AI ethics, AI object detection, deep learning, forecasting, generative AI, and more. The blog showcases cutting-edge research, advancements, and projects in the field of artificial intelligence. It also highlights the work of Salesforce Research team members and their contributions to the AI community.

Cue AI is an AI research lab dedicated to enhancing the capabilities of cutting-edge models. The lab is committed to pushing the boundaries of AI technology and innovation. While the website currently has limited information, it serves as a platform for sharing updates and developments in the field of artificial intelligence. For inquiries or collaborations, users can reach out via email at [email protected].

ScaDS.AI (Center for Scalable Data Analytics and Artificial Intelligence) is a research center focusing on Data Science, Artificial Intelligence, and Big Data with locations in Dresden and Leipzig. It is one of the five new AI centers in Germany funded under the federal government's AI strategy by the Federal Ministry of Education and Research and the Free State of Saxony. The center collaborates closely with TUD Dresden University of Technology and Leipzig University, aiming to bridge the gap between mass data utilization, knowledge management, and advanced AI methods.

The AI Conference 2024 is a groundbreaking vendor-neutral event that brings together researchers, engineers, and entrepreneurs to learn, collaborate, and network with some of the brightest minds in AI. The conference explores cutting-edge technologies, practical applications, and strategic insights in the field of artificial intelligence. Attendees can expect thought-provoking sessions, captivating talks, and valuable networking opportunities, all aimed at shaping the future of AI.

The AI+X Summit is the largest event focusing on AI research, development, and application in the German-speaking part of Switzerland. The event covers various tracks and workshops on topics such as AI in finance, urban mobility, human-centered design, generative AI, responsible AI, AI for science, and more. It aims to bring together industry professionals, startups, academia, and a wider ecosystem to explore the latest advancements in artificial intelligence and its applications across different sectors.

Bethge Lab is an AI research group at the University of Tübingen focusing on Neuro AI - Autonomous Lifelong Learning in Machines and Brains. They develop machine learning tools for neural data analysis and draw inspiration from the brain to address key problems in machine learning. Their research includes representation learning, probabilistic inference, generative modeling, behavioral data analysis, and neural data analysis. Additionally, they explore AI sciencepreneurship and collaborate with startups. Bethge Lab aims to advance the understanding of autonomous learning and develop economically feasible solutions for long-term human needs.

Megagon Labs is an AI research lab focusing on Knowledge Representation & Reasoning, Natural Language Processing, Human-Centered AI, and Data AI Symbiosis. They empower users with better information to make informed decisions through cutting-edge research and technologies. The lab's work includes investigating symbiotic systems, data management for AI, human-centered AI tools, and enhancing NLP capabilities for various domains.

Lex Fridman is an AI tool developed by Lex Fridman, a Research Scientist at MIT, focusing on human-robot interaction and machine learning. The tool offers various resources such as podcasts, research publications, and studies related to AI-assisted driving data collection, autonomous vehicle systems, gaze estimation, and cognitive load estimation. It aims to provide insights into the safe and enjoyable interaction between humans and AI in driving scenarios.

Kovil.AI is an AI-powered platform that connects businesses with top AI talents from India's largest network. The platform offers a vetting process to match businesses with hand-picked Indian developers, covering a wide range of expertise in AI, machine learning, data science, and more. Kovil.AI aims to empower ambitious businesses by providing access to specialized, high-caliber AI professionals, accelerating the hiring process, and reducing costs. The platform also offers managed services and products, ensuring flexibility, adaptability, and a competitive advantage for businesses seeking top talent.

Kyutai is an open science AI lab based in Paris, with a mission to build and democratize artificial general intelligence through open science AI research. The lab offers various resources and tools for AI enthusiasts and researchers to collaborate and innovate in the field of AI. Kyutai aims to foster a community of like-minded individuals who are passionate about advancing AI technology through open collaboration and research.

The Digital Data Design Institute at Harvard is a leading research institute focusing on the interaction between society, business, and artificial intelligence (AI) and digital technologies. Through 14 labs and six communities of practice, the institute produces research-rooted knowledge to explore the impact of AI on various aspects of life and work.

ArxivPaperAI is an AI-powered research paper summarizer that helps you quickly and easily understand the key points of academic papers. With ArxivPaperAI, you can:

Semantic Scholar is a free, AI-powered research tool for scientific literature. It is based at the Allen Institute for AI and provides access to over 217 million papers from all fields of science. Semantic Scholar uses AI to help users discover and explore scientific literature, and to stay up-to-date on the latest research. The tool also includes a number of features to help users manage their research, such as the ability to save papers, create bibliographies, and share research with others.

The Center for AI Safety (CAIS) is a research and field-building nonprofit based in San Francisco. Their mission is to reduce societal-scale risks associated with artificial intelligence (AI) by conducting impactful research, building the field of AI safety researchers, and advocating for safety standards. They offer resources such as a compute cluster for AI/ML safety projects, a blog with in-depth examinations of AI safety topics, and a newsletter providing updates on AI safety developments. CAIS focuses on technical and conceptual research to address the risks posed by advanced AI systems.

Athina AI is a platform that provides research and guides for building safe and reliable AI products. It helps thousands of AI engineers in building safer products by offering tutorials, research papers, and evaluation techniques related to large language models. The platform focuses on safety, prompt engineering, hallucinations, and evaluation of AI models.

OpenRead is an AI-powered research tool that helps users discover, understand, and organize scientific literature. It offers a variety of features to make research more efficient and effective, including semantic search, AI summarization, and note-taking tools. OpenRead is designed to help researchers of all levels, from students to experienced professionals, save time and improve their research outcomes.

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

cake is a pure Rust implementation of the llama3 LLM distributed inference based on Candle. The project aims to enable running large models on consumer hardware clusters of iOS, macOS, Linux, and Windows devices by sharding transformer blocks. It allows running inferences on models that wouldn't fit in a single device's GPU memory by batching contiguous transformer blocks on the same worker to minimize latency. The tool provides a way to optimize memory and disk space by splitting the model into smaller bundles for workers, ensuring they only have the necessary data. cake supports various OS, architectures, and accelerations, with different statuses for each configuration.

Awesome-Robotics-3D is a curated list of 3D Vision papers related to Robotics domain, focusing on large models like LLMs/VLMs. It includes papers on Policy Learning, Pretraining, VLM and LLM, Representations, and Simulations, Datasets, and Benchmarks. The repository is maintained by Zubair Irshad and welcomes contributions and suggestions for adding papers. It serves as a valuable resource for researchers and practitioners in the field of Robotics and Computer Vision.

TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

Rhesis is a comprehensive test management platform designed for Gen AI teams, offering tools to create, manage, and execute test cases for generative AI applications. It ensures the robustness, reliability, and compliance of AI systems through features like test set management, automated test generation, edge case discovery, compliance validation, integration capabilities, and performance tracking. The platform is open source, emphasizing community-driven development, transparency, extensible architecture, and democratizing AI safety. It includes components such as backend services, frontend applications, SDK for developers, worker services, chatbot applications, and Polyphemus for uncensored LLM service. Rhesis enables users to address challenges unique to testing generative AI applications, such as non-deterministic outputs, hallucinations, edge cases, ethical concerns, and compliance requirements.

Verifiers is a library of modular components for creating RL environments and training LLM agents. It includes an async GRPO implementation built around the `transformers` Trainer, is supported by `prime-rl` for large-scale FSDP training, and can easily be integrated into any RL framework which exposes an OpenAI-compatible inference client. The library provides tools for creating and evaluating RL environments, training LLM agents, and leveraging OpenAI-compatible models for various tasks. Verifiers aims to be a reliable toolkit for building on top of, minimizing fork proliferation in the RL infrastructure ecosystem.

uzu is a high-performance inference engine for AI models on Apple Silicon. It features a simple, high-level API, hybrid architecture for GPU kernel computation, unified model configurations, traceable computations, and utilizes unified memory on Apple devices. The tool provides a CLI mode for running models, supports its own model format, and offers prebuilt Swift and TypeScript frameworks for bindings. Users can quickly start by adding the uzu dependency to their Cargo.toml and creating an inference Session with a specific model and configuration. Performance benchmarks show metrics for various models on Apple M2, highlighting the tokens/s speed for each model compared to llama.cpp with bf16/f16 precision.

Atropos is a robust and scalable framework for Reinforcement Learning Environments with Large Language Models (LLMs). It provides a flexible platform to accelerate LLM-based RL research across diverse interactive settings. Atropos supports multi-turn and asynchronous RL interactions, integrates with various inference APIs, offers a standardized training interface for experimenting with different RL algorithms, and allows for easy scalability by launching more environment instances. The framework manages diverse environment types concurrently for heterogeneous, multi-modal training.

siiRL is a novel, fully distributed reinforcement learning (RL) framework designed to break the scaling barriers in Large Language Models (LLMs) post-training. Developed by researchers from Shanghai Innovation Institute, siiRL delivers near-linear scalability, dramatic throughput gains, and unprecedented flexibility for RL-based LLM development. It eliminates the centralized controller common in other frameworks, enabling scalability to thousands of GPUs, achieving state-of-the-art throughput, and supporting cross-hardware compatibility. siiRL is extensively benchmarked and excels in data-intensive workloads such as long-context and multi-modal training.

aira-dojo is a scalable and customizable framework for AI research agents, designed to accelerate hill-climbing on research capabilities toward a fully automated AI research scientist. The framework provides a general abstraction for tasks and agents, implements the MLE-bench task, and includes state-of-the-art agents. It features an isolated code execution environment that integrates smoothly with job schedulers like Slurm, enabling large-scale experiments and rapid iteration across a portfolio of tasks and solvers.

Tunix is a JAX-based library designed for post-training Large Language Models. It provides efficient support for supervised fine-tuning, reinforcement learning, and knowledge distillation. Tunix leverages JAX for accelerated computation and integrates seamlessly with the Flax NNX modeling framework. The library is modular, efficient, and designed for distributed training on accelerators like TPUs. Currently in early development, Tunix aims to expand its capabilities, usability, and performance.

Your AI research assistant, for turning a problem into a research, developing research questions, generating plans, analyzing data and improving research workflows for project success

Your AI research aide for the latest in ML.

Guides scientific investigations and theoretical research methodologies.

Stay up to date with Arxiv Research Papers!

NeurIPS 2023 Gateway

Research Assistant on Creatine

Research assistant for Atomic, Molecular, and Optics Physics

Deep learning researcher explaining Google Gemini from PDFs.

辅助科研人员撰写、修改、评估和优化课题标书，提高课题申报的成功率和质量。

I'll analyze academic and scientific articles and create state-of-the-art documents

Object Messaging and Intelligent Object Model for Deep AI integration with software

Leading AI in graphene research, offering innovative insights and solutions, powered by OpenAI.

Synthèse d'articles d'ophtalmologie de PubMed depuis 2020 (par défaut), ou avant sur demande

Designed to Provide Comprehensive Insights from the AI industry from Reputable Sources.

AI tool for self-evaluating and enhancing scientific research capabilities.

Strategizing balanced outreach for research campaigns.

recommends AI research these days in Japanese using AI-driven's-lab articles

可以从数据层面了解2024年前AI领域信息

Your go-to AI research assistant, ready to tackle the future! Type "list all" to see all features.

人工知能に関する論文PDFをアップロードしてください。ユーザーが指定したキャラがAI論文を解説します。